Fuzzy Adaptive PID Control Research Articles

Adaptive fuzzy control method of hydrogen fuel cell gas supply system

This paper takes the hydrogen fuel cell gas supply system as the research object. Firstly, it analyzes the working principle of the hydrogen fuel cell and the control requirements of the gas supply system, then uses the fuzzy adaptive PID control algorithm to regulate the gas pressure of the system. Finally, Matlab/Simulink software simulation platform is used for simulation verification, and the control effects of conventional PID control and fuzzy adaptive PID control are compared. The results show that the gas supply system based on fuzzy adaptive PID control can effectively regulate the gas pressure value and has a good control effect.

Torsional Vibration Suppression of Electric Vehicle Power Transmission System Based on Parameter Optimization and Fuzzy Control

Background: The torsional vibration of the power transmission system has heavy effect on the ride comfort and safety of the vehicle, which has attracted plenty of research. Therefore, aiming at the torsional vibration problem of the electric vehicle power transmission system, enough study on how to suppress the torsional vibration is clearly of great benefit. Objective: The goal of the work is to explore the new method to suppress the torsional vibration. The main contribution is that the parameter of the power transmission system is optimized and the adaptive fuzzy PID controller is proposed to be utilized and optimized to suppress the torsional vibration. Methods: An optimization objective function including the angular acceleration of the motor shaft, the decelerator and differential assembly and the half shaft is established as the output of the genetic algorithm fitness value. The error combination of the square root of the angular acceleration of the motor shaft, the decelerator and differential assembly and the half shaft is adopted as the input of the fuzzy inference controller and the PID controller, which can significantly simplify the fuzzy rule and the structure of the controllers. Results: The proposed method significantly reduces the torque amplitude of the motor shaft, the decelerator and differential assembly, the transmission half shaft, and the overall vibration amplitude. The maximum reduced vibration amplitude change is up to 41.22% and 87.04% respectively after parameter optimization and fuzzy PID control in the example. Conclusion: The comprehensive utilization of parameter optimization and adaptive fuzzy PID control can successfully suppress the torsional vibration phenomenon of the power transmission system, and the torsional amplitude of the angular velocity and angular acceleration decreases significantly. Therefore, the vehicle noise can be reduced and the stability can be improved. The study lays a foundation for solving the torsional vibration problem and the mechanical optimal design of the vehicle transmission system.

Design and control strategy of 3-prismatic-revolute-spherical ankle rehabilitation robot

ABSTRACT Aiming at the position and pose control requirement of the ankle rehabilitation robot, two schemes of 3-PRS (Prismatic-Revolute-Spherical) parallel ankle rehabilitation robots are proposed, and the analysis and simulation of the control strategy are carried out. Two schemes of 3-PRS ankle rehabilitation robots are statically analysed by the finite element method. Through the stress comparison of the two schemes, the stable one is determined. Three control strategies including the traditional PID control, PID control based on the genetic algorithm and adaptive fuzzy PID control are proposed. A multidisciplinary co-simulation platform is established by the combination of ADAMS, SOLIDWORKS and MATLAB, and the three control strategies are verified. The best control strategy for the rehabilitation robots is determined by comparing control effects. This study lays a solid foundation for research on the control of the rehabilitation robots.

Design of Force-to-Rebalanced System With Adaptive Fuzzy-PID Controller for N = 3 MEMS Disk Gyroscope

In this paper, an adaptive fuzzy-PID controller is proposed to make the closed-loop drive system of a MEMS disk gyroscope with a high Q respond faster without overshoot and achieve high stability accuracy. The principle of the MEMS disk gyroscope operating in force-to-rebalanced (FTR) mode is studied. An adaptive fuzzy-PID controller is designed and implemented. The fuzzy controller adjusts the parameters of the PID controller online according to the input error and error change rate. The response time of closed-loop drive of the gyroscope is reduced from 1.09 s to 0.54 s, and the overshoot is reduced from 20% to 0.004%, with no degradation in angle random walk (ARW) and bias instability (BI). The FTR control system is implemented on a field programmable gate array (FPGA) platform. The performance of the $n=3$ MEMS disk gyroscope with natural frequency of 9,536 Hz and a quality factor of ~20000 using the novel FTR control system revealed an ARW of 0.1667 deg/ $\surd $ hr and an in-run BI of 1.177 deg/hr.

An Adaptive Control for the Path Tracking of an Active Leg Prosthesis

This paper presents an adaptive fuzzy-PID control strategy applied to an active lower limb prosthesis for trajectories tracking in normal walking, stairs climbing, and stairs descent. Trying to imitate a natural human limb, the prosthesis design challenges rehabilitating amputees to resume normal activities. A dynamic model of an ankle-knee active prosthesis is developed without ground reaction in a first case and introduces ground effect in a second one to ameliorate prothesis performances. The obtained models are used to synthesize a control strategy based on TS fuzzy concepts and PID control to reproduce human lower limb behavior in a normal gait and climb and descent of stairs. The RSME errors are calculated to evaluate and compare the various results performances and eventually show the capacity of the proposed control with ground reaction impact on trajectory tracking. The RMSE values obtained for the four outputs of the fuzzy controller are very small for the different modes of locomotion. Moreover, they become weaker when the ground reaction forces are added to the model to show the role of these forces for the body equilibrium maintaining during the gait cycle. The developed approach ensured good trajectories tracking compared to a healthy leg even in presence of disturbances.

Adaptive Type-2 Fuzzy PID LFC for an Interconnected Power System Considering Input Time-Delay

This paper addresses the designing of an adaptive type-2 fuzzy PID (T2FPID) controller for load frequency control (LFC) of an interconnected nonlinear power system considering input time-delays. The input delay, which can commonly be raised by failure in the communication links and actuator operating lag may lead to control system instability. In the proposed adaptive T2FPID controller, output scaling factors have been chosen judiciously (according to a stable tuning algorithm) and thus improves the stability and performance of the controlled system. To perform this, the stability of the proposed method has been examined using the Lyapunov–Krasovskii (L-K) theorem, and the adaptation laws of the output scaling factors are derived. The proposed adaptation laws for the output scaling factors are simple and need a little knowledge about the structure of the power system. To demonstrate the efficiency of the proposed adaptive T2FPID controller, a comparison to the non-adaptive T2FPID and type-1 FPID (T1FPID) controllers is given for a two-area interconnected nonlinear power system with input time-delays. The obtained simulation results prove that the performance of the proposed T2FPID controller is superior compared to the other controllers in the presence of time-varying input delays, nonlinearities, and uncertainties.

Fuzzy Adaptive PID Control of Pneumatic Position Servo System Using High-speed On/Off Valves

Fuzzy Adaptive PID Control of Pneumatic Position Servo System Using High-speed On/Off Valves

Adaptive type-2 fuzzy PID controller for LFC in AC microgrid

An AC microgrid (MG) system links the distributed generation (DG) based on renewable energy resources and local electrical loads in modern power systems. Intermittent characteristics of wind and solar resources influence not only the availability of energy but also the stability of the entire AC microgrid. This matter as well makes a challenge on the load frequency control (LFC) problem in an islanded MG system, and consequently, the fixed structure controllers are unable to provide a proper performance over a wide range of operating conditions. Therefore, it is required to design a dedicated controller for LFC to ensure the capability of maintaining a generation–load balance by managing the intermittent resources. In this paper, an adaptive type-2 fuzzy PID (T2FPID) controller is suggested for LFC in an AC MG system to enforce the frequency deviation to zero by supplying the load demands. The designed T2FPID controller is a nonlinear controller that can handle the system nonlinearities and uncertainties in the MG system in a better way. Using a Lyapunov function, the stability of the system is evaluated and parameter tuning laws for the T2FPID controller are derived using this function. It has been illustrated that the adaptive parameters are bounded and the frequency deviation reaches to zero properly. The proposed adaptive T2FPID controller has been applied for LFC of a MG system, and the simulation results have been compared with those obtained by type-1 fuzzy (T1F) PI and nonadaptive fuzzy PID controllers, which is the latest research in the problem in hand. The simulation results indicate the efficiency of the proposed adaptive controllers in dealing with intermittent resources and changing in the power system parameters.

Mitigation of harmonics and inter-harmonics with LVRT and HVRT enhancement in grid-connected wind energy systems using genetic algorithm- optimized PWM and fuzzy adaptive PID control

The growing installed wind capacity over the last decade has led many energy regulators to define specific grid codes for wind energy generation systems connecting to the electricity grid. These requirements impose strict laws regarding the Low Voltage Ride Though (LVRT) and High Voltage Ride Though (HVRT) capabilities of wind turbines during voltage disturbances. The main aim of this paper is to propose LVRT and HVRT strategies that allow wind systems to remain connected during severe grid voltage disturbances. Power quality issues associated with harmonics and inter-harmonics are also discussed and a control scheme for the grid-side converter is proposed to make the Wind Energy Conversion System insensitive to external disturbances and parametric variations. The Selective Harmonic Elimination Pulse Width Modulation technique based on Genetic Algorithm optimization is employed to overcome over-modulation problems, reduce the amplitudes of harmonics, and thus reduce the Total Harmonic Distortion in the current and voltage waveforms. Furthermore, to compensate for the fluctuations of the wind speed due to turbulence at the blades of the turbine, a fuzzy Proportional-Integral-Derivative controller with adaptive gains is proposed to control the converter on the generator side.

Optimal Design of Excitation Systems of Synchronous Condensers for HVDC Systems in Power Grid Environment Based on Variable Universe Fuzzy Adaptive PID Controller

In a gesture to optimize the reactive power properties of synchronous condensers and improve capabilities of condensers to support the voltages of AC systems, the outer loop control of the reactive power of condensers and the outer loop control of the voltages of AC systems are introduced into the conventional design of the main excitation systems of condensers in high voltage direct current (HVDC) systems in this study. Moreover, variable universe fuzzy adaptive PID controller is proposed to serve excitation systems of condensers to improve the response ability of condensers in power grid faults and the recovery speed of condensers after power grid faults, and to reduce overvoltage of power grids generated by condensers in the process of fault recovery. To verify its experimental validation, the model of ±100 kV HVDC system containing a condenser is set up in MATLAB/Simulink. The verified results reveal that the optimal design strategy of excitation systems of synchronous condensers in the proposal can ensure rapid regulation of the voltages of AC systems arising from condensers, and reduce overcompensation of the voltages and reactive power of AC systems arising from condensers when faults recover.

A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots

A combined backstepping and adaptive fuzzy PID approach for a nonholonomic autonomous mobile robot to follow the desired path is proposed in this paper. Two adaptive fuzzy PID controllers are adopted at the dynamic control level for velocity tracking and steering control of the robot. The fuzzy PID controller consists of a PID controller which is designed by a trial-and-error approach, optimized using the cross-entropy method, and a fuzzy controller based on relational models with two inputs and three outputs. Adaptive adjustment of the PID controllers is implemented by means of the fuzzy controllers. The pose deviations of the robot when trajectory tracking will be eliminated by the backstepping control technique at the kinematic level using a kinematic model. The simulation validation results demonstrate that the proposed control system can offer good performances for the robot in terms of small distance error, rapid response, high stability, and trajectory tracking more accuracy.

Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

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.

Temperature and water level control system in water thermal mixing process using adaptive fuzzy PID controller

Thermal Mixing Process, which is important in various industries, is a multiple-input multiple-output process (MIMO). It works by regulating hot-water and cold-water flows to control the temperature and level of the mixture. The Adaptive Fuzzy PID Control (AFPIDC) algorithm is a combination of two types of controller, has a simple PID basis with added Fuzzy aspects to speed up control. The AFPIDC algorithm is applied to the simulation of water thermal mixing process and is done with MATLAB/SIMULINK program. The fuzzy algorithm uses two inputs (errors & changes of errors) and has three outputs (changes of PID constants). The system is tested by simulating setpoint changes and adding disturbance. The testing result shows that AFPIDC controllers performs better than PID in controlling temperature and level. In temperature control, the PID settling time is 830 seconds, AFPIDC is 328 seconds and the PID overshoot is 6,3% and AFPIDC is 0%. In level control, the settling time of PID is 3221 seconds, while AFPIDC is 235 seconds, with PID overshoot is 10,5%, while AFPIDC 0%. From testing the system with leakage disturbance, the temperature controller needs time to regain stability at PID 780 seconds, AFPIDC 250 seconds. Meanwhile the level controlling stabilizes at PID 4510 seconds, and AFPIDC at 225 seconds.

Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system

A Simplified Grey Wolf Optimizer (SGWO) is suggested for resolving optimization tasks. The simplification in the original Grey Wolf Optimizer (GWO) method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process. The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal, multimodal, and fixed dimension test functions. The results are also contrasted to the Gravitational Search Algorithm, the Particle Swarm Optimization, and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique. Practical application in a Distributed Power Generation System (DPGS) with energy storage is then considered by designing an Adaptive Fuzzy PID (AFPID) controller using the suggested SGWO method for frequency control. The DPGS contains renewable generation such as photovoltaic, wind, and storage elements such as battery and flywheel, in addition to plug-in electric vehicles. It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task. It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller. A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance. Finally, the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.

A Fuzzy Adaptive PID Control Algorithm with Improved Quantification Factor

The phase-shifting full bridge digital power supply based on Fuzzy PID control is difficult to realize the full mapping from the fuzzy universe to the actual output domain, resulting in the loss of fuzzy control quantity, resulting in the dynamic performance problems of large amplitude and long time output fluctuation. In this paper, a fuzzy adaptive PID control algorithm with improved quantification factor is proposed. The online adjustment function of quantification factor in fuzzy universe is introduced in the quantification link. The function can adjust the fuzzy quantification factor in real time according to the current sampling error and error change rate of the system. In order to verify the effectiveness of the improved algorithm, the traditional PID control, the traditional fuzzy PID control and the quantification factor improved fuzzy PID control are applied to the phase-shifting full bridge small signal disturbance model respectively. The results show that the fuzzy adaptive PID control algorithm with the quantification factor online adjustment function has higher dynamic performance.

Simplified grey wolf optimisation algorithm tuned adaptive fuzzy PID controller for frequency regulation of interconnected power systems

ABSTRACT In this study, an adaptive fuzzy-based PID (AFPID) controller has been suggested for frequency regulation of power systems where the parameters of the proposed controller are optimised by a simplified grey wolf optimisation (SGWO) method. The simplification in original grey wolf optimiser (GWO) method is introduced by ignoring the worst category wolves and giving priority to better wolves throughout the search process. The advantage of projected SGWO over GWO in terms of getting a better solution with less execution time is demonstrated by taking unimodal, multimodal and fixed dimension benchmark functions. Then the proposed SGWO technique is engaged to tune AFPID controller for frequency regulation of four power systems i.e. two area non-reheat, two area with reheat turbine, two area reheat steam turbine with nonlinearity and two area system with a PV and thermal system to prove the controller and algorithm’s effectiveness in regulating frequency change.

Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle

A considerable no. of intermittent renewable sources such as PV generation and wind energy when integrated to the conventional grid technology causes serious issues in the power systems like frequency instability. So a more balancing controller is desired for a stable and reliable operation of the power system. Bidirectional power control of the EV aggregator is making itself a wise choice for distributed energy storage to scale down the frequency and power fluctuation. In this work, an intelligent load frequency controller using a fractional-order adaptive fuzzy PID controller with filter (FOAFPIDF) for hybrid power system with electric vehicle (EV) based on modified salp swarm algorithm (MSSA) technique is proposed. The effectiveness of MSSA technique is compared with original salp swarm algorithm as well as moth flame optimization , grey wolf optimization , particle swarm optimization and sine cosine algorithm techniques for benchmark test functions using statistical analysis. The effectiveness of the suggested load frequency control strategy by the use of electric vehicle as well as with other energy storing elements such as the superconducting magnetic energy storage component, flywheel energy storage system and ultra-capacitor along with their inherent rate constraint nonlinearity is validated by numerical simulations conducted on the studied test system. It is demonstrated that the proposed controller provides a better control action to suppress the frequency fluctuations as compared to PID controller. The robustness of the controller is also investigated against variation of system parameters and random load changes.

Discrete fuzzy adaptive PID control algorithm for automotive anti-lock braking system

Due to the low slip rate and poor braking performance of automobile anti-lock braking system, a discrete fuzzy adaptive PID control algorithm for automobile anti-lock braking system is designed in this paper. Based on the theory of discrete fuzzy adaptive PID control algorithm, the braking force of a vehicle is analyzed and the anti-lock control target is set. The automobile adhesion coefficient and wheel slip rate are calculated as the input and output of the PID control algorithm to complete the control of the anti-lock brake system of the network automobile. Experiments show that the sliding rate of the discrete fuzzy adaptive PID control algorithm of the designed anti-lock system is close to 20%, which proves that the design method has good braking effect.

Wavelet Neural Network Control System Based on Fuzzy PID

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.

MATLAB Simulation on Fuzzy PID Control to Feed Servo System of CNC Machine Tool

A CNC machine tool's feed servo mechanism is a dynamic electromechanical coupling system. It is difficult to correctly model the control mechanism due to the characteristics of time-varying parameters, load disturbances, and motor non-linearity. Intend to provide an approach where a fuzzy adaptive PID controller is designed by combining a PID controller with a fuzzy controller based on the mathematical model of the CNC machine tool fed servo system. Then intend to apply this controller to control the feed servo system to gain better control performance. The simulation results for the same are expected to show that this method not only has no static distortion, but also has a fast response with little overshoot, accounting for the high stability and accuracy of the fuzzy adaptive PID controller.