Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Acid-pickling plates and strips speed control system by microwave heating based on self-adaptive fuzzy PID algorithm

Double self-adaptive fuzzy PID algorithm-based control strategy was proposed to construct quasi-cascade control system to control the speed of the acid-pickling process of titanium plates and strips. It is very useful in overcoming non-linear dynamic behavior, uncertain and time-varying parameters, un-modeled dynamics, and couples between the automatic turbulence control (ATC) and the automatic acid temperature control (AATC) with varying parameters during the operation process. The quasi-cascade control system of inner and outer loop self-adaptive fuzzy PID controller was built, which could effectively control the pickling speed of plates and strips. The simulated results and real application indicate that the plates and strips acid pickling speed control system has good performances of adaptively tracking the parameter variations and anti-disturbances, which ensures the match of acid pickling temperature and turbulence of flowing with acid pickling speed, improving the surface quality of plates and strips acid pickling, and energy efficiency.

Study on Fuzzy Self-Adaptive PID Control System of Biomass Boiler Drum Water

The paper discusses the features of the Biomass Boiler drum water level. Conventional PID Control System can not reach a satisfaction result in nonlinearity and time different from Biomass Boiler Drum Water Control System. In this study, a kind of fuzzy self-adaptive PID controller is described and this controller is used in biomass boiler’s drum water level control system. Using the simulink tool of MATLAB simulation software to simulate the fuzzy adaptive PID and conventional PID control system, the result of the comparison shows that the fuzzy self-adaptive PID has the strong anti-jamming, flexibility and adaptability as well as the higher control precision in Biomass Boiler Drum Water.

The control of welding wire feed self-adaptive mechanism based on fuzzy PID

The welding wire feed mechanism is an important component of welding equipment, both reliability and stabilization are the premise that the welding quality can be ensured. The PID is currently adapted to control the welding wire feed mechanism, although the fuzzy PID has advantage of fast response and adaptation, the precision of fuzzy PID is lower. Accordingly, the fuzzy self-adaptive PID controller was proposed through changing fuzzy input variables and output variables based on variable universe, simple furwtion is adopted as scaling factor, the fuzzy PID controller parameters are adjusted to improve the precision and adjustment range. Simulation results show that control effects of fuzzy self-adaptive PID adopted by the welding wire feed mechanism have good adaptive ability and robustness based on variable universe, the welding experiments indicate that the welding quality met the requirements actually.

Self-adaptive fuzzy controller with formulary rule for servo control of discharge gap in Micro EDM

In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems above, a self-adaptive fuzzy controller with formulary rule （SAFCFR） is presented based on the dual feedbacks composed by gap electric signal and discharge-ratio statistics. To ensure the properties of self-optimizing and fast stabilization, the formulary rule was designed with a tuning factor. In addition, the fast-convergence algorithms were introduced to adjust control target center and output scale factor. In this way, the normal discharge ratio can tend to the highest value during micro-EDM process. Experimental results show that the proposed algorithms are effective in improving the servo-control performance. According to the drilling-micro-EDM experiments, the machining efficiency is improved by 20% through applying SAFCFR. Moreover, SAFCFR is a prompt way to optimize parameters of discharge-gap servo control.

基于Fuzzy-PID的棉花打顶升降装置设计与试验

为解决棉花打顶机作业过程中由于仿形时与刀盘移动滞后、定位精准度低,导致的棉花漏打、过打及伤苗的问题,基于模糊控制技术对棉花打顶装置高度控制系统进行研究。以3MDZF-6棉花打顶机升降系统为分析对象,确定了刀盘在升降过程中的定位精度要求。以液压阀-液压缸为对象,在AMESim中建立打顶升降装置的控制模型,并将其导入到MatLab中,设计相应的模糊规则和模糊控制器,对其进行仿真分析,从超调量和响应时间两方面验证了PID和模糊PID控制方法的可行性。仿真结果表明:无外界干扰的情况下,PID参数分别为100800、500、30时,输入信号达到稳态的响应时间为0.55s;模糊PID控制下输入信号达到稳态的响应时间为0.31s。经过对比分析固定参数PID控制和模糊PID控制在不同扰动下的控制效果,发现模糊PID控制系统的超调量为0.84%,输入信号达到稳态所需的时间为0.39s,小于固定参数PID的0.64s。该方法实现了打顶升降装置的位移控制,提高了刀盘的定位精度及控制系统的抗干扰性和鲁棒性,可为棉田环境下的棉花打顶升降装置的精确控制问题提供借鉴。

矿井带式输送机液压拉紧Fuzzy-PID控制技术研究

针对矿井传统带式输送机拉紧系统响应速度慢、调节能力差、拉紧控制时变性和非线性等问题,提出了一种矿井带式输送机液压拉紧系统Fuzzy-PID控制(基于模糊算法的PID控制)方法。首先,根据液压拉紧装置建立数学模型,其次通过Matlab内置的Simulink仿真库分别对Fuzzy-PID控制器和PID控制器的液压拉紧系统进行仿真,得出输送带拉紧张力启动响应阶段和张力突变的调节响应图,并做出对比分析。最后,通过试验测试来验证算法模型的有效性。仿真结果表明:矿井带式输送机液压拉紧Fuzzy-PID控制系统不仅在启动阶段还有张力突变过程中都具有更好的稳态性能、更快的响应速度。在拉紧装置启动响应阶段的张力超调量降低了13.5%、到达期望值的时间缩短了0.5 s。在拉紧装置张力突变即模拟拉紧和松带阶段,当张力增加时,Fuzzy-PID控制器的调节速度缩短了0.4 s,超调量下降了4%。当张力减少时,Fuzzy-PID控制器的调节速度缩短了0.3 s,超调量降低了2%。试验结果表明:采用Fuzzy-PID控制的效果更佳优异稳定,且损耗更小。对比于PID控制,Fuzzy-PID控制效果更为良好,平均时间缩短31%且总体趋于稳定。对于矿井带式输送机这种连续运输作业的设备,Fuzzy-PID控制技术为矿井带式输送带平稳运行提供了一定保障,不仅减少了电能浪费,也降低了维护保养带式输送机的保养成本。

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

This paper presents the design and performance analysis of Differential Evolution(DE)algorithm based Proportional-Integral-Derivative(PID)controller for temperature control of Continuous Stirred Tank Reactor(CSTR)plant in che-mical industries.The proposed work deals about the design of Differential Evolu-tion(DE)algorithm in order to improve the performance of CSTR.In this,the process is controlled by controlling the temperature of the liquid through manip-ulation of the coolantflow rate with the help of modified Model Reference Adap-tive Controller(MRAC).The transient response of temperature process is improved by using PID Controller,Differential Evolution Algorithm based PID and fuzzy based DE controller.Finally,the temperature response is compared with experimental results of CSTR.

An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.

Proportion Integration Differentiation(PID)Control Strategy of Belt Sander Based on Fuzzy Algorithm

Aiming at solving the problems of response lag and lack of precision and stability in constant grinding force control of industrial robot belts,a constant force control strategy combining fuzzy control and proportion integration differentiation(PID)was proposed by analyzing the signal transmission process and the dynamic characteristics of the grinding mechanism.The simulation results showed that compared with the classical PID control strategy,the system adjustment time was shortened by 98.7%,the overshoot was reduced by 5.1%,and the control error was 0.2%-0.5%when the system was stabilized.The optimized fuzzy control system had fast adjustment speeds,precise force control and stability.The experimental analysis of the surface morphology of the machined blade was carried out by the industrial robot abrasive grinding mechanism,and the correctness of the theoretical analysis and the effectiveness of the control strategy were verified.

Tuning of fuzzy PID controller for Smith predictor

An analytical tuning method was proposed for fuzzy PID controller used in Smith predictor in order to extend its application and improve its robustness. The fuzzy PID controller was expressed as a sliding mode control. Based on Lyapunov theory, Smith predictor was analyzed in time domain. The parameters of the fuzzy PID controller can be obtained using traditional linear control theory and sliding mode control theory. The simulation experiments were implemented. The simulation results show that the control performance, robustness and stability of the fuzzy PID controller are better than those of the PID controller in Smith predictor.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

A Novel Design of Fuzzy PID Controllers for Dual-Sensor Cardiac Pacemaker Systems

This work proposes to design a fuzzy proportional-integral derivative (FPID) controller for dual-sensor cardiac pacemaker systems, which can automatically control the heart rate to accurately track a desired preset profile. The combination of fuzzy logic and conventional PID control approaches is adopted for the controller design based on dual-sensors. This controller offers good adaptation of the heart rate to the physiological needs of the patient under different states (rest and walk). Through comparing with the conventional fuzzy control algorithm, FPID provides a more suitable control strategy to determine a pacing rate in order to achieve a closer match between actual heart rate and a desired profile. To assist the heartbeat recovery, the stimuli with adjustable pacing rate is generated by the pacemaker according to the FPID controller, such actual heart rate may track the preset heart rate faithfully. Simulation results confirm that this proposed control design is effective for heartbeat recovery and maintenance. This study will be helpful not only for the analysis and treatment of bradycardias but also for improving the performance of medical devices.

Fuzzy PID Controllers Using FPGA Technique for Real Time DC Motor Speed Control

The design of intelligent control systems has become an area of intense research interest. The development of an effective methodology for the design of such control systems undoubtedly requires the synthesis of many concepts from artificial intelligence. The most commonly used controller in the industry field is the proportional-plus-integral-plus-derivative (PID) controller. Fuzzy logic controller (FLC) provides an alternative to PID controller, especially when the available system models are inexact or unavailable. Also rapid advances in digital technologies have given designers the option of implementing controllers using Field Programmable Gate Array (FPGA) which depends on parallel programming. This method has many advantages over classical microprocessors. In this research, A model of the fuzzy PID control system is implemented in real time with a Xilinx FPGA (Spartan-3A, Xilinx Company, 2007). It is introduced to maintain a constant speed to when the load varies.,The model of a DC motor is considered as a second order system with load variation as a an example for complex model systems. For comparison purpose, two widely used controllers “PID and Fuzzy” have been implemented in the same FPGA card to examine the performance of the proposed system. These controllers have been tested using Matlab/Simulink program under speed and load variation conditions. The controllers were implemented to run the motor as real time application under speed and load variation conditions and showed the superiority of Fuzzy-PID.

Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-I intelligent leg

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.

Comparative Study of Conventional, Fuzzy Logic and Neural PID Speed Controllers with Torque Ripple Minimization for an Axial Magnetic Flux Switched Reluctance Motor

Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high ripple torque is obtained. In order to reduce this ripple, a control strategy with modified current shapes is proposed. A workbench consisting of a machine prototype and the control system based on a microcontroller was built. These controllers were: a conventional PID, a fuzzy logic PID and a neural PID type. From experimental results, the effective reduction of the torque ripple was confirmed and the performance of the controllers was compared.

Aircraft Electric Anti-skid Braking System Based on Fuzzy-PID Controller with Parameter Self-adjustment Feature

The principle of electric braking system is analyzed and an anti-skid braking system based on the slip rate control is proposed.The fuzzy-PID controller with parameter self-adjustment feature is designed for the anti-skid braking system.The dynamic model of aircraft ground braking is established in the simulation environment of MATLAB/SIMULINK,and simulation results of dry runway and wet runway are presented.The results show that the fuzzy-PID controller with parameter self-adjustment feature for the electric anti-skid braking system keeps working in the state of stability and the brake efficiencies are increased to 93%on dry runway and 82%on wet runway respectively.

PID-Based Bit Allocation Strategy for Asymmetric Stereoscopic Video Coding with Fuzzy Quality Controller

Asymmetric stereoscopic video coding can take advantage of binocular suppression in human vision by representing one of the two views in lower quality.This paper proposes a bit allocation strategy for asymmetric stereoscopic video coding.In order to improve the accuracy of bit allocation and rate control in the left view,a proportionalintegral-derivative controller is adopted.Meanwhile,to control the quality fluctuation between consecutive frames of the left view,a quality controller is adopted.Besides,a fuzzy controller is proposed to control the variation in quality between the left and right views by comparing the PSNR disparity of two views with a fixed threshold,which is used to quantize the binocular psycho-visual redundancy and adjust the quantization parameter (QP) of the right view correspondingly.The proposed algorithm has been implemented in H.264/AVC video codec,and the experimental results show its effectiveness in rate control while keeping a good quality for the left view,and fewer bits are allocated for the right view so that the overall bit rate is saved by 7.2% at most without the loss of subjective visual quality for stereoscopic video.