基于BP神经网络的Smith-Fuzzy-PID算法在阀门定位中的应用研究

为解决气动调节阀控制过程中出现的超调大、精度低等问题,本文采用BP神经网络整定出较优的PID(Proportional Integral Derivative)控制参数,对Smith预估控制器以及模糊控制器进行设计,实现了基于BP神经网络的Smith-Fuzzy-PID控制方法。搭建了实验平台,通过阶跃响应实验来对控制方法进行验证,验证结果表明,提出的方法调节过程无超调,调节时间仅为1.9 s,定位精度在±0.5%以内,有效提高了系统的稳定性,实现了气动调节阀的快速精准定位。

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.

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.

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.

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.

基于ISSA-Fuzzy-PID算法的无刷直流电机调速控制系统

为了使无刷直流电机(BLDCM)从复杂多变的非线性关系中找出最佳PID参数,提出了一种基于改进麻雀算法(ISSA)优化模糊(fuzzy)控制的调速系统。根据BLDCM工作原理及控制方法在MATLAB平台上搭建仿真模型并加入反向学习策略初始化麻雀种群,形成非线性时变控制系统,并将该优化系统与传统PID以及模糊PID(fuzzy-PID)控制系统进行对比分析。仿真结果表明,基于ISSA-Fuzzy-PID的调速控制系统有较快的动态响应,有效增强了BLDCM转速控制的精度和鲁棒性。

干涉式闭环光纤陀螺仪的PSO-PID控制优化方法

控制系统的设计会对响应速度快且应用范围较广的数字干涉式闭环光纤陀螺(ICFOG)动态性能产生影响。通过分析ICFOG的工作原理,推导出闭环离散控制系统,并利用粒子群优化算法(Particle Swarm Optimization,PSO)对传统的PID控制器参数进行优化。基于这个优化过程,设计一种新型的PSO-PID复合控制器,以取代传统的PID控制器。通过与其他BP神经网络、模糊控制等方法进行对比凸显该控制方法的优越。通过数字仿真分析显示,跟踪速度相较于BP-PID控制方法提高了1.91倍,相对于PID控制方法提高了3.5倍,相对于F-PID控制方法提高了1.75倍。同时,控制精度相对于BP-PID控制方法提高了46.03%,相对于PID控制方法提高了66.30%,相对于F-PID控制方法提高了45.27%。结果显示,采用PSO-PID控制器能够快速达到控制目标且具有较小的超调量。

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.

Analytical Structures of Typical Takagi-Sugeno Fuzzy PID Gain Scheduling Controllers

The analytical structure of a class of typical Takagi Sugeno (TS) fuzzy controllers is revealed in this paper.The TS fuzzy controllers consist of three or more trapezoidal input fuzzy sets, Zadeh fuzzy logic AND operator,fuzzy rules with linear consequent, and the centriod defuzzifier. The TS fuzzy controllers are proved to be accurately nonlinear PID controllers with gains continuously changing with process output. The analytical expressions of the variable gains of the TS fuzzy controllers are derived and their mathematical characteristics including the bounds and geometrical shape of the gain variation are analyzed. The resulting explicit structures show that the TS fuzzy controllers are inherently nonlinear PID gain scheduling controllers with variable gains in different regions of input space.

Takagi - Sugeno Fuzzy PID Gain - Scheduling Controllers

This paper aims at Takagi - Sugeno (TS) fuzzy controllers as gain scheduling (GS) schemes of PID controllers. A TS fuzzy controller employs arbitrary input fuzzy sets, product or Zadeh fuzzy logic AND, TS fuzzy rules with linear consequent, and the generalized defuzzifler containing the popular centrold defuzzifler as a special case. We first establish the relationship between the TS fuzzy controller and the linear PID controller. The TS ftizzy controller is accurately a nonlinear PID controller with gains continuously changing with Its process output. Then we point out that the TS fuzzy controller is closely related to the traditional gain scheduler. The gains of the TS ftizzy controller are determined by three two - Input - one - output fuzzy systems with singleton output fuzzy sets. Finally, as a demonstration, a simple TS fuzzy controller employing two linear input fuzzy sets, Zadeh fuzzy logic AND, and the popular centrold defuzzifler is designed to be the gain scheduler for the PID controller.

A New Coordinated Fuzzy-PID Controller for Power System Considering Electric Vehicles

With penetration growing of renewable energy sources which integrated into power system have caused problems on grid stability. Electric Vehicles (EV) are one of the renewable energy sources that can bring significant impacts to power system during their charging and discharging operations. This article established a model of single machine infinite bus (SMIB) power system considering EV as a case study of load disturbance for power system oscillation. The objective of this research is to enhance stability and overcome the drawbacks of traditional control algorithms such as power system stabilizer (PSS), PID controller and fuzzy logic controller (FLC). The implementation’s effect of FLC parallel with PID controller (Fuzzy-PID) has been shown in this paper. The speed deviation (?ω) and electrical power (Pe) are the important factors to be taken into consideration without EV (only change in mechanical torque), EV with change in the mechanical torque and sudden plug-in EV. The obtained result by nonlinear simulation using Matlab/Simulink of a SMIB power system with EV has shown the effectiveness of using (Fuzzy-PID) against all disturbances.

Design and Stability Analysis of Fuzzy Switched PID Controller for Ship Track-Keeping

The fuzzy switched PID controller which combines fuzzy PD and conventional PI controller is proposed for ship track-keeping autopilot In this paper. By using rudder angle, the whole voyage is divided into two operating regimes which named transient operating regime and steady operating regime respectively. The fuzzy PD controller is employed in transient operating regime for increasing response, reducing overshoot and shorting transition time. And conventional PI controller is used to improve the stable accuracy in steady operating regime. The global controller is achieved by fuzzy blending of all local controllers. Routh stability criterion is utilized to obtain the stability condition of closed-loop system. The simulation results show the effectiveness of proposed method.

基于ABC-PID算法的变量喷雾控制系统设计与试验

针对常用流量调节式变量喷雾系统控制参数调节复杂,导致流量控制响应延迟、超调量大等问题,提出利用寻优性能好的人工蜂群算法整定PID参数,从而达到精准调节系统流量的目标,进而提高变量喷雾系统的工作性能。为验证该系统的控制效果,运用MatLab进行仿真分析,发现ABC-PID控制在超调量、上升时间两方面均优于常规PID和模糊PID。在黑龙江八一农垦大学植保实验室进行室内试验,验证ABC-PID、模糊PID及常规PID的控制精度,结果表明:恒速试验平均施药量误差分别为1.3%、3.1%、4.5%,变速试验平均施药量误差分别为2.1%、3.8%、4.9%。根据上述仿真试验和室内试验结果分析,验证了ABC-PID在控制效果方面优于其他两种控制方法。结合仿真试验、室内试验分析可知:提出引入ABC算法优化常规PID控制参数,在施药量控制精度和减小超调量等方面均表现良好,可为精准变量施药技术提供新的思路。

Particle Swarm Optimization Based Fuzzy-Neural Like PID Controller for TCP/AQM Router

In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks.

基于Fuzzy-PID的负载敏感挖掘机位姿控制策略

挖掘机执行机构轨迹的精确控制是实现其智能化、无人化发展的基础。针对泵控/阀控相耦合的负载敏感(Load Sensitive,LS)系统挖掘机,提出了一种自适应的模糊PID控制方法(Fuzzy-PID)以实现LS挖掘机执行机构位姿的精确控制。该方法不依赖离线计算,可实现作业过程中PID参数的整定。建立LS挖掘机联合仿真模型对Fuzzy-PID的控制性能进行验证,结果表明,Fuzzy-PID控制精度更高,与PID相比,其均方根误差(Root Mean Square Error,RMSE)减少了23.85%。进一步,通过发动机转速及斗杆运行速度验证了Fuzzy-PID稳定性和响应性。研究结果可为负载敏感系统液压挖掘机智能化升级提供理论指导及工程应用价值。

基于AWF-DPID控制的同步Buck变换器研究

针对当前各种电子设备不断发展对电源管理芯片的精度、速度等要求越来越高的趋势,提出一种基于模糊逻辑的反计算法抗饱和双环PID(AWF-DPID)控制策略,实现同步Buck变换器的恒压限流功能。在双环PID(DPID)的基础上,设计一个二维模糊控制器,实时调整电压环输出参数,在电压环输出设计一个反算法抗积分饱和模块,从而调节Buck变换器的脉冲宽度,更高效精准地实现控制。变换器在10 kHz的频率下工作,给出变换器参数通过软件对不同控制策略进行仿真。结果表明,在实现恒压限流的相同控制目标下,AWF-DPID控制较其他控制响应更迅速、超调量更小稳态误差更小并且纹波系数更小;变输出指令下电压跟随性最好,有良好的动态特性。