Implementation of adaptive fuzzy controller on the variable speed wind turbines in comparison with conventional methods

The conventional PID pitch controller was usually used in direct-drive permanent magnet wind turbine in order to maintain the output power unchanged above the rated wind speed. However, due to the complex structure of wind turbine as well as high nonlinearity of wind speed, the effect of PID control method is often less than ideal. A new pitch control method combined with fuzzy adaptive PID and fuzzy feed forward controller is proposed in this paper. The fuzzy adaptive PID controller is able to ensure the unit having a better control result than PID controller at various wind speeds. The fuzzy feed forward controller has improved the responsiveness of pitch control system. With the simulation of a 1.5MW wind turbine, the responsiveness and stability of wind turbine can be effectively proved by the new pitch control method above the rated wind speed, the system’s overshoot is also reduced, which satisfies the demand of power control.

To achieve a better robustness and get a static response of a certain target missiles in longitudinal control, a mathematical model of target missiles is established. The uncontrolled trajectory, the predetermined trajectory and the requirements of the predetermined trajectory are analyzed next. Then the trajectory instruction angle is designed to ensure the stable flight of the target missiles system. To solve the problem that the PID controller cannot adjust parameters online and the output is easy to be disturbed by external noise, the fuzzy adaptive PID control is designed. It enhanced the robustness of the target missiles by fuzzy control, and also has a good response by using PID control. The simulation experiments show the fuzzy adaptive control system can track the predetermined trajectory rapidly and smoothly, and the static error and overshoot were significantly reduced. When the system are affected by noise, the fuzzy adaptive PID control can achieve a good robustness than traditional PID controller.

Solving the path-tracking problem of vehicles is a key part of realizing intelligent transportation. The four-wheel- independent-steering (4WIS) electric vehicle (EV) with steer-by-wire (SBW) has the advantages of good controllability, low speed maneuverability and high speed stability, which is beneficial to improving the path tracking performance. In this paper, a novel 4WIS electric vehicle is proposed and the path tracking control research was carried out on it by using the adaptive fuzzy PID algorithm. Firstly, the four-wheel steering strategy was analyzed, and the geometric relationship between the front and rear steering angle when the vehicle's centroid angle is zero was derived. Furthermore, based on the principle of adaptive fuzzy PID control and the single-step heading prediction algorithm, a path-tracking controller was designed for the self-built 4WIS EV. A Simulink/Carsim co-simulation platform was built, on which the simulation was carried out at different condition, and the simulation results show that the designed adaptive fuzzy PID controller has good path-tracking performance and strong robustness. Finally, the serpentine trajectory and the double lane change (DLC) trajectory are taken as the reference trajectory, and the reliability and robustness of the designed adaptive fuzzy PID control algorithm were verified on the open road. The results show that the adaptive fuzzy PID control algorithm has good tracking performance and is far superior to pure PID control.

Based on the PID control and adaptive fuzzy PID control, the paper establishes semi-active suspension model of engineering vehicle and compares vibration control and suspension performance among the semi-active suspensions, taking engineering vehicle as the study object. Through the further simulation research, from the comparative analysis performance indexes of riding comfort and handling safety, we can reach the conclusion that the control effect of adaptive fuzzy PID control improves by different degrees than the passive suspension and PID control suspension. The results show that adaptive fuzzy PID control suspension can reduce the vehicle vibration commendably during operation

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.

The control strategy for wind turbine is very important an equipage that diverts the kinetic energy of the wind to electrical power. The rotation speed control of wind turbine is necessary for highest degree energy capture and pitch angle advanced control planning depending on traditional PID controller and fuzzy logic adaptive PID controller. Input variables of fuzzy logic controller (FLC) are selected to give smooth control of pitch angle which in turns will adjust the speed of wind turbine at reference level. A very small adaptation in pitch angle has influence on the ancestry of obtainable energy, torque and output power of the grid. A modeling of self-excitation induction generator (SEIG) 1KW wind turbine is achieved by Matlab/Simulink package and all modeling equations are studied. The effect of pitch angle on speed of the wind turbine is studied and a comparison between traditional PID controller and fuzzy logic adaptive PID controller was made.

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.

In general, an adaptive fuzzy PID controller (AF-PIDC) means that the PID parameters are adjusted by fuzzy rules in real-time. In this paper, the controller is functionally divided into three operating units which are the proportional operating unit, the integral operating unit, and the derivative operating unit. The inputs of the controller are composes of two kinds of signals. The main input signal is the weighted system error and the weights are the traditional PID parameters with fixed gains tuned in a traditional way. The auxiliary input signal is produced by a fuzzy logic controller. Since the output of the fuzzy logic controller is changeable, the gain of the controller is adjusted implicitly and adaptively. The simulation results show the validity of the proposed approach.

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.

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.

Based on the ant colony system (ACS) algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-I intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.

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.

So far, most of the proton exchange membrane fuel cell (PEMFC) models are difficult to apply in the control system design. In this paper, a dynamic control model of PEMFC system is set up. Based on this model, a self-adaptive fuzzy PID controller and a PID controller are respectively used to control the output voltage. The simulation results show that, this adaptive fuzzy PID controller can control the output characteristics more effectively than the PID controller, which can ensure PEMFC operate stably.

Due to some nonlinear factors and time delay in electronic throttle, the opening of electronic throttle is difficult to control. For overcoming these difficulties, in this paper a fuzzy adaptive PID controller is designed for an electronic throttle control system. The model of electronic throttle control system is built based on Matlab/Simulink and realized with a hardware-in-the-loop simulation system, which is based on MACS565. Simulation research and experiment results show that, compared with PID control system, the fuzzy adaptive PID control system, has some advantages. The all performance index with the fuzzy adaptive PID control is better than that of the PID control.