Relevant bibliographies by topics / Fuzzy controller algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy controller algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Fuzzy controller algorithm"

1

Zhang, Cui Ping, Li Ping Sun, and Zhi Ying Yue. "Gasoline Engine Idle Speed Control Based on PID Fuzzy Algorithm." Advanced Materials Research 338 (September 2011): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amr.338.65.

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According to the characteristics of operating procedures of gasoline engine idle speed; a fuzzy control method is developed to control idle speed of gasoline engine. A novel controller is designed. The controller, which combines fuzzy logic algorithm with traditional PID algorithm, improves steady and dynamic performances of idle speed control. The method has the advantage of not requiring a precise mathematical model of the controlled object. By using SIMULINK simulation software of MATLAB, the simulation results obtained with the PID fuzzy controller show that the PID fuzzy controller has better controlled performances and robustness. It provides some reference values for further practical application.
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Castillo, Oscar, Fevrier Valdez, José Soria, Leticia Amador-Angulo, Patricia Ochoa, and Cinthia Peraza. "Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms." Algorithms 12, no. 1 (December 27, 2018): 9. http://dx.doi.org/10.3390/a12010009.

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This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.
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Giurgi, Gavril-Ionel, Lorant Andras Szolga, and Danut-Vasile Giurgi. "Benefits of Fuzzy Logic on MPPT and PI Controllers in the Chain of Photovoltaic Control Systems." Applied Sciences 12, no. 5 (February 23, 2022): 2318. http://dx.doi.org/10.3390/app12052318.

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This paper presents a comparative study between two maximum power point tracking (MPPT) algorithms, the incremental conductance algorithm (InC) and the fuzzy logic controller (FLC). The two algorithms were applied to a low photovoltaic power conversion system, and they both use different PI controllers and grid synchronization techniques. Moreover, both InC and FLC methods have Clarke and Park Transformation. To some extent, the incremental conductance and fuzzy logic controller approaches are similar, but their control loops are different. Therefore, the InC has classic Proportional Integrative (PI) controllers with simple phase-locked loops (PLL). At the same time, the FLC works with fuzzy logic PI controllers linked with the Second Order Generalized Integrator (SOGI). The proposed techniques examine the solar energy conversion performance of the photovoltaic (PV) system under possible irradiance changes and constant temperature conditions. Finally, a performance comparison has been made between InC and FLC, which demonstrates the effectiveness of the fuzzy controller over the incremental conductance algorithm. FLC turns to convert photovoltaic power easily, decreasing fluctuations, and it offers a quick response to the variation of solar irradiance (shading effect). The simulation results show a superior performance of the controller with fuzzy logic, which helps the inverter convert over 99% of the power generated by the photovoltaic panels. In comparison, the incremental conductance algorithm converts around 80%.
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Mohammed, Reham H., Ahmed M. Ismaiel, Basem E. Elnaghi, and Mohamed E. Dessouki. "African vulture optimizer algorithm based vector control induction motor drive system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (June 1, 2023): 2396. http://dx.doi.org/10.11591/ijece.v13i3.pp2396-2408.

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<span lang="EN-US">This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.</span>
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Kim, Min-Soeng, Sun-Gi Hong, and Ju-Jang Lee. "Self-Learning Fuzzy Logic Controller using Q-Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (September 20, 2000): 349–54. http://dx.doi.org/10.20965/jaciii.2000.p0349.

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Fuzzy logic controllers consist of if-then fuzzy rules generally adopted from a priori expert knowledge. However, it is not always easy or cheap to obtain expert knowledge. Q-learning can be used to acquire knowledge from experiences even without the model of the environment. The conventional Q-learning algorithm cannot deal with continuous states and continuous actions. However, the fuzzy logic controller can inherently receive continuous input values and generate continuous output values. Thus, in this paper, the Q-learning algorithm is incorporated into the fuzzy logic controller to compensate for each method’s disadvantages. Modified fuzzy rules are proposed in order to incorporate the Q-learning algorithm into the fuzzy logic controller. This combination results in the fuzzy logic controller that can learn through experience. Since Q-values in Q-learning are functional values of the state and the action, we cannot directly apply the conventional Q-learning algorithm to the proposed fuzzy logic controller. Interpolation is used in each modified fuzzy rule so that the Q-value is updatable.
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Pletl, Szilveszter, and Bela Lantos. "Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 2 (March 20, 2001): 81–89. http://dx.doi.org/10.20965/jaciii.2001.p0081.

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Soft computing (fuzzy systems, neural networks and genetic algorithms) can solve difficult problems, especially non-linear control problems such as robot control. In the paper two algorithms have been presented for the nonlinear control of robots. The first algorithm applies a new neural network based controller structure and a learning method with stability guarantee. The controller consists of the nonlinear prefilter, the feedforward neural network and feadback PD controllers. The fast learning algorithm of the neural network is based on Moore-Penrose pseudoinverse technique. The second algorithm is based on a decentralized hierarchical neuro-fuzzy controller structure. New approach to evolutionary algorithms called LEGA optimizes the controller during the teaching period. LEGA combines the standard GA technique with numerical optimum seeking for a limited number of elite individuels in each generation. It can lead to global optimum in few generations. The soft computing based nonlinear control algorithms have been applied for the control of a rigid link flexible joint (RLFJ) 4 DOF SCARA robot in order to prove the effectiveness of the proposed methods.
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Guo, Li-Xin, and Dinh-Nam Dao. "A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system." Journal of Vibration and Control 26, no. 13-14 (December 30, 2019): 1187–98. http://dx.doi.org/10.1177/1077546319890188.

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This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.
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Hadi, Alireza, Hossein Akbari, Khalil Alipour, and Bahram Tarvirdizadeh. "Precise position control of shape memory alloy–actuated continuum modules through fuzzy algorithm." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 2 (November 8, 2017): 121–36. http://dx.doi.org/10.1177/0959651817740001.

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Development of a fuzzy precise controller for the continuum modules utilizing shape memory alloy actuators is the main focus of this study. To this end, two continuum and flexible shape memory alloy–actuated modules, containing shape memory alloy wires or shape memory alloy springs, are considered as the testbed of the control problem to be tackled. The fuzzy controllers in this application are developed using two strategies in this research. In the first technique, the position errors of the two motion variables of the system are considered in the controller design process. The resulted controller is referred as “error-based fuzzy controller.” In the second technique, which is called as “improved fuzzy controller,” the parameters of the desired configuration of the system, in addition to their errors, are considered in controller design process. This procedure makes it possible to overcome the error-based fuzzy controller drawbacks. In order to validate the simulation results, experimental tests are conducted. Both simulation and experimental results reveal the performance of the developed novel two-stage improved fuzzy controller against error-based fuzzy controller and traditional proportional–integral–derivative controller.
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Esmaeili, Mehran, Hossein Shayeghi, Hamid Mohammad Nejad, and Abdollah Younesi. "Reinforcement learning based PID controller design for LFC in a microgrid." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 36, no. 4 (July 3, 2017): 1287–97. http://dx.doi.org/10.1108/compel-09-2016-0408.

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Purpose This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid. Design/methodology/approach To evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out using MATLAB/SIMULINK package. Findings Simulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions. Originality/value In this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.
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Situm, Z., D. Pavkovic, and B. Novakovic. "Servo Pneumatic Position Control Using Fuzzy PID Gain Scheduling." Journal of Dynamic Systems, Measurement, and Control 126, no. 2 (June 1, 2004): 376–87. http://dx.doi.org/10.1115/1.1767857.

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In this paper, a design procedure and experimental implementation of a PID controller is presented. The PID controller is tuned according to damping optimum in order to achieve precise position control of a pneumatic servo drive. It is extended by a friction compensation and stabilization algorithm in order to deal with friction effects. In a case of supply pressure variations, more robust control system is needed. It is implemented by extending the proposed PID controller with friction compensator with the gain scheduling algorithm, which is provided by means of fuzzy logic. The effectiveness of proposed control algorithms is experimentally verified on an industrial cylindrical rodless actuator controlled by a proportional valve.
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Dissertations / Theses on the topic "Fuzzy controller algorithm"

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Vick, Andrew W. "Genetic Fuzzy Controller for a Gas Turbine Fuel System." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1291053513.

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Liu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.

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This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.
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麥禮安 and Lai-on Mak. "Fuzzy logic statcom controller design with genetic algorithm application for stability enhancement of interconnected power systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B42128699.

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Mak, Lai-on. "Fuzzy logic statcom controller design with genetic algorithm application for stability enhancement of interconnected power systems." Click to view the E-thesis via HKUTO, 2000. http://sunzi.lib.hku.hk/hkuto/record/B42128699.

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Lima, Robson Pacífico Guimarães. "Uma aplicação baseada em sistemas imunológicos artificiais para detecção de falhas em uma plataforma de abastecimento." Universidade Federal da Paraí­ba, 2013. http://tede.biblioteca.ufpb.br:8080/handle/tede/5293.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
In this work, an Artificial-Immune-System based anomaly detection system applied to Water Supply System is presented. At normal working, the pressure level into the system is controlled by a Fuzzy Control System. As the Water Supply System is composed of pressure sensors, valves, pumps, and other devices, faults in these devices causing abnormal disturbances can occur. An algorithm of Artificial-Immune-System, namely, the Negative Selection Algorithm, is the base of the proposed anomaly detection system. The Negative Selection Algorithm verifies abnormal system conditions based on the normal system conditions. Experimental results show that the proposed system is effective in order to detect anomaly.
Neste trabalho é apresentado um método de detecção automática de falhas, baseado em Sistemas Imunológicos Artificias, aplicado em um sistema de abastecimento de água. Este processo utiliza um Sistema de Controle Fuzzy para manter o nível de pressão estabilizado em seu princípio de operação normal do sistema. Esta plataforma de abastecimento de água é composta por sensores de pressão, válvulas, bombas e outros dispositivos. Falhas nos componentes que compõem a plataforma poderão ocorrer causando perturbações em seu funcionamento. Um algoritmo, extraído dos Sistemas Imunológicos Artificiais, denominado de Algoritmo de Seleção Negativa, é a base de detecção de falhas proposto neste trabalho. Este algoritmo verifica condições de operação anormais baseado nas condições de funcionamento normal do sistema. Resultados das simulações e experimentos acerca da utilização deste algoritmo foram obtidos comprovando a eficiência dessa técnica.
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Hitchings, Mark R., and n/a. "Distance and Tracking Control for Autonomous Vehicles." Griffith University. School of Microelectronic Engineering, 1999. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050902.084155.

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The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.
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Hitchings, Mark. "Distance and Tracking Control for Autonomous Vehicles." Thesis, Griffith University, 1999. http://hdl.handle.net/10072/366396.

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The author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.
Thesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Science, Environment, Engineering and Technology
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LIMA, Fernanda Maria Maciel de. "PROPOSTA DE CONTROLE NEBULOSO BASEADO EM CRITÉRIO DE ESTABILIDADE ROBUSTA NO DOMÍNIO DO TEMPO CONTÍNUO VIA ALGORITMO GENÉTICO MULTIOBJETIVO." Universidade Federal do Maranhão, 2015. http://tedebc.ufma.br:8080/jspui/handle/tede/1861.

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A fuzzy project Takagi-Sugeno (TS) with robust stability based on the specifications of the gain and phase margins via multi-objective genetic algorithm in continuos time domain is proposed in this master thesis. A Fuzzy C-means (FCM) clustering algorithm is used to estimate the antecedent parameters and rules number of a fuzzy TS model by means of the input and output experimental data of the plant to be controlled, while minimum squares algorithm estimate the consequent parameters. A multi-objective genetic strategy is defined to adjust the parameters of a fuzzy PID controller, so that, the gain and phase margins of the fuzzy control system are close to the specified values. Two theorems are proposed to analyse the necessary and sufficient conditions for the fuzzy PID controller design to ensure the robust stability in the close-loop control. The fuzzy PID controller was simulated in the Simulink environment and compared with lead and delay compensator. Experimental results obtained in a control platform in real time to validation the methodology proposed are presented and compared with fuzzy PID controller obtained by the Ziegler Nichols method. The results demonstrate the effectiveness and practical feasibility of the proposed methodology.
Um projeto de controle nebuloso Takagi-Sugeno(TS) com estabilidade robusta baseado nas especificações das margens de ganho e fase via algoritmo genético multiobjetivo no domínio do tempo contínuo é proposto nesta dissertação. Um algoritmo de agrupamento Fuzzy C-Means (FCM) é usado para estimar os parâmetros do antecedente e o número da regras de um modelo nebuloso TS, por meio dos dados experimentais de entrada e de saída da planta a ser controlada, enquanto que o algoritmo de mínimos quadrados estima os parâmetros do consequente. Uma estratégia genética multiobjetiva é definida para ajustar os parâmetros de um controlador PID nebuloso, de modo que, as margens de ganho e fase do sistema de controle nebuloso estejam próximos dos valores especificados. São propostos dois teoremas que analisam as condições necessárias e suficientes para o projeto do controlador PID nebuloso de modo a garantir a estabilidade robusta na malha de controle. O controlador PID nebuloso foi simulado no ambiente Simulink e comparado com compensadores de avanço e de atraso e os resultados analisados. Resultados experimentais obtidos em uma plataforma de controle, em tempo real, para validação da metodologia proposta são apresentados e comparado com controlador PID nebuloso obtido pelo método de Ziegler Nichols. Os resultados obtidos demonstram a eficácia e viabilidade prática da metodologia proposta.
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Carlos, Luiz Amorim. "Algoritmos gen?ticos: uso de l?gica nebulosa e an?lise de converg?ncia por cadeia de Markov." Universidade Federal do Rio Grande do Norte, 2013. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15236.

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In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables
Neste trabalho, a cadeia de Markov ser? a ferramenta usada na modelagem e na an?lise de converg?ncia do algoritmo gen?tico, tanto para sua vers?o padr?o quanto para as demais vers?es que o algoritmo gen?tico permite. Al?m disso, pretende-se comparar o desempenho da vers?o padr?o com a vers?o nebulosa, por acreditar que esta vers?o d? ao algoritmo gen?tico uma grande capacidade para encontrar um ?timo global, pr?prio dos algoritmos de otimiza??o global. A escolha deste algoritmo deve-se tamb?m ao fato do mesmo ter se tornado, nos ?ltimos anos, uma das ferramentas mais usadas para achar uma solu??o do problema de otimiza??o. Esta escolha deve-se ? sua comprovada efic?cia na busca de uma solu??o de boa qualidade para o problema, considerando que o conhecimento de uma solu??o de boa qualidade torna-se aceit?vel tendo em vista que pode n?o existir um outro algorimo capaz de obter a solu??o ?tima, para muitos desses problemas. Entretanto, esse algoritmo pode ser definido, levando em conta que o mesmo ? dependente n?o apenas da forma como o problema ? representado, mas tamb?m como s?o definidos alguns dos operadores, desde sua vers?o padr?o, quando os par?metros s?o mantidos fixos, at? suas vers?es com par?metros vari?veis. Por isso, para se alcan?ar um bom desempenho com o aludido algoritmo ? necess?rio que o mesmo tenha um adequado crit?rio na escolha de seus par?metros, principalmente da taxa de muta??o e da taxa de cruzamento ou, at? mesmo, do tamanho da popula??o. ? importante lembrar que as implementa??es em que par?metros s?o mantidos fixos durante toda a execu??o, a modelagem do algoritmo por cadeia de Markov resulta numa cadeia homog?nea, e quando permite a varia??o de par?metros ao longo da execu??o, a cadeia de Markov que o modela passa a ser do tipo n?o-homog?nea. Portanto, na tentativa de melhorar o desempenho do algoritmo, alguns trabalhos t?m procurado realizar o ajuste dos par?metros atrav?s de estrat?gias que captem caracter?sticas intr?nsecas ao problema. Essas caracter?sticas s?o extra?das do estado presente de execu??o, com o fim de identificar e preservar algum padr?o relacionado a uma solu??o de boa qualidade e, ao mesmo tempo, descartando aquele padr?o de baixa qualidade. As estrat?gias de extra??o das caracter?sticas tanto podem usar t?cnicas precisas quanto t?cnicas nebulosas, sendo neste ?ltimo caso feita atrav?s de um controlador nebuloso. Com o fim de avaliar empiriccamente o desempenho de um algoritmo n?o-homog?neo, apresenta-se testes onde se compara o algoritmo gen?tico padr?o com o algoritmo gen?tico nebuloso, sendo a taxa de muta??o ajustada por um controlador nebuloso. Para isso, escolhe-se problemas de otimiza??o cujo n?mero de solu??es varia exponencialmente com o n?mero de vari?veis
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Pires, Danúbia Soares. "PROPOSTA DE CONTROLE NEBULOSO BASEADO EM CRITÉRIO DE ESTABILIDADE ROBUSTA NO DOMÍNIO DO TEMPO DISCRETO VIA ALGORITMO GENÉTICO MULTIOBJETIVO." Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/505.

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In this master thesis, a robust fuzzy digital PID control methodology based on gain and phase margins specifications, is proposed. A mathematical formulation, based on gain and phase margins specifications, the Takagi-Sugeno fuzzy model of the plant to be controlled, the structure of the digital PID controller and the time delay uncertain system, was developed. From input and output data of the plant, the fuzzy clustering Fuzzy C-Means (FCM) algorithm estimates the antecedent parameters (operation areas ) and the rules number of Takagi-Sugeno fuzzy model. The least squares algorithm provides the consequent parameters linear submodels. A multiobjective genetic strategy is defined to tune the fuzzy digital PID controller parameters, so the gain and phase margins specified to the fuzzy control system are get. An analysis of necessary and sufficient conditions for fuzzy digital PID controller design with robust stability, with the proposal of the two theorems are presented. The digital fuzzy PID controller was implemented on a platform designed for monitoring and control in real time, based on CompactRIO and LabVIEW 9073, National Instruments, of the Laboratory of Computational Intelligence Applied to Technology (ICAT/DEE/IFMA), applying the temperature control of a thermal plant. Experimental results show the efficiency of the proposed methodology, through tracking of the reference and the gain and phase margins keeping closed of the specified ones.
Nesta dissertação é proposta uma metodologia para projeto de controle PID digital nebuloso robusto baseado nas especificações das margens de ganho e fase. É desenvolvida uma formulação matemática, baseada nas especificações das margens de ganho e fase, no modelo nebuloso Takagi-Sugeno da planta a ser controlada, na estrutura do controlador PID digital e o atraso de tempo do sistema incerto. A partir dos dados de entrada e saída da planta, o algoritmo de agrupamento nebuloso Fuzzy C-Means (FCM), estima os parâmetros do antecedente (regiões de operação) e o número de regras do modelo nebuloso Takagi-Sugeno. O algoritmo de mínimos quadrados fornece os parâmetros dos submodelos lineares do consequente. Uma estratégia genética multiobjetiva é utilizada para encontrar os parâmetros do controlador PID digital nebuloso, de modo que as margens de ganho e fase especificadas para o sistema de controle nebuloso sejam alcançadas. Uma análise das condições necessárias e suficientes para o projeto do controlador PID digital nebuloso com estabilidade robusta, a partir da proposta de dois teoremas, é apresentada. O controlador PID digital nebuloso projetado foi implementado numa plataforma para supervisão e controle em tempo real, baseada no CompactRIO 9073 e no software LabVIEW, da National Instruments, do Laboratório de Inteligência Computacional Aplicada à Tecnologia (ICAT/DEE/IFMA), com aplicação ao controle de temperatura de uma planta térmica. Resultados experimentais mostram a eficiência da metodologia proposta, uma vez que a trajetória de referência é seguida e as margens de ganho e fase permanecem próximas às especificadas.
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Books on the topic "Fuzzy controller algorithm"

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. Chemical Optimization Algorithm for Fuzzy Controller Design. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8.

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McClintock, Shaunna. Soft computing: A fuzzy logic controlled genetic algorithm environment. [S.l: The Author], 1999.

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Amador, Leticia, and Oscar Castillo. Optimization of Type-2 Fuzzy Controllers Using the Bee Colony Algorithm. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54295-9.

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Rommelfanger, Heinrich. PC software FULPAL 2.0: An interactive algorithm for solving multicriteria fuzzy linear programs controlled by aspiration levels. Frankfurt/Main: Johann Wolfgang Goethe-Universität Frankfurt, Fachbereich Wirtschaftswissenschaften, 1995.

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Castillo, Oscar, Patricia Melin, and Leslie Astudillo. Chemical Optimization Algorithm for Fuzzy Controller Design. Springer London, Limited, 2014.

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Castillo, Oscar, Patricia Melin, and Leslie Astudillo. Chemical Optimization Algorithm for Fuzzy Controller Design. Springer, 2014.

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Precup, Radu-Emil, and Radu-Codrut David. Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems. Elsevier Science & Technology Books, 2019.

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Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems. Elsevier Science & Technology, 2019.

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Nature-inspired Optimization Algorithms for Fuzzy Controlled Servo Systems. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-00098-5.

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Amador, Leticia, and Oscar Castillo. Optimization of Type-2 Fuzzy Controllers Using the Bee Colony Algorithm. Springer International Publishing AG, 2017.

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Book chapters on the topic "Fuzzy controller algorithm"

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Grantner, J. L. "Parallel Algorithm for Fuzzy Logic Controller." In Fuzzy Logic, 177–95. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_6.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "The Proposed Chemical Reaction Algorithm." In Chemical Optimization Algorithm for Fuzzy Controller Design, 13–18. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_4.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "Introduction." In Chemical Optimization Algorithm for Fuzzy Controller Design, 1–3. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_1.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "Theory and Background." In Chemical Optimization Algorithm for Fuzzy Controller Design, 5–9. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_2.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "Chemical Definitions." In Chemical Optimization Algorithm for Fuzzy Controller Design, 11–12. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_3.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "Application Problems." In Chemical Optimization Algorithm for Fuzzy Controller Design, 19–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_5.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "Simulation Results." In Chemical Optimization Algorithm for Fuzzy Controller Design, 27–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_6.

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Astudillo, Leslie, Patricia Melin, and Oscar Castillo. "Conclusions." In Chemical Optimization Algorithm for Fuzzy Controller Design, 57–58. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05245-8_7.

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Yun, YoungSu, and Mitsuo Gen. "Adaptive Hybrid Genetic Algorithm with Fuzzy Logic Controller." In Fuzzy Sets Based Heuristics for Optimization, 251–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36461-0_16.

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Xie, Hongmei, Yuxiao Yan, and Tianzi Zeng. "Simulations of Fuzzy PID Temperature Control System for Plant Factory." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 1089–99. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_109.

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AbstractThe five key factors that affect plant growth are temperature, humidity, CO2 gas density, nutritious liquid density and light intensity. The monitoring and controlling of these factors are vital. Fuzzy PID controller technology for plant factory environment parameter controlling was proposed and temperature controlling using three different methods were given out. The physical and mathematical models of ordinary differential equation used in temperature subsystem in plant factory was established, traditional PID controller was discussed and specifically the fuzzification interface, membership function, fuzzy inference rule and the defuzzification procedure were designed for mere fuzzy and fuzzy PID controllers. Simulations for temperature controlling using pure PID, mere fuzzy and fuzzy PID control algorithm were performed respectively. The experimental results show that the performance of the novel fuzzy PID controller is best since it outperforms the other controllers in terms of stable error, overshooting and stabling time. The stable error, overshooting and time to stable for fuzzy PID are 0, 0.1% and 170 s respectively, all are the minimum among the three controllers.
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Conference papers on the topic "Fuzzy controller algorithm"

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Parimi, V. Ram Mohan, and Devendra P. Garg. "Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60502.

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This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.
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Lieh, Junghsen, and Wei Jie Li. "Fuzzy Logic Control of Material Forming Process." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0409.

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Abstract Adaptive controllers with self-adjustment capabilities possess many advantages over conventional methods. An adaptive fuzzy controller may be implemented either in a direct form or in an indirect form, and it is generally referred to as a self-organizing controller. One advantage of fuzzy controllers is their simple computation requirements in comparison with more algorithmic-based controllers. A metal forming process is a good candidate for the implementation of fuzzy logic rules because of its nonlinear and stochastic properties. In this paper, a prototype electromechanical forming machine was developed and tested. The system includes an optical sensor, an AC induction motor, a servo controller, a forming mechanism, and a microprocessor. The measured state variable is processed by the CPU with a fuzzy logic algorithm. The controller utilizes primary and secondary errors between the actual response and desired output to conduct rule reasoning. Results from testing are presented.
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Shill, Pintu Chandra, Kishore Kumar Pal, Md Faijul Amin, and Kazuyuki Murase. "Genetic algorithm based fully automated and adaptive fuzzy logic controller." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007560.

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Shirzi, Moteaal Asadi, and M. R. Hairi-Yazdi. "Active tracking using Intelligent Fuzzy Controller and kernel-based algorithm." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007585.

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Ansari, Afshin, Rasoul Rajaei, and Nasim Nourafza. "Fuzzy controller improvement through imperialist colonial algorithm." In 2011 23rd Chinese Control and Decision Conference (CCDC). IEEE, 2011. http://dx.doi.org/10.1109/ccdc.2011.5968997.

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Chae, Myungjin, Kyubyung Kang, Dan D. Koo, Sukjoon Oh, and Jae Youl Chun. "Fuzzy Controller Algorithm for Automated HVAC Control." In 37th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2020. http://dx.doi.org/10.22260/isarc2020/0078.

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Young Im Cho. "An improved fuzzy inference algorithm by weighted in fuzzy controller." In 2007 International Conference on Control, Automation and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iccas.2007.4406952.

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Shill, Pintu Chandra, Md Amjad Hossain, Md Faijul Amin, and Kazuyuki Murase. "An adaptive fuzzy logic controller based on real coded quantum-inspired evolutionary algorithm." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007374.

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Rahimi, Mohammad A., Rasoul Salehi, and Aria Alasty. "Designing Gear-Shift Pattern for an Electric Vehicle to Optimize Energy Consumption." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40457.

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In this paper optimization of energy consumption in an electric vehicle is presented. The main idea of this optimization is based on selecting the best gear level in driving the vehicle. Two algorithms for optimization are introduced which are based on fuzzy rules and fuzzy controllers. In first algorithm, fuzzy controller simulates energy consumption in different gear levels, and chooses the optimum gear level. While in second method, fuzzy controller detects the optimum gear level by measuring the vehicle’s average speed and acceleration. To investigate the performance of these controllers, a model of TOSAN vehicle is developed and the controllers outputs are checked in simulation of TOSAN being driven within drive cycles in the city of Tehran. It is shown that both algorithms are able to improve efficiency in typical city driving cycles.
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Yang, Shichun, Ming Li, Bin Xu, Bin Guo, and Chuangao Zhu. "Optimization of Fuzzy Controller Based on Genetic Algorithm." In 2010 International Conference on Intelligent System Design and Engineering Application (ISDEA). IEEE, 2010. http://dx.doi.org/10.1109/isdea.2010.159.

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Reports on the topic "Fuzzy controller algorithm"

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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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