Journal articles on the topic 'Fuzzy controller algorithm'
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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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Natsheh, Essam. "Dissimilarity Clustering Algorithm for Designing the PID-like Fuzzy Controllers." Journal of information and organizational sciences 45, no. 1 (June 15, 2021): 267–86. http://dx.doi.org/10.31341/jios.45.1.12.
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Fuzzy logic controller is one of the most prominent research fields to improve efficiency for process industries, which usually stick to the conventional proportional-integral-derivative (PID) control. The paper proposes an improved version of the three-term PID-like fuzzy logic controller by removing the necessity of having user-defined parameters in place for the algorithm to work. The resulting non-parametric three-term dissimilarity-based clustering fuzzy logic controller algorithm was shown to be very efficient and fast. The performance study was conducted by simulation on armature-controlled and field-controller DC motors, for linguistic type and Takagi-Sugeno-Kang (TSK) models. Comparison of the created algorithm with fuzzy c-means algorithm resulted in improved accuracy, increased speed and enhanced robustness, with an especially high increase for the TSK type model.
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Wang, Xue Song. "Research of Fuzzy PID Control Based on VHDL." Applied Mechanics and Materials 416-417 (September 2013): 885–89. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.885.
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This paper describes the use of VHDL and fuzzy controller design process.Compared with conventional PID control, fuzzy control is not dependent on the accurate mathematical model of the controlled object. The implementation of integrated practical operating experience and the operation is simple, fast response, anti-interference ability, strong robustness parameters of controlled object. In response to the limitations of the traditional PID control algorithm in the control field, the fuzzy control theory with the traditional PID control algorithm by combining the parameters self-tuning function, the proposed fuzzy self-tuning PID controller design method and algorithm-depth study analysis.
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Kirn Kumar, N., and V. Indra Gandhi. "Design of fuzzy logic controller for load frequency control in an isolated hybrid power system." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8273–83. http://dx.doi.org/10.3233/jifs-189147.
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As the world is moving towards green energy generation to reduce the pollution by renewable sources such as wind, solar, geothermal and more. These sources are intermittent in nature, to coordinate and control with traditional power generating units a control technique is necessary. This paper mainly focuses on the design of fuzzy based classical controller using a PSO algorithm for optimal controller gains to control the frequency variations in island hybrid power system. The considered mathematical model comprises of a diesel generating model, wind turbine generator and a battery storage system. Fuzzy is an intelligent controller which is designed with trial and error rules or on the basis of past experience provided by experts or by optimization methods for optimized gains using computational algorithms. To give best solution for these kinds of problems with FLCs traditional controllers are integrated with fuzzy logic. The PSO algorithm is applied to tune the classical controller gains to decrease the frequency deviation of the island power system, during the different load and wind disturbances. The Fuzzy PID classical controller shows the best performance compared with the only fuzzy and Fuzzy-PI controller configurations by illustrating the under shoot, overshoot and settling time and the proposed method is robust for various loading conditions and different wind changes.
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SOUKKOU, A., A. KHELLAF, and S. LEULMI. "SYSTEMATIC DESIGN PROCEDURE OF TS-TYPE FUZZY CONTROLLERS." International Journal of Computational Intelligence and Applications 06, no. 04 (December 2006): 531–49. http://dx.doi.org/10.1142/s1469026806002106.
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This paper contributes a new alternative for the synthesis of Takagi–Sugeno fuzzy logic controller with reduced rule base. A Genetic Approach to Fuzzy Supervised Learning algorithm called GAFSL based on the Multiobjective Genetic Algorithms (MGAs) is used to construct the proposed robust fuzzy controller. The result controller is similar to nonlinear PI/PD controllers. The tuning algorithm cannot only tune the scaling factors, the shapes of membership functions, and the consequent values, but also optimize the number of rules as possible with guaranteed desired performances: accuracy and robustness. The construction of the chromosomes is based on the mixed binary–real coding system. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that represent the fuzzy knowledge base. The concept of elite strategy is adopted, where the best individuals in a population are regarded as elites. Computer simulation results on two nonlinear problems that are derived to demonstrate the powerful GAFSL algorithm.
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Balochian, Saeed, and Eshagh Ebrahimi. "Parameter Optimization via Cuckoo Optimization Algorithm of Fuzzy Controller for Liquid Level Control." Journal of Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/982354.
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Cuckoo optimization algorithm (COA) is one of the latest evolutionary algorithms. Finding the best optimal point, rapid convergence, and simplicity in determining algorithm parameters are some merits of COA. In this paper, COA is applied to tuning optimal fuzzy parameters for Sugeno-type fuzzy logic controllers (S-FLCs) which are used for liquid level control. A programmable logic controller (PLC) is used with fuzzy controller. For this purpose, a liquid level control set and PLC have been assembled together. MATLAB/Simulink program has been used to achieve the optimal parameters of the membership functions. The results show clearly that the optimized FLC using COA has better performance compared to manually adjustments of the system parameters for different datasets.
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Yang, Jun Jing, Hong Yan Chu, Li Gang Cai, and Lei Su. "Fuzzy Control of Printing Color Quality Based on Genetic Algorithm." Advanced Materials Research 472-475 (February 2012): 3071–77. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3071.
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Abstract : Aiming at the controlled object with large lag, model uncertainty and time variation due to the effects of working environment in printing process, and printing process requires few adjustment times, this paper designs a T-S fuzzy controller based on the theoretical model of printing color quality control, and uses the genetic algorithm to optimize the initial control rules of fuzzy controller . The optimization method aims at the problems of less known condition and the uncertain effects due to the environmental changes after the first printing. In the process of optimization, the theoretical model of the printing color quality control is used as the controlled object, and the parameters of control rule corresponding to the points of special error are optimized one by one, then the general fuzzy control rules can be got. Finally, an example illustrates the process of this method, and the robustness of the optimized fuzzy controller is analyzed. From the control results got by the optimized fuzzy controller, it can be seen that this method improves the control effects greatly, and reduces adjustment times. Finally, this paper gives some suggestions on its further perfection.
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Changizi, Nemat, Mahbubeh Moghadas, Mohamad Reza Dastranj, and Mohsen Farshad. "Design a Fuzzy Logic Based Speed Controller for DC Motor with Genetic Algorithm Optimization." Applied Mechanics and Materials 110-116 (October 2011): 2324–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.2324.
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In this paper, an intelligent speed controller for DC motor is designed by combination of the fuzzy logic and genetic algorithms. First, the speed controller is designed according to fuzzy rules such that the DC drive is fundamentally robust. Then, to improve the DC drive performance, parameters of the fuzzy speed controller are optimized by using the genetic algorithm. Simulation works in MATLAB environment demonstrate that the genetic optimized fuzzy speed controller became very strong, gives very good results and possesses good robustness.
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Byun, Kwang-Sub, Chang-Hyun Park, and Kwee-Bo Sim. "Co-Evolution of Fuzzy Controller for the Mobile Robot Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 356–61. http://dx.doi.org/10.20965/jaciii.2004.p0356.
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In this paper, we design the fuzzy rules using a modified Nash Genetic Algorithm. Fuzzy rules consist of antecedents and consequents. Because this paper uses the simplified method of Sugeno for the fuzzy inference engine, consequents have not membership functions but constants. Therefore, each fuzzy rule in this paper consists of a membership function in the antecedent and a constant value in the consequent. The main problem in fuzzy systems is how to design the fuzzy rule base. Modified Nash GA coevolves membership functions and parameters in consequents of fuzzy rules. We demonstrate this co-evolutionary algorithm and apply to the design of the fuzzy controller for a mobile robot. From the result of simulation, we compare modified Nash GA with the other co-evolution algorithms and verify the efficacy of this algorithm.
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Suwarno, Iswanto, Yaya Finayani, Robbi Rahim, Jassim Alhamid, and Ahmed Ramadhan Al-Obaidi. "Controllability and Observability Analysis of DC Motor System and a Design of FLC-Based Speed Control Algorithm." Journal of Robotics and Control (JRC) 3, no. 2 (February 5, 2022): 227–35. http://dx.doi.org/10.18196/jrc.v3i2.10741.
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DC motor is an electrical motor widely used for industrial applications, mostly to support production processes. It is known for its flexibility and operational-friendly characteristics. However, the speed of the DC motor needs to be controlled to have desired speed performance or transient response, especially when it is loaded. This paper aims to design a DC motor model and its speed controller. First, the state space representation of a DC motor was modeled. Then, the controllability and observability were analyzed. The transfer function was made based on the model after the model was ensured to be fully controllable and observable. Therefore, a fuzzy logic controller is employed as its speed controller. Fuzzy logic controller provides the best system performance among other algorithms; the overshoot was successfully eliminated, rise time was improved, and the steady-state error was minimized. The proposed control algorithm showed that the speed controller of the DC motor, which was designed based on the fuzzy logic controller, could quickly control the speed of the DC motor. The detail of resulted system performance was 2.427 seconds of rising time, 11 seconds of settling time, and only required 12 seconds to reach the steady state. These results were proved faster and better than the system performance of PI and PID controllers.
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Javanbakht, Mohammad, and Mohammad Javad Mahmoodabadi. "Achieving More Stringent Levels of Comfort via an Adaptive Fuzzy Controller Optimized by the Gravitational Search Algorithm for a Half-Body Car Model." Volume 24, No 3, September 2019 24, no. 3 (September 2019): 567–77. http://dx.doi.org/10.20855/jav.2019.24.31399.
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An optimal adaptive fuzzy controller is designed to achieve more stringent levels of comfort for a half-body car model. This aim will be fulfilled by reducing road disturbances and decreasing the acceleration of the body. The proposed controller consists of two adaptive fuzzy controllers with two fuzzy systems. Each one has two inputs, one output and twenty five linguistic fuzzy IF-THEN rules. Every input has five Gaussian membership functions and uses the product inference engine, singleton fuzzifier and the centre average defuzzifier. In order to determine the optimal parameters for the Adaptive Fuzzy Controller (AFC), the Gravitational Search Algorithm (GSA) is applied. The relative displacement between spring mass and tire, along with the acceleration of the body, are the two objective functions being applied in the optimization algorithm. The results illustrate the superiority of the proposed optimal adaptive fuzzy controller in comparison with traditional controllers.
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Bouzaida, Sana, and Anis Sakly. "Adaptive Neuro-Fuzzy Sliding Mode Controller." International Journal of System Dynamics Applications 7, no. 2 (April 2018): 34–54. http://dx.doi.org/10.4018/ijsda.2018040103.
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A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model information. An adaptive training algorithm based on cooperative particle sub-swarm optimization is used for the online tuning of the controller parameters to deal with system uncertainties and disturbances. The algorithm was derived in the sense of Lyapunov stability analysis in order to guarantee the high quality of the controlled system. The performance of the proposed algorithm is evaluated against two well-known benchmark problems and simulation results that illustrate the effectiveness of the proposed controller.
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Zhang, Yong Chao, Wen Zhuang Zhao, and Jin Lian Chen. "The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 191–95. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.191.
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How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.
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Hu, Huangshui, Tingting Wang, Siyuan Zhao, and Chuhang Wang. "Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller." Advances in Mechanical Engineering 11, no. 11 (November 2019): 168781401989019. http://dx.doi.org/10.1177/1687814019890199.
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In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.
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Wu, Hao, and Taoyan Zhao. "Design of interval type-2 fuzzy fractional order PID controller based on particle swarm optimization." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012063. http://dx.doi.org/10.1088/1742-6596/2258/1/012063.
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Abstract In this paper, an interval type-2 fuzzy fractional order PID controller (IT2F-FOPIDC) based on particle swarm optimization is proposed. The proposed controller has two adjustable degree of freedom parameters, so the control range of the controller parameters becomes larger and can control the controlled object more flexibly. Aiming at the difficult problem of many controller parameters and tuning, this paper introduces particle swarm optimization algorithm to optimize the controller parameters. In order to avoid the problems of long iteration time and high computational cost caused by Karnik-Mendel reduction algorithm, the enhanced iterative with stop condition algorithm is selected for the proposed controller. Finally, the proposed controller is applied to the second order systems with time delays. Simulation results show that the proposed strategy has faster response time and smaller oscillation amplitude, and its performance is better than fuzzy controller and interval type-2 fuzzy controller.
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Chen, Yin Ping, and Hong Xia Wu. "Fuzzy Neural Network Controller Based on Hybrid GA-BP Algorithm." Advanced Materials Research 823 (October 2013): 335–39. http://dx.doi.org/10.4028/www.scientific.net/amr.823.335.
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This paper presents a hybrid GA-BP algorithm for fuzzy neural network controller (FNNC). BP algorithm is a method to monitor learning, easily realized and with good local searching ability. But it depends too much on the the initial states of the network. Genetic algorithm is a random search algorithm which has strong global searching ability. The hybrid GA-BP algorithm ensure the global convergence of learning by genetic algorithm, overcomes the BP algorithms dependency on the initial states on the one hand. On the other hand, combined with the BP algorithm overcome the simple genetic algorithms randomness, improve the searching efficiency. The simulations on the inverted pendulun problem show good performance and robustness of the proposed fuzzy neural network controller based on hybrid GA-BP algorithm.
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Ataşlar-Ayyıldız, B., and O. Karahan. "Design of a MAGLEV System with PID Based Fuzzy Control Using CS Algorithm." Cybernetics and Information Technologies 20, no. 5 (December 1, 2020): 5–19. http://dx.doi.org/10.2478/cait-2020-0037.
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AbstractThe main aim of this study consists of proposing a simple but effective and robust approach for PID type fuzzy controller (Fuzzy-PID) in order to improve the dynamics and stability of a magnetic ball levitation system. The design parameters of the proposed controller are optimally determined based on Cuckoo Search (CS) algorithm. During the optimization, a time domain objective function is used for minimizing the values of common step response characteristics for the optimal selection of the controller parameters. Robustness tests are performed to evaluate the performance of the proposed controller through extensive simulations under load disturbance, parametric variation and changes in references. Moreover, to show the advantage and compare the performance of the proposed controller, the PID and Fractional Order PID (FOPID) controllers tuned with CS are designed. The simulation results and comparisons with the CS based PID and FOPID controllers demonstrate that the CS based Fuzzy-PID controller has superior performance depending on small overshoot, short settling time, fast rise time and minimum steady state error. Compared with the PID and FOPID controller tuned with CS, the simulation results show that the proposed Fuzzy-PID controller tuned with CS outperforms in terms of the accuracy, robustness and the least control effort.
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Dai, Aini, Xiaoguang Zhou, and Xiangdong Liu. "A GODFIP Control Algorithm for an IRC Grain Dryer." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1406292.
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Drying is an energy intensive and complex nonlinear process and it is difficult to control and make the traditional control meet the challenges. In order to effectively control the output grain moisture content of a combined infrared radiation and convection (IRC) grain dryer, taking into account the superiority of the fuzzy control method in dealing with complex systems, in this article, a genetic optimization dual fuzzy immune PID (Proportional-Integral-Derivative) (GODFIP) controller was proposed from the aspects of energy savings, stability, accuracy, and rapidity. The structure of the GODFIP controller consists of two fuzzy controllers, a PID controller, an immune algorithm, and a genetic optimization algorithm. In addition, a NARX model which can give relatively good predictive output information of the IRC dryer was established and used to represent the actual drying process to verify the control performance in the control simulation and anti-interference tests. The effectiveness of this controller was demonstrated by computer simulations, and the anti-interference performance comparative study with the other controllers further confirmed the superiority of the proposed grain drying controller which has the least value of performance objective function, the shortest rising time, and the best anti-interference ability compared to the other three compared controllers.
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Li, Wei, Jingqian Wen, Qing Jiang, Liangtu Song, and Zhengyong Zhang. "Implementation of a Fuzzy Logic Control Strategy on a Harvester’s Controller Based on MATLAB Environment." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 13 (December 15, 2019): 1959043. http://dx.doi.org/10.1142/s0218001419590432.
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Due to the nonlinear process of grain harvesting, there is no precise mathematical model to describe the behavior of the cleaning system of a harvester. Both the classical control and modern control methods cannot fulfil the requirements. Owing to this, the intelligent control algorithm was proposed, and the fuzzy logic control (FLC) method is a type of this. At present, most FLC algorithms are proposed in a MATLAB environment. However, the control problems in reality are controlled by microcomputer controllers with different chips. The control language of the microcomputer controller is usually written in C language. It is impossible to directly migrate the algorithm between these two different languages. Therefore, it is an important issue to transplant the FLC algorithm procedure written by MATLAB to the microcomputer controller. To realize the above target, we have built a complete set of control systems for our harvester’s cleaning system based on an upper computer and an STM32 core-chip controller. By means of combining FLC theory and expert knowledge, we adopted an improved FLC algorithm for the cleaning system, which is mounted in our self-designed combine harvester. Through this scheme, we have realized the objective of migrating the FLC algorithm from a MATLAB environment to the controller. The results of the experiment show that our method is reliable.
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Can, Erol. "MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE." Acta Polytechnica 59, no. 1 (February 28, 2019): 1–11. http://dx.doi.org/10.14311/ap.2019.59.0001.
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A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.
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Siddikov, I. H., P. I. Kalandarov, and D. B. ,. Yadgarova. "Engineering Calculation And Algorithm Of Adaptation Of Parameters Of A Neuro-Fuzzy Controller." American Journal of Applied sciences 03, no. 09 (September 30, 2021): 41–49. http://dx.doi.org/10.37547/tajas/volume03issue09-06.
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As part of the study, a control scheme with the adaptation of the coefficients of the neuron-fuzzy regulator implemented. The area difference method used as a training method for the network. It improved by adding a rule base, which allows choosing the optimal learning rate for individual neurons of the neural network. The neural network controller applied as a superstructure of the PID controller in the process control scheme. The dynamic object can function in different modes. This technological process operates in different modes in terms of loading and temperature setpoints. Because of experiments, the power consumption and the amount of time required maintaining the same absorption process, using a conventional PID controller and a neural-network controller evaluated. It concluded that the neuro-fuzzy controller with a superstructure reduced the transient time by 19%.
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Yan, Dongwen. "A method to solve the problem of low precision of micro stabilized platform caused by frame coupling - Based on Fuzzy PID." E3S Web of Conferences 284 (2021): 04008. http://dx.doi.org/10.1051/e3sconf/202128404008.
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Aiming at the problem of low control efficiency of small stable platform due to frame coupling, an intelligent control algorithm (fuzzy-PID) combining fuzzy controller with traditional PID is designed. The fuzzy PID controller is added to the position closed loop of the stable platform control system, and the motor position signal is collected and analyzed. Compared with the traditional PID control algorithm, the fuzzy PID control algorithm has the advantages of small overshoot, high control precision and strong anti-interference ability. The simulation test in Simulink environment shows that the overshoot of the system is reduced under the algorithm σ, which can be controlled within 1% and the adjusting time t is controlled within 0.5s, which can realize the stable control of the yaw angle, pitch angle and roll angle of the stabilized platform.
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Ghadiri, Hamid, Hamed Khodadadi, Hooman Eijei, and Milad Ahmadi. "PSO based Takagi-Sugeno fuzzy PID controller design for speed control of permanent magnet synchronous motor." Facta universitatis - series: Electronics and Energetics 34, no. 2 (2021): 203–17. http://dx.doi.org/10.2298/fuee2102203g.
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A permanent magnet synchronous motor (PMSM) is one kind of popular motor. They are utilized in industrial applications because their abilities included operation at a constant speed, no need for an excitation current, no rotor losses, and small size. In the following paper, a fuzzy evolutionary algorithm is combined with a proportional-integral-derivative (PID) controller to control the speed of a PMSM. In this structure, to overcome the PMSM challenges, including nonlinear nature, cross-coupling, air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy logic-PID (TSFL-PID) controller is designed. Additionally, the particle swarm optimization (PSO) algorithm is developed to optimize the membership functions' parameters and rule bases of the fuzzy logic PID controller. For evaluating the proposed controller's performance, the genetic algorithm (GA), as another evolutionary algorithm, is incorporated into the fuzzy PID controller. The results of the speed control of PMSM are compared. The obtained results demonstrate that although both controllers have excellent performance; however, the PSO based TSFL-PID controller indicates more superiority.
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Sun, Hong Xing, Chuang Gao, and Na Li. "Research on Dissolved Oxygen Intelligent Control System in Waste Water Treatment." Advanced Materials Research 591-593 (November 2012): 1461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1461.
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By combining with the research hot spot of intelligent control, the oxygen fuzzy-single neuron PID compound controller is designed based on control increment. It contains the advantages of fuzzy controller and the single neuron PID controller. The problem of causing the oscillation in switching two kinds of controllers is solved. The results of the dissolved oxygen fuzzy-single neurons PID controller and fuzzy controller simulation are compared. The results show that the control effect of dissolved oxygen is more ideal. Thus the algorithm is effective and feasible.
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Rana, Sudesh. "DEVELOPMENT OF PID LIKE FLC ALGORITHM FOR INDUSTRIAL APPLICATIONS." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 12–22. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.26.
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Now a day, in many industries different types of controllers (PD, PID, PLC, FLC etc.) are used. One of them is fuzzy logic controller. Here we develop a PID like fuzzy logic controller for industrial application, such application is water purification plant. For developing the PID like FLC, first we have to design a PID algorithm than we develop an algorithm for fuzzy logic controller. By comparing this two of controller we will develop a PID like FLC. A simple PID controller is sum of three type of controller proportional, integral and derivative controller, after simulated on MATLAB. Same cases we can be develop a structure of FLC for water purification plant. In the water purification plant raw water or ground water is promptly purified by injecting chemical rates at rates, related to water quality [13][2]. The feed of chemical rate judged and determined by the skilled operator. Here we try to develop an FLC algorithm so that the feed rate of coagulant is can be judged automatically without any skilled operator, than compose a PID like FLC for water purification plant process.
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Kulikova, Irina V. "MODELING THE SYNTHESIS OF TAKAGI — SUGENO — KANG FUZZY CONTROLLERS IN SOME CONTROL SYSTEMS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 2 (2021): 147–69. http://dx.doi.org/10.21684/2411-7978-2021-7-2-147-169.
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Modern challenges in a post-industrial society require further development of management systems for complex technical and technological phenomena and processes. Effective control of an object is possible if a controller, or a fuzzy controller, correctly generates the required control action. Recently, fuzzy controllers have been very popular. Fuzzy logical statements in this case help considering various nonlinear relationships. The synthesis of the fuzzy controller parameters allows for more efficient operation of the control system. A possible option for obtaining the best set of parameters for a fuzzy controller is the use of genetic algorithms for its synthesis. The use of genetic algorithms for the fuzzy controllers synthesis can lead to the fact that the elements of its parameters array will change in such a way that an incorrect value of one or more elements will occur. This situation leads to impossibility of composing membership functions for the terms of the variables of the fuzzy controller. Incorrect value formation is excluded by constructing a limited functional dependency. This paper proposes a mathematical model of the parameters of the term-set of variables of a fuzzy controller of the Takagi — Sugeno — Kang type of the zero and first orders. The authors disclose the content of the conditions and conclusions of the rule base for the fuzzy controller of the above type. As a result of the simulation, some operations of the genetic algorithm are implemented using a random number generator. Graphical models of the membership functions of the input variables of the fuzzy controller of the type under consideration clearly illustrate the occurrence of all parameters in their range of possible values. A description of the control system operation with two control parameters and one control action at the specified values of the control parameters is presented.
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Lippe, Wolfram-M., Steffen Niendieck, and Andreas Tenhagen. "On the Optimization of Fuzzy-Controllers by Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (June 20, 1999): 158–63. http://dx.doi.org/10.20965/jaciii.1999.p0158.
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Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.
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Bernal, Emer, Oscar Castillo, José Soria, and Fevrier Valdez. "Generalized type-2 fuzzy logic in galactic swarm optimization: design of an optimal ball and beam fuzzy controller." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3545–59. http://dx.doi.org/10.3233/jifs-191873.
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In this paper we present a modification based on generalized type-2 fuzzy logic to an algorithm that is inspired on the movement of large masses of stars and their attractive force in the universe, known as galactic swarm optimization (GSO). The modification consists on the dynamic adjustment of parameters in GSO using type-1 and type-2 fuzzy logic. The main idea of the proposed approach is the application of fuzzy systems to dynamically adapt the parameters of the GSO algorithm, which is then applied to parameter optimization of the membership functions of the bar and ball fuzzy controller. The experimentation was carried out using the original GSO algorithm, and the type-1 and type-2 fuzzy variants of GSO. In addition a disturbance was added to the bar and ball fuzzy controller plant to be able to validate the effectiveness of the proposed approach in optimizing fuzzy controllers. A formal comparison of results is performed with statistical tests showing that GSO with generalized type-2 fuzzy logic is the best method for optimizing the fuzzy controller.
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Tran, Tuan Quang, and Minh Xuan Phan. "MODEL PREDICTIVE CONTROL BASED ON FUZZY SYSTEM, APPLICATION FOR A CONTINUOUS STIRRED TANK REACTOR (CSTR)." Science and Technology Development Journal 13, no. 1 (March 30, 2010): 16–23. http://dx.doi.org/10.32508/stdj.v13i1.2064.
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The paper presents one method to design the Model Predictive Controller based on Fuzzy Model. The Plant is simulated by Takagi-Sugeno Fuzzy Model and the Optimisation Problem is solved by the Genetic Algorithms. By using the Fuzzy Model and Genetic Algorithm this MPC gives better quality than the other General Predictive Controllers. The case study of a continuous stirred tank reactor (CSTR) control is presented in this paper.
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Abdalla, M. O., and T. A. Al–Jarrah. "Autogeneration of Fuzzy Logic Rule-Base Controllers." Applied Mechanics and Materials 110-116 (October 2011): 5123–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5123.
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A novel Fuzzy Logic controller design methodology is presented. The method utilizes a Particle Swarm Optimization (PSO) binary search algorithm to generate the rules for the Fuzzy Logic controller rule-base stage without human experience intervention. The proposed technique is compared with the well established Lyapunov based Fuzzy Logic controller design in generating the rules. Finally, the controller’s effectiveness and performance are tested, verified and validated using an elevator control application. The novel controller’s results are to be compared with traditional Proportional Integral Derivative (PID) controller and classical Fuzzy Logic (FL) controllers.
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Guo, Yufei, Leru Luo, and Changchun Bao. "Design of a Fixed-Wing UAV Controller Combined Fuzzy Adaptive Method and Sliding Mode Control." Mathematical Problems in Engineering 2022 (January 31, 2022): 1–22. http://dx.doi.org/10.1155/2022/2812671.
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To overcome the complexity of the coupled nonlinear model of a fixed-wing UAV system and the uncertainty caused by a large number of interference factors, a control algorithm combining fuzzy adaptive control and sliding mode variable structure control was proposed. The controller algorithm mainly relies on the sliding mode variable structure control method to solve the control problem of the strongly coupled complex nonlinear system. Based on sliding mode control, a fuzzy adaptive method is introduced to reduce the chattering problem of the traditional sliding mode control, and the uncertain parameters and unknown functions caused by external disturbances are approximated by this method. In this study, two types of fuzzy adaptive sliding mode controller were designed according to the different object ranges of the fuzzy adaptive algorithm. In addition, the stability of the controllers was verified using the Lyapunov method. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed controllers by comparing with the traditional sliding mode controller.
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Zhang, Liying, and Manchao Li. "Taekwondo Trajectory Tracking Based on Multitarget Detection Algorithm." Mobile Information Systems 2022 (September 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/3416682.
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In order to further improve the effect of fuzzy PID controller applied to joint robot trajectory tracking control, a Taekwondo trajectory tracking method based on multitarget detection algorithm is proposed in this study. Firstly, a fuzzy PID controller for trajectory tracking of joint robot is designed. Secondly, considering the two optimization objectives of controller output torque and trajectory tracking control deviation, an improved multiobjective PSO algorithm is designed to optimize the membership function and fuzzy rules of fuzzy PID controller. Finally, the multiobjective PSO algorithm and the improved multiobjective PSO algorithm are used to optimize the trajectory tracking fuzzy PID controller. The vector sets of the two optimization objectives are obtained, and the optimization results are compared and analyzed. The simulation results show that the simulation step length is t = 0.01 s, and the total simulation time is 10 s. Using the controller, the simulation values and output torque values of each joint angle at each time can be obtained, which are used to calculate the two objective function values in the optimization algorithm. According to the process, the optimization algorithm parameters are set, and the control simulation system designed above is run to complete the improved multiobjective PSO optimization of the trajectory tracking fuzzy PID controller of the joint robot. Conclusion. The improved multiobjective PSO algorithm has better nondominated solution set, which verifies the effectiveness and superiority of the algorithm to optimize the robot trajectory tracking fuzzy PID controller.
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Fukumi, Junji, Takuya Kamano, Takayuki Suzuki, and Yu Kataoka. "Positioning System with Progressive Wave-Type Ultrasonic Motor under Self-Tuning Fuzzy Control." Journal of Robotics and Mechatronics 7, no. 1 (February 20, 1995): 63–68. http://dx.doi.org/10.20965/jrm.1995.p0063.
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This paper considers the use of a self-tuning fuzzy controller for a positioning system with a progressive wavetype ultrasonic motor. The system consists of a feedback loop with a conventional controller and a self tuning fuzzy controller. The objective of the self tuning fuzzy controller is to restrain the adverse effect of nonlinear characteristics of the motor and to improve the tracking performance. The self-tuning fuzzy controller is functionally divided into two layers. The fuzzy rules are automatically adjusted by a tuning algorithm so that the tracking error is minimized in the upper layer. In lower layer, the output signal of the self tuning fuzzy controller is obtained by fuzzy reasoning procedure. After the tuning process is completed, the tracking error almost converges to zero, and the ultrasonic motor is no longer controlled by the fixed gain feedback controller but by the self-tuning fuzzy controller. The effectiveness of the proposed self-tuning fuzzy controller is demonstrated by an experiment.
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Lal, Deepak Kumar, and Ajit Kumar Barisal. "Grasshopper Algorithm Optimized Fractional Order Fuzzy PID Frequency Controller for Hybrid Power Systems." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 6 (November 22, 2019): 519–31. http://dx.doi.org/10.2174/2352096511666180717142058.
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Background: Due to the increasing demand for the electrical power and limitations of conventional energy to produce electricity. Methods: Now the Microgrid (MG) system based on alternative energy sources are used to provide electrical energy to fulfill the increasing demand. The power system frequency deviates from its nominal value when the generation differs the load demand. The paper presents, Load Frequency Control (LFC) of a hybrid power structure consisting of a reheat turbine thermal unit, hydropower generation unit and Distributed Generation (DG) resources. Results: The execution of the proposed fractional order Fuzzy proportional-integral-derivative (FO Fuzzy PID) controller is explored by comparing the results with different types of controllers such as PID, fractional order PID (FOPID) and Fuzzy PID controllers. The controller parameters are optimized with a novel application of Grasshopper Optimization Algorithm (GOA). The robustness of the proposed FO Fuzzy PID controller towards different loading, Step Load Perturbations (SLP) and random step change of wind power is tested. Further, the study is extended to an AC microgrid integrated three region thermal power systems. Conclusion: The performed time domain simulations results demonstrate the effectiveness of the proposed FO Fuzzy PID controller and show that it has better performance than that of PID, FOPID and Fuzzy PID controllers. The suggested approach is reached out to the more practical multi-region power system. Thus, the worthiness and adequacy of the proposed technique are verified effectively.
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Li, Ruifeng, and Peifen Gong. "Fuzzy PID Speed Controller of DC Motor Based on MATLAB." Journal of Physics: Conference Series 2417, no. 1 (December 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2417/1/012037.
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Because of its few of components and low price, DC motors frequently serve in the field of automatic control. The control accuracy and stability of the DC motor depend on the parameters of the control system, so it is of great significance to design the controller and optimize the control parameters. This paper presents a Fuzzy PID control framework, which successfully works on the performance of DC motor. The controller is based on the basic rules of fuzzy control theory and uses MATLAB simulation to realize its function. To look at the control precision of Fuzzy PID and the dependability of motor activity, Fuzzy PID algorithm and PID algorithm are simulated in MATLAB programming, separately. The simulation data show that the control effect of Fuzzy PID controllor is better than PID controllor in the DC motor control system, so Fuzzy PID controllor is worth popularizing.
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Shan, Yihe, Lurui Xia, and Sen Li. "Design and Simulation of Satellite Attitude Control Algorithm Based on PID." Journal of Physics: Conference Series 2355, no. 1 (October 1, 2022): 012035. http://dx.doi.org/10.1088/1742-6596/2355/1/012035.
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Abstract According to the tasks of mapping and reconnaissance, the satellite needs to adjust its attitude and keep its attitude stable when it is in orbit. Firstly, the kinematic model and dynamic model of satellite attitude are established.Then the digital simulation of satellite attitude control system is built based on the mathematical model of satellite attitude, in which the control modules use PID controller and fuzzy PID controller respectively. Finally, the steady-state performance and dynamic performance of the two controllers are compared and analyzed.The experimental results show that both fuzzy PID control and fuzzy PID control can be used as effective means of attitude control.Both of them have high precision, fast speed and good steady-state performance to achieve the desired attitude. Fuzzy PID control also has the ability of self-tuning controller parameters, which makes the control process smoother.
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Chen, Chunchao, Jinsong Li, Jun Luo, Shaorong Xie, and Hengyu Li. "Seeker optimization algorithm for optimal control of manipulator." Industrial Robot: An International Journal 43, no. 6 (October 17, 2016): 677–86. http://dx.doi.org/10.1108/ir-12-2015-0225.
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Purpose This paper aims to improve the adaptability and control performance of the controller, a proposed seeker optimization algorithm (SOA) is introduced to optimize the controller parameters of a robot manipulator. Design/methodology/approach In this paper, a traditional proportional integral derivative (PID) controller and a fuzzy logic controller are integrated to form a fuzzy PID (FPID) controller. The SOA, as a novel algorithm, is used for optimizing the controller parameters offline. There is a performance comparison in terms of FPID optimization about the SOA, the genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The DC motor model and the experimental platform are used to test the performance of the optimized controller. Findings Compared with GA, PSO and ACO, this novel optimization algorithm can enhance the control accuracy of the system. The optimized parameters ensure a system with faster response speed and better robustness. Originality/value A simplified FPID controller structure is constructed and a novel SOA method for FPID controller is presented. In this paper, the SOA is applied on the controller of 5-DOF manipulator, and the validation of controllers is tested by experiments.
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Hadi, M. Sukri, Intan Z. Mat Darus, M. Osman Tokhi, and Mohd Fairus Jamid. "Active vibration control of a horizontal flexible plate structure using intelligent proportional–integral–derivative controller tuned by fuzzy logic and artificial bee colony algorithm." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (May 27, 2019): 1159–71. http://dx.doi.org/10.1177/1461348419852454.
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This paper presents the development of an intelligent controller for vibration suppression of a horizontal flexible plate structure using hybrid Fuzzy–proportional–integral–derivative controller tuned by Ziegler–Nichols tuning rules and intelligent proportional–integral–derivative controller tuned by artificial bee colony algorithm. Active vibration control technique was implemented during the development of the controllers. The vibration data obtained through experimental rig was used to model the system using system identification technique based on auto-regressive with exogenous input model. Next, the developed model was used in the development of an active vibration control for vibration suppression of the horizontal flexible plate system using proportional–integral–derivative controller. Two types of controllers were proposed in this paper which are the hybrid Fuzzy–proportional–integral–derivative controller and intelligent proportional–integral–derivative controller tuned by artificial bee colony algorithm. The performances of the developed controllers were assessed and validated. Proportional–integral–derivative–artificial bee colony controller achieved the highest attenuation for first mode of vibration with 47.54 dB attenuation as compared to Fuzzy–proportional–integral–derivative controller with 32.04 dB attenuation. The experimental work was then conducted for the best controller to confirm the result achieved in the simulation work.
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Salas, Francisco G., Jorge Orrante-Sakanassi, Raymundo Juarez-del-Toro, and Ricardo P. Parada. "A stable proportional–proportional integral tracking controller with self-organizing fuzzy-tuned gains for parallel robots." International Journal of Advanced Robotic Systems 16, no. 1 (January 1, 2019): 172988141881995. http://dx.doi.org/10.1177/1729881418819956.
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Parallel robots are nowadays used in many high-precision tasks. The dynamics of parallel robots is naturally more complex than the dynamics of serial robots, due to their kinematic structure composed by closed chains. In addition, their current high-precision applications demand the innovation of more effective and robust motion controllers. This has motivated researchers to propose novel and more robust controllers that can perform the motion control tasks of these manipulators. In this article, a two-loop proportional–proportional integral controller for trajectory tracking control of parallel robots is proposed. In the proposed scheme, the gains of the proportional integral control loop are constant, while the gains of the proportional control loop are online tuned by a novel self-organizing fuzzy algorithm. This algorithm generates a performance index of the overall controller based on the past and the current tracking error. Such a performance index is then used to modify some parameters of fuzzy membership functions, which are part of a fuzzy inference engine. This fuzzy engine receives, in turn, the tracking error as input and produces an increment (positive or negative) to the current gain. The stability analysis of the closed-loop system of the proposed controller applied to the model of a parallel manipulator is carried on, which results in the uniform ultimate boundedness of the solutions of the closed-loop system. Moreover, the stability analysis developed for proportional–proportional integral variable gains schemes is valid not only when using a self-organizing fuzzy algorithm for gain-tuning but also with other gain-tuning algorithms, only providing that the produced gains meet the criterion for boundedness of the solutions. Furthermore, the superior performance of the proposed controller is validated by numerical simulations of its application to the model of a planar three-degree-of-freedom parallel robot. The results of numerical simulations of a proportional integral derivative controller and a fuzzy-tuned proportional derivative controller applied to the model of the robot are also obtained for comparison purposes.
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Song, Rong Rong. "Fuzzy Comprehensive Evaluation for Two Kinds of Suspension Controllers." Applied Mechanics and Materials 865 (June 2017): 561–64. http://dx.doi.org/10.4028/www.scientific.net/amm.865.561.
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In order to improve the nonlinear and uncertain characteristics of the suspension system, using the differential geometry, the suspension system is transformed into two linear subsystems. The state feedback controller and the proportional integral derivative (PID) controller based on the genetic algorithm are designed, and the fuzzy comprehensive evaluation method based on the analytic hierarchy process is modified, which can evaluate the suspension performance of the controllers. The evaluation results show that the proportional integral derivative controller with the genetic algorithm is better than the state feedback controller.
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Gülbahçe, Erdi, and Mehmet Çelik. "Fuzzy logic aided PPF controller design to active vibration control of a flexible beam." Journal of Structural Engineering & Applied Mechanics 4, no. 3 (September 30, 2021): 184–95. http://dx.doi.org/10.31462/jseam.2021.03184195.
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This paper presents a fuzzy-logic-based observer and a positive position feedback controller to reduce a standard beam's free vibrations using a piezoelectric actuator. It is aimed that fuzzy-logic-based observer is used as feed-through and improves the overall performance of the PPF controller. For this aim, the cantilever beam and a piezoelectric patch are initially numerically modeled using the finite element method considering the close loop control algorithm. The displacement and strain responses results are compared with the experimental model. Then, two controllers are applied to the designed system: positive position feedback (PPF) and fuzzy-logic-based positive position feedback (FLBPPF). The uncontrolled and controlled system responses are investigated and compared in terms of the linear strain and tip displacement results. Using the FLBPPF controller, the settling times of controlled systems are decreased by about 20.7% and 41.6% regarding the linear strain and tip displacement response compared to the PPF controller.