Application of Optimal Fuzzy PID Controller Design: PI Control for Nonlinear Induction Motor

The purpose of the study is to develop an adaptive electropneumatic automatic system based on the classical PID controller using modern technology for controlling like fuzzy controlling. The need of an adaptive setting occurred because of the fact that the classic controller has high sensitivity towards interferences with the main signal and the need for the parameters to be regulated often and be done by hand. Another reason is the inability to provide optimal parameters for the controller for nonlinear processes and changes of the parameters in time. In this article the work of electropneumatic system with linear actuator, is examined. A mathematical model describing the dynamics of the processes, is developed. An adaptive fuzzy PID controller with variable coefficients is synthesized. The system is studied in the environment of Matlab Simulink. The transient processes in the system are compared to processes where the classical PID controller. The results are presented in graphical form.

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

The paper realizes a design of digital fuzzy adaptive tuning PID electronic governor. It is based on VHDL description and implemented on FPGA. The paper proposes to detect the speed with duty cycle calculation and use fuzzy-control rules to amend the PID parameters on line to achieve the fuzzy adaptive tuning PID control. On this basis, the paper introduces the implementation of the system in detail, including speed detection, fuzzy logic inference, and fuzzy adaptive tuning PID algorithm. The experiment proves that digital wide-range electronic speed governor implemented on control chip FPGA can be used widely and fuzzy adaptive tuning PID control algorithm can improve the dynamic response of the system, which makes the electronic speed governor under good control in a wider work scope.

ABSTRACTAn autonomous power generation system (APGS) contains units such as diesel energy generator, solar photovoltaic units, wind turbine generator and fuel cells along with energy-storing units such as the flywheel energy storage system and battery energy storage system. The components either run at lower/higher power output or may turn on/off at different instants of their operation. Due to this, the conventional controllers will not provide desired performance under varied load conditions. This paper proposes an adaptive fuzzy logic PID (AFPID) controller for load frequency control. In order to achieve an improved performance, a modified whale optimization algorithm (mWOA) was also proposed in this paper for tuning of the AFPID parameters. The proposed algorithm was first evaluated using standard test functions and compared with other recent algorithms to authenticate the competence of algorithm. The proposed mWOA algorithm outperforms PSO, GSA, DE and FEP algorithms in five out of seven unimodal test functions and four out of six multimodal test functions. The effectiveness of the AFPID compared with the conventional PID and the proposed AFPID provides better performance. Reduction of 39.13% in error criteria (objective function) compared with WOA-PID controller. The proposed approach was also compared with some recently proposed frequency control approaches in a widely used two-area test system.

This paper presents a method for improving the performance of a vehicle suspension system using an adaptive fuzzy dual PID controller optimized with a genetic algorithm. The fuzzy dual PID controller utilizes fuzzy logic to adapt to changing conditions and improve control, while the genetic algorithm optimizes the controller parameters to further enhance performance. The study uses velocity and position PID controllers because velocity PID controls acceleration well and position PID controls position well, and the incorporation of an adaptive fuzzy combination of two controllers ensures optimal performance of the suspension system under all operating conditions. To avoid the issue of suspension distance narrowing and to prevent instability in the controller, the low‐pass filtered displacement response of unsprung mass is utilized as the reference for the position PID controller. Quantitatively, according to the ISO‐8608 road entry for the goal function, the dual PID achieved a 53.35% improvement over the passive state, 6.57% better than dual PD, 33.06% over the Velocity PID, and 32.93% over the Position PID. These significant, quantifiable results confirm that the proposed adaptive fuzzy dual PID structure offers a robust and highly effective solution for advancing active vehicle suspension control technology.

Aiming at the network control system with time delay and packet loss, this paper designs a fuzzy adaptive PID controller to realize the adjustment of the network control system. Through the design of wavelet neural algorithm to predict the time delay, the fuzzy adaptive PID can realize the online adjustment of Kp, Ki and Kd, which overcomes the problem of constant input parameter adjustment of the traditional PID controller. Fuzzy control is realized by designing fuzzy rules, and finally through comparison The Matlab simulation renderings of traditional PID controller and fuzzy adaptive PID controller show that the robustness and accuracy of fuzzy adaptive PID controller are better.

Fuzzy-logic-based algorithms applied in the individual pitch control make the effect of the controlled system imperfect. Thus, an adjustable factor algorithm of adaptive fuzzy PID controller based on chaos theory was proposed. To achieve good control effects, the chaos algorithm was applied in designing the fuzzy PID control system to optimize the parameters of the membership functions of controller under the control of the fuzzy PID. Simulation based on matlab/simulink was made to analyze and to compare fuzzy PID individual pitch control system with and without the chaos algorithm respectively. Simulation results show that, the fuzzy PID individual pitch control system based on chaos algorithm was much more smooth and steady and response faster than the system without chaos algorithm within the rated wind speed.

Aiming at the microwave-vacuum wood drying controlled object with the characteristics of strong coupling, large delay, large inertia, nonlinear and time-variant, a control system based on fuzzy self-tuning PID control and conventional digital PID is proposed. The system combines the cascade control with the fuzzy self-tuning PID control, taking fully advantage of the cascade control, fuzzy control, self-tuning control and PID control, which makes the system possess not only better track ability but also stronger robust and anti-disturbance The design, analysis, and implementation stages are carried out entirely in a TMS320LF2407A digital signal processor-based real-time data acquisition control system and MATLAB/Simulink environment. The simulation result indicates that this system may enhance the entire microwave-vacuum wood drying system control precision and working efficiency, and it's an effective method to realize automatic control in the wood drying process. What's more, the control algorithm is simple and practical.

The research on active control of the pantograph has been intensively studied, however, the control results in practice are not satisfactory as expected, for the existence of time delays coming from the input and output measurement. This paper focuses on the problem of time delays in the designing of the control strategy, including the sensor signal input and the action of the actuator. A multibody dynamic pantograph model is established based on the relative coordinate method, combined with the rigid catenary established by the modal superposition method. Firstly, the performance of the traditional PID controller with signal time delay is studied, including the separation or combination of sensor signal input delay and actuator signal output delay. Then, a novel fuzzy PID controller with an improved rule library is proposed, which further improves the control performance of the PID controller. Finally, to deal with the time delay of sensor and actuator well, a dynamic predictive fuzzy adaptive PID (DMC-Fuzzy-PID) controller is proposed by combining dynamic matrix control (DMC) with the improved fuzzy PID controller, which may support the engineering application of active control of pantograph.KeywordsActive controlTime delayRigid catenaryMultibody dynamicsFuzzy PID controller

This paper presents a new approach using fuzzy compensator and PID controller to an experimental propeller setup which is called the twin rotor multi-input multi-output system (TRMS). Some previous works ignored the interactions between two axes and the controller being designed in horizontal or vertical direction separately. The goal of this study is to stabilize the TRMS in significant cross coupling conditions and to experiment with trajectory tracking. The fuzzy compensator and PID controller design is performed by a real-valued GA (RGA) with system performance index as fitness function. We apply the integral of time multiplied by the square error criterion to form a suitable fitness function in the RGA. Simulation results show that the proposed design can successfully adapt system nonlinearity and complex coupling conditions.

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.

A fuzzy fractional-order proportional integral derivative (FFOPID) control scheme is proposed. In the recent years, the application of fractional calculus has been applied in science and engineering fields and proved to be a better. However, the use of fuzzy with fractional order control strategy for real-time control has not been studied greatly. In this proposed work an intelligent fuzzy fractional-order PID controller is designed and its performance is studied with a novel pressure regulating system model. To emphasize the advantages of proposed control scheme, the controller performances are investigated under load disturbances and changes in set-point conditions. A comparative study is also made with fractional order proportional integral derivative (FOPID) and classical PID control schemes. From the simulation results, the proposed control scheme outperforms other types of control scheme.

The control performance of a fixed parameters PID controller in a dynamic system will be reduced. To solve this problem, an optimized nonlinear PID controller with fuzzy inference system is proposed. The first order plus dead time (FOPDT) is a high dynamic system. This paper introduces the development and implementation of the optimal fuzzy PID controller for the FOPDT system to improve the control performance. The control process of temperature is a FOPDT model, and the approximate mathematical model is constructed according to the analysis of the step response curve without close-loop control. The design of fuzzy PID controller and the implementation of the control software are carried out by using MATLAB\Simulink. The experimental results show that, compared with the traditional PID controller, the optimized fuzzy PID controller can greatly improve the control performance for the FOPDT system.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

A balancing control algorithm for a wheel-legged robot is designed for current logistics and distribution mobile robots. Since the wheel-legged robot is a nonlinear underactuated system, it is crucial to realize the balanced control and robustness of the wheel-legged robot in the absence of an accurate dynamics model. In this paper, an optimal control scheme for the wheel-legged robot based on the fusion control of variable-height adaptive fuzzy PID and conventional PID is designed. The balance of the wheel-legged robot is controlled by adaptive fuzzy PID control, the lifting and attitude changes of the two sides of the wheel-legs are controlled with high precision by traditional PID control, and the speed and steering control of the wheel-legged robot is not affected by the structure of the model, and is controlled by traditional PID control with linearization. The attitude of the robot is detected in real time using the BMI088 attitude sensor to realize the positional control. Experimental and simulation results show that the control algorithm of the wheel-legged robot designed in this paper is reliable, and the robot runs smoothly and robustly.