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Journal articles on the topic 'Classical adaptive controllers'
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1
Costa, Victor, and Wesley Beccaro. "Benefits of Intelligent Fuzzy Controllers in Comparison to Classical Methods for Adaptive Optics." Photonics 10, no. 9 (August 30, 2023): 988. http://dx.doi.org/10.3390/photonics10090988.
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Adaptive Optics (AO) systems have been developed throughout recent decades as a strategy to compensate for the effects of atmospheric turbulence, primarily caused by poor astronomical seeing. These systems reduce the wavefront distortions using deformable mirrors. Several AO simulation tools have been developed, such as the Object-Oriented, MATLAB, and Adaptive Optics Toolbox (OOMAO), to assist in the project of AO. However, the main AO simulators focus on AO models, not prioritizing the different control techniques. Moreover, the commonly applied control strategies in ground-based telescopes are based on Integral (I) or Proportional-Integral (PI) controllers. This work proposes the integration of OOMAO models to Simulink to support the development of advanced controllers and compares traditional controllers with intelligent systems based on fuzzy logic. The controllers were compared in three scenarios of different turbulence and atmosphere conditions. The simulations were performed using the characteristics/parameters of the Southern Astrophysical Research (SOAR) telescope and assessed with the Full Width at Half Maximum (FWHM), Half Light Radius (HLR), and Strehl ratio metrics to compare the performance of the controllers. The results demonstrate that adaptive optics can be satisfactorily simulated in OOMAO adapted to Simulink and thus further increase the number of control strategies available to OOMAO. The comparative results between the MATLAB script and the Simulink blocks designed showed a maximum relative error of 3% in the Strehl ratio and 1.59% in the FWHM measurement. In the assessment of the control algorithms, the fuzzy PI controller reported a 25% increase in the FWHM metrics in the critical scenario when compared with open-loop metrics. Furthermore, the fuzzy PI controller outperformed the results when compared with the I and PI controllers. The findings underscore the constraints of conventional control methods, whereas the implementation of fuzzy-based controllers showcases the promise of intelligent approaches in enhancing control performance under challenging atmospheric conditions.
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Intidam, Abdessamad, Hassan El Fadil, Halima Housny, Zakariae El Idrissi, Abdellah Lassioui, Soukaina Nady, and Abdeslam Jabal Laafou. "Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles." Energies 16, no. 11 (May 29, 2023): 4395. http://dx.doi.org/10.3390/en16114395.
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This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling time.
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Kamal, Tariq, Murat Karabacak, Vedran S. Perić, Syed Zulqadar Hassan, and Luis M. Fernández-Ramírez. "Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid." Energies 13, no. 18 (September 10, 2020): 4721. http://dx.doi.org/10.3390/en13184721.
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In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response.
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Zhang, Chao, Sheng Xiu Zhang, and Yi Nan Liu. "Invariant Manifolds Based Modular Adaptive Control for a Class of Nonlinear Systems with Application to Flight Control." Applied Mechanics and Materials 373-375 (August 2013): 1488–92. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1488.
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In this paper a novel modular framework for adaptive control for a class of nonlinear system is developed and applied to flight controller design. The framework is based on the invariant manifolds approach with a new type of reduced-order estimator which allows for stable dynamics to be assigned to the estimation error. We show that this method can be applied to systems with unknown parameters, leading to a new class of modular adaptive controllers which is easier to tune compared to controllers obtained using the classical adaptive approaches and does not suffer from unpredictable dynamical behavior of the parameter update laws.
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Humaidi, Amjad J., Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Musaab E. Sadiq. "A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles." Entropy 22, no. 7 (June 30, 2020): 723. http://dx.doi.org/10.3390/e22070723.
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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.
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Noordin, Aminurrashid, Mohd Ariffanan Mohd Basri, and Zaharuddin Mohamed. "Real-Time Implementation of an Adaptive PID Controller for the Quadrotor MAV Embedded Flight Control System." Aerospace 10, no. 1 (January 6, 2023): 59. http://dx.doi.org/10.3390/aerospace10010059.
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This paper presents the real-time implementation of an altitude-embedded flight controller using proportional, integral, and derivative (PID) control, adaptive PID (APID) control, and adaptive PID control with a fuzzy compensator (APIDFC) for a micro air vehicle (MAV), specifically, for a Parrot Mambo Minidrone. In order to obtain robustness against disturbance, the adaptive mechanism, which was centered on the second-order sliding mode control, was applied to tune the classical parameters of the PID controller of the altitude controller. Additionally, a fuzzy compensator was introduced to diminish the existence of the chattering phenomena triggered by the application of the sliding mode control. Four simulation and experimental scenarios were conducted, which included hovering, as well as sine, square, and trapezium tracking. Moreover, the controller’s resilience was tested at 1.1 m above the ground by adding a mass of about 12.5 g, 15 s after the flight launch. The results demonstrated that all controllers were able to follow the reference altitude, with some spike or overshoot. Although there were slight overshoots in the control effort, the fuzzy compensator reduced the chattering phenomenon by about 6%. Moreover, it was found that in the experiment, the APID and APIDFC controllers consumed 2% and 4% less power, respectively, when compared to the PID controller used to hover the MAV.
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Braz-César, Manuel, and Rui Barros. "Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure." International Journal of Structural Stability and Dynamics 17, no. 05 (December 8, 2016): 1740007. http://dx.doi.org/10.1142/s0219455417400077.
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Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller.
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Zhu, Gao Ke, Xiao Gang Duan, and Hua Deng. "Adaptive Fuzzy PID Force Control for a Prosthetic Hand." Applied Mechanics and Materials 433-435 (October 2013): 93–101. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.93.
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An adaptive fuzzy proportional-integral-derivative (PID) force control strategy for a prosthetic hand is presented. The classical PID controller is also applied on the prosthetic hand as comparison. The parameters of PID controller are firstly tuned by Cut and Try method. Then a fuzzy logic system is used to adjust those parameters on line. Real-time force control experiments are realized on LabVIEW and PXI (PCI eXtensions for Instrumentation) real-time (RT) platforms. A rigid object and a compliant object are grasped by the prosthesis respectively to test the performance of controllers. Experimental results indicate that the adaptive fuzzy PID force controller is more effective than PID controller.
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Mahdi, Shaymaa M., Noor Q. Yousif, Ahmed A. Oglah, Musaab E. Sadiq, Amjad J. Humaidi, and Ahmad Taher Azar. "Adaptive Synergetic Motion Control for Wearable Knee-Assistive System: A Rehabilitation of Disabled Patients." Actuators 11, no. 7 (June 22, 2022): 176. http://dx.doi.org/10.3390/act11070176.
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In this study, synergetic-based adaptive control design is developed for trajectory tracking control of joint position in knee-rehabilitation system. This system is often utilized for rehabilitation of patients with lower-limb disabilities. However, this knee-assistive system is subject to uncertainties when applied to different persons undertaking exercises. This is due to the different masses and inertias of different persons. In order to cope with these uncertainties, an adaptive scheme has been proposed. In this study, an adaptive synergetic control scheme is established, and control laws are developed to ensure stable knee exoskeleton system subjected to uncertainties in parameters. Based on Lyapunov stability analysis, the developed adaptive synergetic laws are used to estimate the potential uncertainties in the coefficients of the knee-assistive system. These developed control laws guarantee the stability of the knee rehabilitation system controlled by the adaptive synergetic controller. In this study, particle swarm optimization (PSO) algorithm is introduced to tune the design parameters of adaptive and non-adaptive synergetic controllers, in order to optimize their tracking performances by minimizing an error-cost function. Numerical simulations are conducted to show the effectiveness of the proposed synergetic controllers for tracking control of the exoskeleton knee system. The results show that compared to classical synergetic controllers, the adaptive synergetic controller can guarantee the boundedness of the estimated parameters and hence avoid drifting, which in turn ensures the stability of the controlled system in the presence of parameter uncertainties.
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Uçak, Kemal, and Beyza Nur Arslantürk. "Adaptive MIMO fuzzy PID controller based on peak observer." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 13, no. 2 (July 9, 2023): 139–50. http://dx.doi.org/10.11121/ijocta.2023.1247.
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In this paper, a novel peak observer based adaptive multi-input multi-output (MIMO) fuzzy proportional-integral-derivative (PID) controller has been introduced for MIMO time delay systems. The adaptation mechanism proposed by Qiao and Mizumoto [1] for single-input single-output (SISO) systems has been enhanced for MIMO system adaptive control. The tracking, stabilization and disturbance rejection performances of the proposed adaptation mechanism have been evaluated for MIMO systems by comparing with non-adaptive fuzzy PID and classical PID controllers. The obtained results indicate that the introduced adjustment mechanism for MIMO fuzzy PID controller can be successfully deployed for MIMO time delay systems.
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Portillo, Patricia, Luis E. Garza-Castañón, Luis I. Minchala-Avila, Adriana Vargas-Martínez, Vicenç Puig Cayuela, and Pierre Payeur. "Robust Nonlinear Trajectory Controllers for a Single-Rotor UAV with Particle Swarm Optimization Tuning." Machines 11, no. 9 (August 29, 2023): 870. http://dx.doi.org/10.3390/machines11090870.
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This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.
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Lou, Ya’nan, Haoyu Lin, Pengkun Quan, Dongbo Wei, and Shichun Di. "Robust Adaptive Control of Fully Constrained Cable-Driven Serial Manipulator with Multi-Segment Cables Using Cable Tension Sensor Measurements." Sensors 21, no. 5 (February 25, 2021): 1623. http://dx.doi.org/10.3390/s21051623.
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The structure of the cable-driven serial manipulator (CDSM) is more complex than that of the cable-driven parallel manipulator (CDPM), resulting in higher model complexity and stronger structural and parametric uncertainties. These drawbacks challenge the stable trajectory-tracking control of a CDSM. To circumvent these drawbacks, this paper proposes a robust adaptive controller for an n-degree-of-freedom (DOF) CDSM actuated by m cables. First, two high-level controllers are designed to track the joint trajectory under two scenarios, namely known and unknown upper bounds of uncertainties. The controllers include an adaptive feedforward term based on inverse dynamics and a robust control term compensating for the uncertainties. Second, the independence of control gains from the upper bound of uncertainties and the inclusion of the joint viscous friction coefficient into the dynamic parameter vector are realised. Then, a low-level controller is designed for the task of tracking the cable tension trajectory. The system stability is analysed using the Lyapunov method. Finally, the validity and effectiveness of the proposed controllers are verified by experimenting with a three-DOF six-cable CDSM. In addition, a comparative experiment with the classical proportional–integral–derivative (PID) controller is carried out.
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Yue, Wei Qiang, Li Qiang Jin, and Chuan Xue Song. "An Auto-Adaptive GA-PID Control Method Based on CMAC Net." Advanced Materials Research 219-220 (March 2011): 1139–44. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1139.
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This paper aimed at solving the difficulty of nonlinear process control by classical PID controller. The author structured a GA-PID controller taking advantage of the multipoint optimizing and fast compute speed of GA, which can get the optimal PID parameters by on-line turning. At the same time, the author introduced a CMAC feed-forward controller which make full use of the high precision to approach nonlinearly object of CMAC. Combine them, a concurrent pattern control method appear, which synthesize advantages of two controllers and is more fit for nonlinear process. The simulation result indicated that the method has high accuracy and good robustness.
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Kadjoudj, Mohamed, Noureddine Golea, and Hachemi Benbouzid. "Fuzzy rule: Based model reference adaptive control for PMSM drives." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 13–22. http://dx.doi.org/10.2298/sjee0701013k.
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The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning features and good tracking characteristics even under severe variations of system parameters. The learning mechanism observes the plant outputs and adjusts the rules in a direct fuzzy controller, so that the overall system behaves like a reference model, which characterizes the desired behavior. In the proposed scheme, the error and error change measured between the motor speed and output of the reference model are applied to the MRAFC. The latter will force the system to behave like the signal reference by modifying the knowledge base of the FLC or by adding an adaptation signal to the fuzzy controller output. In this paper, the MRAFC is applied to a permanent magnet synchronous motor drive (PMSM). High performances and robustness have been achieved by using the MRAFC. This will be illustrated by simulation results and comparisons with other controllers such as PI classical and adaptive fuzzy controller based on gradient method controllers.
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Maalej, Boutheina, Rim Jallouli Khlif, Chokri Mhiri, Mohamed Habib Elleuch, and Nabil Derbel. "L 1 Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems." Mathematical Problems in Engineering 2021 (April 9, 2021): 1–14. http://dx.doi.org/10.1155/2021/5579541.
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Recently, an adaptive control approach has been proposed. This approach, named L 1 adaptive control, involves the insertion of a low-pass filter at the input of the Model Reference Adaptive Control (MRAC). This controller has been designed to overcome several limitations of classical adaptive controllers such as (i) the initialization of estimated parameters, (ii) the stability problems with high adaptation gains, and (iii) the appropriate parameter excitation. In this paper, a new design of the filter is presented, used for L 1 adaptive control, for which the desired performances are guaranteed (appropriate values of the control during start-up, a high filtering of noises, a reduced time lag, and a reduced energy consumption). Parameters of the new proposed filter have been optimised by genetic algorithms. The proposed L 1 adaptive fractional control is applied to a polyarticulated robotic system. Simulation results show the efficiency of the proposed control approach with respect to the classical L 1 adaptive control in the nominal case and in the presence of a multiplicative noise.
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Abut, Tayfun, and Servet Soyguder. "Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum." Industrial Robot: the international journal of robotics research and application 46, no. 1 (January 21, 2019): 159–70. http://dx.doi.org/10.1108/ir-10-2018-0206.
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PurposeThis paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers.Design/methodology/approachAs inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems.FindingsAs a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed.Originality/valueIn this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.
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Guan, Wei, Haowen Peng, Xianku Zhang, and Hui Sun. "Ship Steering Adaptive CGS Control Based on EKF Identification Method." Journal of Marine Science and Engineering 10, no. 2 (February 20, 2022): 294. http://dx.doi.org/10.3390/jmse10020294.
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In recent years, marine autonomous surface vessels (MASS) have grown into a ship research issue to increase the level of autonomy of ship behavior decision-making and control while sailing at sea. This paper focuses on the MASS motion control module design that aims to improve the accuracy and reliability of ship steering control systems. Nevertheless, the stochastic sea and wind environment have led to the extensive use of filters and state observers for estimating the ship-motion-related parameters, which are important for ship steering control systems. In particular, the ship maneuverability Nomoto index, which primarily determines the designed ship steering controller’s performance, cannot be observed directly due to the model errors and the external environment disturbance in the process of sailing. Hence, an adaptive robust ship steering controller based on a closed-loop gain shaping (CGS) scheme and an extended Kalman filter (EKF) on-line identification method is explored in this paper. To verify the effectiveness of the proposed steering controller design scheme, the motor vessel YUKUN was taken as the control plant and a series of simulation experiments were carried out. The results show the advantages of the dynamic response performance of the proposed steering controller compared with the classical PD and traditional CGS controllers. Therefore, the proposed adaptive CGS steering controller would be a good solution for MASS motion control module design.
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Timene, Aristide, Ndjiya Ngasop, and Haman Djalo. "Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." Proceedings of Engineering and Technology Innovation 19 (May 25, 2021): 53–61. http://dx.doi.org/10.46604/peti.2021.7522.
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This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations. Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement. The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, and a neuro-fuzzy controller, are conducted in SolidWorks and MATLAB software. The performance of the proposed controller is analyzed and is contrasted with a Proportional Integral Derivative (PID) controller. The obtained results show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.
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Al-Dujaili, Ayad Q., Amjad J. Humaidi, Ziyad T. Allawi, and Musaab E. Sadiq. "Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control." Applied System Innovation 6, no. 2 (February 28, 2023): 34. http://dx.doi.org/10.3390/asi6020034.
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This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulated on the basis of earthquake acceleration data recorded from the El Centro Imperial Valley Earthquake. The effectiveness of the adaptive synergetic control was verified and assessed via numerical simulation, and a comparison study was conducted between the adaptive and classical versions of synergetic control (SC). The vibration suppression index was used to evaluate both controllers. The numerical simulation showed the capability of the proposed adaptive controller to stabilize and to suppress the vibration of a building subjected to earthquake. In addition, the adaptive controller successfully kept the estimated viscosity and stiffness coefficients bounded.
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Suarez-Rivera, Guiovanny, Nelson David Muñoz-Ceballos, and Henry Mauricio Vásquez-Carvajal. "Development of an Adaptive Trajectory Tracking Control of Wheeled Mobile Robot." Revista Facultad de Ingeniería 30, no. 55 (February 13, 2021): e12022. http://dx.doi.org/10.19053/01211129.v30.n55.2021.12022.
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Classical modeling and control methods applied to differential locomotion mobile robots generate mathematical equations that approximate the dynamics of the system and work relatively well when the system is linear in a specific range. However, they may have low accuracy when there are many variations of the dynamics over time or disturbances occur. To solve this problem, we used a recursive least squares (RLS) method that uses a discrete-time structure first-order autoregressive model with exogenous variable (ARX). We design and modify PID adaptive self-adjusting controllers in phase margin and pole allocation. The main contribution of this methodology is that it allows the permanent and online update of the robot model and the parameters of the adaptive self-adjusting PID controllers. In addition, a Lyapunov stability analysis technique was implemented for path and trajectory tracking control, this makes the errors generated in the positioning and orientation of the robot when performing a given task tend asymptotically to zero. The performance of the PID adaptive self-adjusting controllers is measured through the implementation of the criteria of the integral of the error, which allows to determine the controller of best performance, being in this case, the PID adaptive self-adjusting type in pole assignment, allowing the mobile robot greater precision in tracking the trajectories and paths assigned, as well as less mechanical and energy wear, due to its smooth and precise movements.
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Dmitrii, Shprekher, Babokin Gennadii, Kolesnikov Evgenii, and Zelenkov Aleksandr. "Rating the speed of the shearer’s electric motor drive load automatic control." Izvestiya vysshikh uchebnykh zavedenii Gornyi zhurnal, no. 6 (September 24, 2020): 109–20. http://dx.doi.org/10.21440/0536-1028-2020-6-109-120.
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Introduction. It is possible to improve productivity, effectiveness, and cost-efficiency of coal extraction due to the efficient use of physical resources, technical upgrade of mechanized longwall equipment, and introduction of advanced technologies and control methods. The existing method of shearer electric motor drive automation based on the automated load controller of Uran type has a significant drawback of low speed. In case the actuator (A) meets solid rock and the shearer’s (S) speed is not changed, it may result in heavy shock loads on A and its transmission, therefore, increased wear of the cutter or machine’s breakage, leading to production loss due to the reduced speed of travel along the face. The foregoing demands higher standards of the load controller’s speed, making the task of improving the control system’s development a relevant scientific task. Research aim is to synthesize the neural tuner for the coefficients of the proportional-integral controller (PI controller) in the control system of a shearer with increased speed as compared to the existing standard controllers. The research also aims to estimate its efficiency by the method of mathematical simulation. Methodology. Mathematical model has been developed which has made it possible to compare the performance of standard controllers with an adaptive PI controller. The structure and parameters of the neural network underlying the controller have been substantiated. The proposed controller was compared to the standard PI controller and to the MPC controller (microprocessor-based speed controller) by the method of simulation experiment. Research results. The adaptive PI controller has been synthesized based on the neural network which allows changing the coefficients of the PI controller as soon as coal strength changes. Summary. The simulation experiment has shown that the PI controller with the neural network tuner for its coefficients in the control system will make it possible to increase the load controller’s speed by 1.5 to 3 times on average as compared to the classical controller. Therefore, it is going to be possible to avoid critical overload and breakage of mechanical parts in the shearer’s transmission in case of the sudden contact of its actuator with solid inclusion.
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Tárník, Marián, and Ján Murgaš. "Model Reference Adaptive Control of Permanent Magnet Synchronous Motor." Journal of Electrical Engineering 62, no. 3 (May 1, 2011): 117–25. http://dx.doi.org/10.2478/v10187-011-0020-4.
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Model Reference Adaptive Control of Permanent Magnet Synchronous MotorIn this paper the classical theory of the direct Model Reference Adaptive Control is used to develop a control algorithm for Permanent Magnet Synchronous Motor (PMSM). A PMSM model widely used in electric drives community is considered as base for control system development. Conventionally used controllers are replaced by adaptive ones. The resulting control system adapts to changes in any of PMSM parameters.
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Younesi, Abdollah, Hossein Shayeghi, and Pierluigi Siano. "Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids." Energies 13, no. 5 (March 8, 2020): 1250. http://dx.doi.org/10.3390/en13051250.
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The main purpose of this paper is to present a novel algorithmic reinforcement learning (RL) method for damping the voltage and frequency oscillations in a micro-grid (MG) with penetration of wind turbine generators (WTG). First, the continuous-time environment of the system is discretized to a definite number of states to form the Markov decision process (MDP). To solve the modeled discrete RL-based problem, Q-learning method, which is a model-free and simple iterative solution mechanism is used. Therefore, the presented control strategy is adaptive and it is suitable for the realistic power systems with high nonlinearities. The proposed adaptive RL controller has a supervisory nature that can improve the performance of any kind of controllers by adding an offset signal to the output control signal of them. Here, a part of Denmark distribution system is considered and the dynamic performance of the suggested control mechanism is evaluated and compared with fuzzy-proportional integral derivative (PID) and classical PID controllers. Simulations are carried out in two realistic and challenging scenarios considering system parameters changing. Results indicate that the proposed control strategy has an excellent dynamic response compared to fuzzy-PID and traditional PID controllers for damping the voltage and frequency oscillations.
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Ladaci, Samir, Abdelfatah Charef, and Jean Loiseau. "Robust Fractional Adaptive Control Based on the Strictly Positive Realness Condition." International Journal of Applied Mathematics and Computer Science 19, no. 1 (March 1, 2009): 69–76. http://dx.doi.org/10.2478/v10006-009-0006-6.
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Robust Fractional Adaptive Control Based on the Strictly Positive Realness ConditionThis paper presents a new approach to robust adaptive control, using fractional order systems as parallel feedforward in the adaptation loop. The problem is that adaptive control systems may diverge when confronted with finite sensor and actuator dynamics, or with parasitic disturbances. One of the classical robust adaptive control solutions to these problems makes use of parallel feedforward and simplified adaptive controllers based on the concept of positive realness. The proposed control scheme is based on the Almost Strictly Positive Realness (ASPR) property of the plant. We show that this condition implies also robust stability in the case of fractional order controllers. An application to Model Reference Adaptive Control (MRAC) with a fractional order adaptation rule is provided with an implementable algorithm. A simulation example of a SISO robust adaptive control system illustrates the advantages of the proposed method in the presence of disturbances and noise.
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Timis, Daniel D., Cristina I. Muresan, and Eva-H. Dulf. "Design and Experimental Results of an Adaptive Fractional-Order Controller for a Quadrotor." Fractal and Fractional 6, no. 4 (April 6, 2022): 204. http://dx.doi.org/10.3390/fractalfract6040204.
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The use of multi-copter systems started to grow over the last years in various applications. The designed solutions require high stability and maneuverability. To fulfill these specifications, a robust control strategy must be designed and integrated. Focusing on this challenge, this research proposes an adaptive control design applied to a physical model of a quadrotor prototype. The proposed adaptive structure guarantees robustness, control flexibility, and stability to the whole process. The prototype components, structure, and laboratory testing equipment that are used to run the experiments are presented in this paper. The study is focused on the performance comparison of a classical PID controller and a fractional-order controller, which are both integrated into the adaptive scheme. Fractional-order controllers are preferred due to their recognized ability to increase the robustness of the overall closed-loop system. Furthermore, this work covers the design and the tuning method of this control approach. The research concludes with the actual results obtained for this comparative study that highlights the advantages of the fractional-order controller.
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Modirrousta, Alireza, and Mahdi Khodabandeh. "Adaptive non-singular terminal sliding mode controller: new design for full control of the quadrotor with external disturbances." Transactions of the Institute of Measurement and Control 39, no. 3 (July 22, 2016): 371–83. http://dx.doi.org/10.1177/0142331215611210.
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This paper proposes two different adaptive robust sliding mode controllers for attitude, altitude and position control of a quadrotor. Firstly, it proposes a backstepping non-singular terminal sliding mode control with an adaptive algorithm that is applied to the quadrotor for free chattering, finite time convergence and robust aims. In this control scheme instead of regular control input, the derivative of the control input is achieved from a non-singular terminal second-layer sliding surface. An adaptive tuning method is utilized to deal with the external disturbances whose upper bounds are not required to be known in advance in the inner loop. Secondly, a nonlinear disturbance observer based on the integral sliding mode with adaptive gains is proposed for position control, which is known as the outer loop. Stability and robustness of the proposed controller are proved by using the classical Lyapunov criterion. The simulation results demonstrate the validation of the proposed control scheme.
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Eissa, M. Abdullah. "Novel Fuzzy-Based Self-Adaptive Single Neuron PID Load Frequency Controller for Power System." Power Electronics and Drives 4, no. 1 (June 1, 2019): 141–50. http://dx.doi.org/10.2478/pead-2019-0002.
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AbstractThis paper proposes a newly adaptive single-neuron proportional integral derivative (SNPID) controller that uses fuzzy logic as an adaptive system. The main problem of the classical controller is lacking the required robustness against disturbers, measurement noise in industrial applications. The new formula of the proposed controller helps in fixing this problem based on the fuzzy logic technique. In addition, the genetic algorithm (GA) is used to optimize parameters of the SNPID controller. Because of the high demands on the availability and efficiency of electrical power production, the design of robust load-frequency controller is becoming increasingly important due to its potential in increasing the reliability, maintainability and safety of power systems. So, the proposed controller has been applied for load-frequency control (LFC) of a single-area power system. The effectiveness of the proposed SNPID controller has been compared with the conventional controllers. The simulation results show that the proposed controller approach provides better damping of oscillations with a smaller settling time. This confirms its superiority against its counterparts. In addition, the results show the robustness of the proposed controller against the parametric variation of the system.
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Aljuboury, Anwer S., Akram Hashim Hameed, Ahmed R. Ajel, Amjad J. Humaidi, Ahmed Alkhayyat, and Ammar K. Al Mhdawi. "Robust Adaptive Control of Knee Exoskeleton-Assistant System Based on Nonlinear Disturbance Observer." Actuators 11, no. 3 (March 4, 2022): 78. http://dx.doi.org/10.3390/act11030078.
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This study presents a control design of an angular position for the exoskeleton knee assistance system based on a model reference adaptive control (MRAC) strategy. Three schemes of the MRAC design have been proposed: the classical MRAC, MRAC with an adaptive disturbance observer, and MRAC with a nonlinear observer. The stability analysis for each scheme has been conducted and developed based on the Lyapunov theorem to prove the uniform ultimate bound of tracking and estimation errors. In addition, the adaptive laws have been developed for the proposed schemes according to the stability analysis. The effectiveness of the proposed state and output feedback controllers has been verified via computer simulation. The results based on numerical simulation have shown that the MRAC with a nonlinear observer could give better robustness characteristics and better performance in terms of tracking and estimation errors as compared to the other controllers.
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Yue, Hongyun, Junmin Li, Jiarong Shi, and Wei Yang. "Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Time-Varying Input Delays Using the Quadratic Functions." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 01 (January 31, 2018): 109–42. http://dx.doi.org/10.1142/s0218488518500071.
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In this paper, for the stochastic nonlinear systems the adaptive fuzzy tracking controllers are constructed by using the fuzzy logic systems (FLS) and the classical quadratic functions. Compared with the existing results for adaptive fuzzy control, the stochastic nonlinear systems investigated in this paper are much more complex since the systems not only have distributed state time-varying delays in the noise jamming intensity terms but also have the time-varying delays in the input signals. During the controller design procedure, through appropriate assumptions and a state transformation the system with time-varying input delay can be easily transformed into a system without input delay. The other main advantage is that quadratic functions are used as Lyapunov functions to analyze the stability of systems, other than the fourth moment approach proposed by H. Deng and M. Krstic, and the hyperbolic tangent functions are introduced to deal with the Hessian terms. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error can converge to a small residual set around the origin in the mean square sense.
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Sheh Zad, Haris, Abasin Ulasyar, Adil Zohaib, and Abraiz Khattak. "Adaptive sliding mode predictive power control of three-phase AC/DC converters." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 236, no. 5 (February 24, 2022): 897–912. http://dx.doi.org/10.1177/09596518221079469.
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This article presents a new adaptive sliding mode–based model predictive controller for AC/DC three-phase converters achieving better dynamic performance and stability. In the classical model predictive controller available in the literature, the model-based approach, for example, proportional–integral controller is employed for producing the active power reference for the three-phase converters. The traditional proportional–integral–based model predictive controllers consist of steady-state error and slow transient response characteristics. As a result, the DC-link voltage contains uncertainties due to variations in the load demand and output voltage. To overcome these limitations, this article suggests an adaptive sliding mode controller for generating the active power reference value from the DC-link voltage which then combines with the model predictive controller in order to minimize the cost function. The proposed controller minimizes the effects of uncertainties and variations in the output voltage by adaptively regulating the gain of sliding mode controller and modifying the control law online. The cost function is then minimized using the model predictive controller in order to control the active and reactive power flow. The stability analysis of the designed controller is performed using Lyapunov theorem. The effectiveness of the designed control scheme is proved by comparing its performance with the proportional–integral model predictive controller and fixed gain sliding mode–based model predictive controller control schemes. Simulation and experimental system results are obtained for validating the presented control approach.
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Sands, Timothy. "Comparison and Interpretation Methods for Predictive Control of Mechanics." Algorithms 12, no. 11 (November 4, 2019): 232. http://dx.doi.org/10.3390/a12110232.
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Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.
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Ray, S., G. K. Venayagamoorthy, B. Chaudhuri, and R. Majumder. "Comparison of Adaptive Critic-Based and Classical Wide-Area Controllers for Power Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 4 (August 2008): 1002–7. http://dx.doi.org/10.1109/tsmcb.2008.924141.
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Yang, Mu, Xiaojie Wu, and Maxwell Chiemeka Loveth. "A Deep Reinforcement Learning Design for Virtual Synchronous Generators Accommodating Modular Multilevel Converters." Applied Sciences 13, no. 10 (May 10, 2023): 5879. http://dx.doi.org/10.3390/app13105879.
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The deep reinforcement learning (DRL) technique has gained attention for its potential in designing “virtual network” controllers. This skill utilizes a novel solution that can avoid the specific parameters and system model required in classical dynamic programming algorithms. However, addressing the issue of system uncertainties and performance deterioration remains a challenge. To overcome this challenge, the authors propose a new control prototype using a twin delayed deep deterministic policy gradient (TD3)-based adaptive controller, which replaces the conventional virtual synchronous generator (VSG) module in the modular multilevel converter (MMC) control. In this approach, an adaptive programming module is developed using a critic fuzzy network point of view to determine the optimal control policy. The modification presented in this framework is able to improve the system stability and resist disruptions while retaining the merits of the conventional VSG control model. The proposed approach is implemented and tested using the DRL toolbox in MATLAB/Simulink.
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Stanislawski, Radoslaw, Jules-Raymond Tapamo, and Marcin Kaminski. "Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System." Energies 16, no. 15 (July 26, 2023): 5629. http://dx.doi.org/10.3390/en16155629.
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Neural network approaches have commonly been used to solve complex mathematical equations in the literature. They have inspired the modifications of state controllers and are often implemented for electrical drives with an elastic connection. Given that the addition of a virtual signal can provide adaptive properties to classical controllers and that selected feedback signals can also be replaced with a virtual state variable from a neural network, several combinations can be considered and compared. In this paper, Radial Basis Function neural-network-based control algorithms are proposed in which online updating of the output weights is used. Analyses of simulation experiment results reveal that the proposed control algorithms significantly improve the operation of classic-state feedback controllers applied to two-mass systems in the presence of parameter uncertainty.
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Mehedi, Ibrahim M., Heidir S. M. Shah, Ubaid M. Al-Saggaf, Rachid Mansouri, and Maamar Bettayeb. "Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator." Journal of Healthcare Engineering 2021 (June 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/1926711.
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This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient’s lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios.
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Adetifa, A. O., P. P. Okonkwo, B. B. Muhammed, and D. A. Udekwe. "Deep reinforcement learning for aircraft longitudinal control augmentation system." Nigerian Journal of Technology 42, no. 1 (May 8, 2023): 144–51. http://dx.doi.org/10.4314/njt.v42i1.18.
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Control augmentation systems (CAS) are conventionally built with classical controllers which have the following drawbacks: dependence on domain specific knowledge for tuning and limited self-learning capability. Consequently, these drawbacks lead to sub-optimal aircraft stability and performance when exposed to time varying disturbances. Hence, to curb the stated problems; this paper proposes the development of a deep reinforcement learning (DRL) pitch-rate CAS (qCAS), aimed at guaranteeing adaptive stability, pitch-rate control tracking and disturbance rejection across the longitudinal dynamics of an aircraft. This stated aim was actualized by developing a CAS with a deep deterministic policy gradient (DDPG) agent. Subsequently, this proposed method was compared with two classical qCAS methods (a developed PID-aCAS and a benchmark PIqCAS obtained from literature). The results show that the developed DDPG-qCAS method outperformed the classical methods in peak overshoot, referemce command tracking and disturbance rejection as well as mean absolute error (MSE) and mean steady state error (MSSE). Hence, it can be inferred that it is important to apply artificially intelligent controllers to the flight control systems of aircraft in order to achieve superior time response, control command tracking accuracy and disturbance rejection.
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Hassan, Maha Raad, and Shibly Ahmed Al-Samarraie. "Robust Nonlinear Control Design for the HVAC System Based on Adaptive Sliding Mode Control." Journal Européen des Systèmes Automatisés 55, no. 5 (November 30, 2022): 593–601. http://dx.doi.org/10.18280/jesa.550504.
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Control of Heating, Ventilating, and Air Conditioning (HVAC) aims to provide a comfortable environment for human life in terms of temperature and humidity and improve indoor air quality. The HVAC system is multi-input multi-output, where the control design of this system is challenging due to its strong nonlinearity and the coupled influence of both system controllers on the temperature within the thermal zone. The aim of this study is to design a dual-controller for the HVAC system. The first controller is a non-linear feedback controller which is devoted to control the humidity ratio of the thermal zone with the desired characteristic. While for the second one, a robust controller is designed to maintain the desired thermal zone temperature based on the adaptive sliding mode controller (ASMC). Using the ASMC enabled us to design the second controller without the need to know the uncertainty bound on the HVAC system model. Additionally, the stability of the proposed control system was verified using the Lyapunov theory. To construct the sliding variable for the temperature control, the error state which is the difference between the thermal zoon temperature and the desired value and its derivative is needed. Due to the uncertainty in the error state derivative, a robust differentiator was designed using the approximate classical sliding mode differentiator (ACSMD). Finally, the performance of the control system is confirmed via numerical simulation. The results showed the robust ability of the control system to make the humidity and temperature of the thermal area follow the required values and with high accuracy.
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Naranjo, José Eugenio, Francisco Serradilla, and Fawzi Nashashibi. "Speed Control Optimization for Autonomous Vehicles with Metaheuristics." Electronics 9, no. 4 (March 26, 2020): 551. http://dx.doi.org/10.3390/electronics9040551.
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The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes.
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Zhang, Lingping, Feng Duan, and Bo Du. "Synchronization Problems of Fuzzy Competitive Neural Networks." Advances in Mathematical Physics 2022 (May 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/5926415.
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This paper is devoted to investigating the fixed-time and finite-time synchronization for fuzzy competitive neural networks with discontinuous activation functions. We introduce Filippov solution for overcoming the nonexistence of classical solutions of discontinuous system. Using the fixed-time synchronization theory, inequality technique, we obtain simple robust fixed-time synchronization conditions. Designing proper feedback controllers is a key step for the implementation of synchronization. Furthermore, based on the fixed-time robust synchronization, we design a switching adaptive controller and obtain the finite-time synchronization. It is noted that the settling time is independent on the initial value in the fixed-time robust synchronization. Hence, under the conditions of this paper, the considered system has better stability and feasibility. Finally, the theoretical results of this paper are attested to be indeed feasible in terms of a numerical example.
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Urrea, Claudio, and Yainet Garcia-Garcia. "Design and Performance Analysis of Level Control Strategies in a Nonlinear Spherical Tank." Processes 11, no. 3 (February 28, 2023): 720. http://dx.doi.org/10.3390/pr11030720.
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This work seeks to contribute to the study of techniques for level control considering a nonlinear plant model. To achieve this goal, different approaches are applied to classical control techniques and their results are analyzed. Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Model Predictive Control (MPC) and Nonlinear Auto-Regressive Moving Average (NARMA-L2) controllers are designed for the level control of a spherical tank. Subsequently, several tests and scenarios similar to those present in industrial processes are established, while the transient response of the controllers, their performance indices for monitoring the reference value, the rejection of disturbances, the presence of parameter uncertainties and the effects of noise are analyzed. The results show good reference tracking, with a settling time of approximately 5 s for 5 cm and a rise time of less than 4 s. No evidence for steady-state error or overshoot was found and controllers behave positively in the diverse scenarios assessed. The FLC and ANN controllers showed the greatest limitations, while ANFIS, MPC and NARMA-L2 exhibited competitive results considering their transient response and the performance indices calculated.
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Ridha, Taghreed Mohammad, and Mina Qays Kadhim. "A Barrier Function-Based Variable Structure Control for Maglev System." Journal Européen des Systèmes Automatisés 55, no. 5 (November 30, 2022): 633–39. http://dx.doi.org/10.18280/jesa.550508.
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Magnetic levitation (Maglev) systems are widely employed in the industry especially in mechatronics systems for precise positioning and suspension. They are inherently unstable having nonlinear models with uncertain parameters and exposed to external disturbances. Therefore, high-performance robust control designs are recommended for these systems. An Adaptive Variable Structure Controller based on barrier function (AVSCbf) is designed for the first time in this work to control the displacement of the ball position of a disturbed Maglev system. This approach does not require prior knowledge of the disturbance upper bounds in the design procedure. The state space region defined by the barrier function is designed to be attractive and invariant. This feature is essential to reject disturbances and handle parametric uncertainties. The adaptive law is activated when the state trajectory is initiated outside the invariant set defined by the barrier function. The gain of the VSC is adapted according to an adaptation law, which considers the system input constraints. The control input is constrained to be a bounded positive quantity. The adaptive VSC is only applied during the reaching phase. Once the state reaches the invariant set, the barrier-function-based VSC is applied to confine the state inside it. The resulting overall controller is a chattering-free VSC since the barrier-function based VSC is continuous. The steady-state error is limited to a minimal value by only specifying the barrier function parameter. Numerical simulations are conducted to show the efficiency of the new approach. Three types of VSC controllers for the Maglev system are compared. AVSCbf is compared to the performance of adaptive only VSC without the barrier function (AVSC) and both are designed in this work. AVSCbf is also compared to the classical VSC performance from previous work in the literature. The results of the comparison showed the efficiency of the proposed controller.
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Boz, Utku, and Ipek Basdogan. "A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control." Journal of Vibration and Control 24, no. 6 (July 6, 2016): 1086–100. http://dx.doi.org/10.1177/1077546316657502.
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In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.
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Ibraheem, Ghusn Abdul Redha, Ahmad Taher Azar, Ibraheem Kasim Ibraheem, and Amjad J. Humaidi. "A Novel Design of a Neural Network-Based Fractional PID Controller for Mobile Robots Using Hybridized Fruit Fly and Particle Swarm Optimization." Complexity 2020 (April 29, 2020): 1–18. http://dx.doi.org/10.1155/2020/3067024.
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The design of a swarm optimization-based fractional control for engineering application is an active research topic in the optimization analysis. This work offers the analysis, design, and simulation of a new neural network- (NN) based nonlinear fractional control structure. With suitable arrangements of the hidden layer neurons using nonlinear and linear activation functions in the hidden and output layers, respectively, and with appropriate connection weights between different hidden layer neurons, a new class of nonlinear neural fractional-order proportional integral derivative (NNFOPID) controller is proposed and designed. It is obtained by approximating the fractional derivative and integral actions of the FOPID controller and applied to the motion control of nonholonomic differential drive mobile robot (DDMR). The proposed NNFOPID controller’s parameters consist of derivative, integral, and proportional gains in addition to fractional integral and fractional derivative orders. The tuning of these parameters makes the design of such a controller much more difficult than the classical PID one. To tackle this problem, a new swarm optimization algorithm, namely, MAPSO-EFFO algorithm, has been proposed by hybridization of the modified adaptive particle swarm optimization (MAPSO) and the enhanced fruit fly optimization (EFFO) to tune the parameters of the NNFOPID controller. Firstly, we developed a modified adaptive particle swarm optimization (MAPSO) algorithm by adding an initial run phase with a massive number of particles. Secondly, the conventional fruit fly optimization (FFO) algorithm has been modified by increasing the randomness in the initialization values of the algorithm to cover wider searching space and then implementing a variable searching radius during the update phase by starting with a large radius which decreases gradually during the searching phase. The tuning of the parameters of the proposed NNFOPID controller is carried out by reducing the MS error of 0.000059, whereas the MSE of the nonlinear neural system (NNPID) is equivalent to 0.00079. The NNFOPID controller also decreased control signals that drive DDMR motors by approximately 45 percent compared to NNPID and thus reduced energy consumption in circular trajectories. The numerical simulations revealed the excellent performance of the designed NNFOPID controller by comparing its performance with that of nonlinear neural (NNPID) controllers on the trajectory tracking of the DDMR with different trajectories as study cases.
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Zaki Diab, Ahmed A., Abou-Hashema M. El-Sayed, Hossam Hefnawy Abbas, and Montaser Abd El Sattar. "Robust Speed Controller Design Using H_infinity Theory for High-Performance Sensorless Induction Motor Drives." Energies 12, no. 5 (March 12, 2019): 961. http://dx.doi.org/10.3390/en12050961.
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In this paper, a robust speed control scheme for high dynamic performance sensorless induction motor drives based on the H_infinity (H) theory has been presented and analyzed. The proposed controller is robust against system parameter variations and achieves good dynamic performance. In addition, it rejects disturbances well and can minimize system noise. The H controller design has a standard form that emphasizes the selection of the weighting functions that achieve the robustness and performance goals of motor drives in a wide range of operating conditions. Moreover, for eliminating the speed encoder—which increases the cost and decreases the overall system reliability—a motor speed estimation using a Model Reference Adaptive System (MRAS) is included. The estimated speed of the motor is used as a control signal in a sensor-free field-oriented control mechanism for induction motor drives. To explore the effectiveness of the suggested robust control scheme, the performance of the control scheme with the proposed controllers at different operating conditions such as a sudden change of the speed command/load torque disturbance is compared with that when using a classical controller. Experimental and simulation results demonstrate that the presented control scheme with the H controller and MRAS speed estimator has a reasonable estimated motor speed accuracy and a good dynamic performance.
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BOUMALHA, NOUREDDINE, DJILLALI KOUCHIH, and MOHAMED SEGHIR BOUCHERIT. "Sensorless Speed and Reactive Power Control of a Double-Feed Induction Generator using Adaptive Observer in Wind Turbine Power Plant." Algerian Journal of Signals and Systems 3, no. 2 (June 15, 2018): 54–64. http://dx.doi.org/10.51485/ajss.v3i2.59.
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This work presents a new method for the synthesis of a sensorless speed and reactive power control applied to a wind turbine system based to a Doubly Fed Induction Generator (DFIG). The proposed method based on adaptive observers: The rotor speed is adapted using adaptation mechanisms. Stability analysis based on Lyapunov theory is used to guarantee the stability of the observer. To verify the consistency of the proposed approach. We will be interested in the study of vector control based on the synthesis of classical controllers. Simulation results provided with the MATLAB/SIMULINK environment show the consistency of the proposed approaches.
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Ceballos Benavides, Gustavo E., Manuel A. Duarte-Mermoud, Marcos E. Orchard, and Juan Carlos Travieso-Torres. "Pitch Angle Control of an Airplane Using Fractional Order Direct Model Reference Adaptive Controllers." Fractal and Fractional 7, no. 4 (April 20, 2023): 342. http://dx.doi.org/10.3390/fractalfract7040342.
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This paper deals with the longitudinal movement control of an airplane (pitch angle) using fractional order adaptive controllers (FOACs). It shows the improvements achieved in the plane’s behavior, in terms of the minimization of a given performance index. At the same time, less control effort is needed to accomplish the control objectives compared with the classic integer order adaptive controllers (IOACs). In this study, fractional order direct model reference adaptive control (FO-DMRAC) is implemented at the simulation level, and exhibits a better performance compared with the classic integer order (IO) version of the DMRAC (IO-DMRAC). It is also shown that the proposed control strategy for FO-DMRAC reduces the resultant system control structure down to a relative degree 2 system, for which the control implementation is simpler and avoids oscillations during the transient period. Moreover, it is interesting to note that this is the first time that an FOAC with fractional adaptive laws is applied to the longitudinal control of an airplane. A suitable model for the longitudinal movement of the airplane related to the pitch angle θ as the output variable with the lifter angle (δe) as the control variable, is first analyzed and discussed to obtain a reliable mathematical model of the plane. All of the other input variables acting on the plane are considered as perturbations. For certain operating conditions defined by the flight conditions, an FO-DMRAC is designed, simulated, and analyzed. Furthermore, a comparison with the implementation of the classical adaptive general direct control (relative degree ≥ 2) is presented. The boundedness and convergence of all of the signals are theoretically proven based on the new Lemma 3, assuring the boundedness of all internal signals ω(t).
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Momeni, Z., and A. Bagchi. "Intelligent Control Methodology for Smart Highway Bridge Structures Using Optimal Replicator Dynamic Controller." Civil Engineering Journal 9, no. 1 (January 1, 2023): 1–16. http://dx.doi.org/10.28991/cej-2023-09-01-01.
Full textAbstract:
Control algorithms are an essential part of effective semi-active vibration control systems used for the protection of large structures under dynamic loading. Adaptive control algorithms, which are data-driven methods, have recently been developed to replace model-based control algorithms, thus improving efficiency. The dynamic parameters of semi-actively controlled infrastructures will change after significant vibration loading. As a result, these structures require real-time, effective control actions in response to changing conditions, which classical controllers are unable to provide. To improve the efficiency of the semi-active controller, the optimal control algorithm was developed in this study. The algorithm is the integration of the replicator dynamics with an improved non-dominated sorting genetic algorithm (NSGA), which is NSGA-II. The optimal parameters of replicator dynamics (total resources, growth rate, and fitness function), which represent the behavior of the actuators, were obtained through a multi-objective optimization process. The new control system was then used to reduce the vibrations of the isolated highway bridge, which is equipped with semi-active control devices known as MR dampers. Moreover, the current study improved the performance of the structural control system with minimum energy consumption by assigning a specific growth rate to each control device. In order to reduce the vibrations of the highway bridge, the results show that the performance of the optimal replicator controller is better than the performance of the classical control algorithms. Doi: 10.28991/CEJ-2023-09-01-01 Full Text: PDF
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Sengupta, Ipsita, Sagar Gupta, Dipankar Deb, and Stepan Ozana. "Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control." Applied Sciences 11, no. 20 (October 19, 2021): 9766. http://dx.doi.org/10.3390/app11209766.
Full textAbstract:
This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink.
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Znidi, Aicha, Khadija Dehri, and Ahmed Said Nouri. "Discrete indirect adaptive sliding mode control for uncertain Hammerstein nonlinear systems." Transactions of the Institute of Measurement and Control 44, no. 10 (January 9, 2022): 1907–21. http://dx.doi.org/10.1177/01423312211067801.
Full textAbstract:
The robustness issue of uncertain nonlinear systems’ control has attracted the attention of numerous researchers. In this paper, we propose three techniques to deal with the uncertain Hammerstein nonlinear model. First, a discrete sliding mode control (SMC) is developed, which is based on converting the original nonlinear system into a linearized one in the vicinity of the operating region using Taylor series expansion. However, the presence of relatively high nonlinearities and parameter variations leads to the deterioration of the desired performances. In order to overcome these problems and to improve the performance of classical SMC, we propose two solutions. The first one is based on the synthesis of a discrete SMC, taking into account the presence of nonlinearity. The second solution is a new discrete adaptive SMC for input–output Hammerstein model. In order to show the effectiveness of the proposed controllers, a detailed robustness analysis is clearly developed. Simulation examples are reported at the end of the paper.
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Lai, Pengyu, Yi Liu, Wei Zhang, and Hui Xu. "Intelligent controller for unmanned surface vehicles by deep reinforcement learning." Physics of Fluids 35, no. 3 (March 2023): 037111. http://dx.doi.org/10.1063/5.0139568.
Full textAbstract:
With the development of the applications of unmanned surface vehicles (USVs), USV automation technologies are attracting increasing attention. In the industry, through the subtask division, it is generally believed that course-keeping is a critical basic sub-system in a series of complex automation systems and affects USV automation performance to a great extent. By course-keeping, we mean USV adjusts its angle to the desired angle and keeps it. In recent decades, course-keeping has been mainly achieved through classical first principles technologies, such as proportion–integral–differential (PID) controllers, leading to extremely laborious parameter tuning, especially in changeable wave environments. With the emergence and extensive application of data-driven technologies, deep reinforcement learning is conspicuous in sequential decision-making tasks, but it introduces a lack of explainability and physical meaning. To take full advantage of the data-driven and first principles paradigm and easily extend to the industry, in this paper, we propose an intelligent adaptive PID controller enhanced by proximal policy optimization (PPO) to achieve USV high-level automation. We then further verify its performance in path-following tasks compared with the PID controller. The results demonstrate that the proposed controller inherits the merits of explainability from PID and excellent sequential decision making from PPO and possesses excellent disturbance rejection performance when facing the disturbance of a changeable wave environment.