Fuzzy controller training using particle swarm optimization for nonlinear system control

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi–Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.     

阀控非对称伺服缸基于粒子群优化的模糊PID控制器研究

针对阀控非对称伺服缸非线性、参数时变的特点,考虑到油液压缩特性的影响,建立了包含变体积弹性模量的系统数学模型。提出一种基于粒子群算法优化(PSO)的模糊自适应PID控制方法(简称PSO_FPID)。模糊逻辑推理在线调整PID控制器的比例、积分和微分系数,以粒子群算法实现对模糊控制比例因子和量化因子的参数寻优,两种方法的组合保证了系统最佳参数匹配下的自适应控制。同时,利用AMESim与Simulink联合仿真研究不同含气量的阀控缸模型在传统PID与PSO_FPID两种控制方法下的动态响应特性。结果表明:PSO_FPID综合了PID控制器高精度的优点和模糊控制器快速、适应性强的特点,能够有效抑制油液动态压缩特性的非线性影响,使系统具有良好的动、稳态特性。     

Application of polynomial control to design a robust oscillation-damping controller in a multimachine power system

This paper addresses the application of a static Var compensator (SVC) to improve the damping of interarea oscillations. Optimal location and size of SVC are defined using bifurcation and modal analysis to satisfy its primary application. Furthermore, the best-input signal for damping controller is selected using Hankel singular values and right half plane-zeros. The proposed approach is aimed to design a robust PI controller based on interval plants and Kharitonov׳s theorem. The objective here is to determine the stability region to attain robust stability, the desired phase margin, gain margin, and bandwidth. The intersection of the resulting stability regions yields the set of k_p–k_i parameters. In addition, optimal multiobjective design of PI controller using particle swarm optimization (PSO) algorithm is presented. The effectiveness of the suggested controllers in damping of local and interarea oscillation modes of a multimachine power system, over a wide range of loading conditions and system configurations, is confirmed through eigenvalue analysis and nonlinear time domain simulation.     

Optimal Predicted Fuzzy PI Gain Scheduling Controller of Constant Turning Force Systems with Fixed Metal Removal Rate

The dynamic behaviour of the turning process is nonlinear and time-varying owing to variations in cutting depth. This paper proposes an optimal predicted fuzzy PI gain scheduling controller to control the constant turning force (CTF) process with a fixed metal removal rate (MRR) under various cutting conditions. The predicted fuzzy PI gain scheduling control scheme consists of two parts: the fuzzy PI gain scheduling controller; and the grey predictor. First, the optimal parameters of both the grey predictor and the optimal PI gains corresponding to each desired cutting depth in the range of operation, are designed off-line by using the proposed optimal combined method, i.e. Taguchi–RGA method, which integrates the Taguchi method and a real-coded genetic algorithm (RGA). Then, before the parameters of both the grey predictor and the PI gains are scheduled on-line, by fuzzy inference in terms of the changes of cutting depth, the optimal set of triangular-type membership functions of the fuzzy inference mechanism for scheduling the parameters of both the grey predictor and the PI gains are also designed off-line by using the Taguchi–RGA method. Computer simulations are performed to verify the applicability of this optimal predicted fuzzy PI gain scheduling control scheme for controlling the CTF process with a fixed MRR under various cutting conditions. It is shown that such an optimal predicted fuzzy PI gain scheduling control scheme can achieve satisfactory performance and better results than those reported recently in the literature.     

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.     

车辆四轮转向控制平台研究

针对机-电-液复合结构的四轮转向平台具有非线性、快时变的特点，提出了模糊自适应PID控制策略。将模糊自适应补偿器与PID控制器并联，提高系统的鲁棒性和抗干扰能力。使用PID控制器稳定系统的线性标称部分，应用模糊自适应补偿器调节PID参数来补偿系统参数摄动、非线性和外界扰动对系统控制性能的影响。仿真和外加扰动实验结果验证了提出的控制策略对四轮转向平台的控制具有快速性、准确性、稳定性和鲁棒性。     

Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor

In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.     

基于PSO的模糊PID车载平台调平控制系统研究

为解决车载平台调平控制系统响应速度慢、自适应能力差的问题,对一款液压马达带动滚珠丝杠的调平支腿,建立其数学模型,提出了基于粒子群优化(Particle Swarm Optimization,PSO)的模糊比例积分微分(Proportional Integral Derivative,PID)控制方案.通过结合PSO算法...     

小型航空汽油发动机模糊增益恒转速控制

汽油发动机具有非线性、时变时滞等特点,传统的增益可调PID方法很难保证发动机整个工作范围内转速控制的稳定性及性能要求.笔者采用模糊PID控制,来实现小型汽油发动机的恒转速控制.在不同转速/负载点,采用时域阶跃响应方法,建立了一组线性模型来表示发动机的非线性特性.基于建立的发动机模型,给出了模糊规则和推理来在线调整PID...     

A simple nonlinear PD controller for integrating processes

Many industrial processes are found to be integrating in nature, for which widely used Ziegler–Nichols tuned PID controllers usually fail to provide satisfactory performance due to excessive overshoot with large settling time. Although, IMC (Internal Model Control) based PID controllers are capable to reduce the overshoot, but little improvement is found in the load disturbance response. Here, we propose an auto-tuning proportional-derivative controller (APD) where a nonlinear gain updating factor α continuously adjusts the proportional and derivative gains to achieve an overall improved performance during set point change as well as load disturbance. The value of α is obtained by a simple relation based on the instantaneous values of normalized error (e_N) and change of error (Δe_N) of the controlled variable. Performance of the proposed nonlinear PD controller (APD) is tested and compared with other PD and PID tuning rules for pure integrating plus delay (IPD) and first-order integrating plus delay (FOIPD) processes. Effectiveness of the proposed scheme is verified on a laboratory scale servo position control system.     

单元机组模糊免疫自适应PID控制器的设计

针对火电单元机组的多变量、时变和非线性特性,应用免疫反馈系统的原理和模糊控制理论设计了一个模糊免疫自适应PID控制器。对控制器中的比例系数kp采用免疫控制器调节,用模糊规则分别对ki,kd进行在线整定。结果表明,模糊免疫自适应PID控制器具有响应速度快、鲁棒性强等优点。     

Positioning and tracking of a linear motion stage with friction compensation by fuzzy logic approach

In this paper, the development of a fuzzy controller that compensates for nonlinear friction in a linear motion stage is presented. The experimental work and instrumentation set up is presented for this research. Based upon a nonlinear friction model, friction parameters were estimated from experimental results. Simulation and experimental validation on a ball-screw mechanism is presented, showing the effectiveness of the proposed controller. Furthermore, it is shown that the proposed fuzzy control scheme offers several implementation advantages such as smaller control effort, and reduced effect of measurement noise. Moreover, the fuzzy logic methodology displays superior performance when compared to a conventional PID controller. It also shows good and robust tracking with respect to system parameters variation.     

基于微粒群算法的钢水液位模糊控制器的设计

针对钢水液位控制系统的非线性、时变和大滞后性,提出一种基于微粒群算法的钢水液位模糊控制器的设计来改变系统的性能,通过仿真分析得出文中采用的控制方案与传统的PID控制方案相比,具有良好的动静态性能.     

四相平板式TFPM模糊PID控制系统的设计

针对四相平板式横向磁场永磁电机非线性、时变性的特点,采用单一传统的PID控制无法达到满意的控制效果,提出了一种复合控制即模糊PID控制算法。借助于模块化建模仿真工具搭建系统仿真模型,进行模糊PID与传统PID控制的对比研究,仿真结果表明模糊PID控制精度高,动态响应速度快,静态误差小,对外部扰动(负载扰动)具有很强抑制能力并且对电机参数变化也具有较强的自适应性。解决了四相平板式横向磁场永磁电机非线性、时变性特点的控制问题。     

基于遗传整定的主动悬架模糊PID控制设计

针对车辆主动悬架模糊PID控制设计,提出了一种利用遗传算法整定模糊PID增益系数的方法。基于二自由度的1/4车辆模型,根据模糊控制原理和PID控制方法,设计了主动悬架的模糊PID控制器。利用具有全局寻优能力的遗传算法,以悬架二次型性能指标为目标函数,采用适应性比例法确定选择概率,整定了模糊PID的增益系数。优化结果表明,整定后的模糊PID控制主动悬架相对被动悬架综合性能良好,说明了提出的方法是有效的,对模糊PID控制设计具有重要的参考价值。     

极低静压模拟系统非对称模糊自调整PID控制

极低静压模拟系统以真空泵为负压源,由于真空泵抽气能力随模拟压力降低呈非线性降低,导致系统具有强非线性和非对称性。针对线性控制器难以克服非对称性影响,且不能够获得大范围性能一致性的难题,提出一种非对称模糊自调整PID控制策略。该控制方法根据系统特性,结合模糊逻辑构建非对称模糊自调整机构对PID控制器参数在线调整,以适应系统特性的非线性变化。仿真结果表明：该控制方法可以克服系统非对称性,在大范围内获得良好的控制性能。     

Nonlinear control of an activated sludge aeration process: use of fuzzy techniques for tuning PID controllers

In this paper, several tuning algorithms, specifically ITAE, IMC and Cohen and Coon, were applied in order to tune an activated sludge aeration PID controller. Performance results of these controllers were compared by simulation with those obtained by using a nonlinear fuzzy PID controller. In order to design this controller, a trial and error procedure was used to determine, as a function of error at current time and at a previous time, sets of parameters (including controller gain, integral time and derivative time) which achieve satisfactory response of a PID controller actuating over the aeration process. Once these sets of data were obtained, neural networks were used to obtain fuzzy membership functions and fuzzy rules of the fuzzy PID controller.     

模糊PID控制器在电锅炉温度控制系统中的应用

电锅炉已经成为供热采暧的主要设备．它的温度控制系统由于存在非线性、大滞后以及时变性等特点,常规的PID控制器很难达到较好的控制效果。考虑到模糊控制能够对复杂的非线性、时变系统进行很好的控制。但却无法消除静态误差的特点．本文将模糊控制引入到常规PID控制中．提出了一种模糊PID参数自整定控制器。并且对电锅炉温度控制系统进行了抗扰动的实验。仿真结果表明。和常规PID控制器相比．所设计的模糊PID控制器改善了温度控制系统的动态性能。提高了系统的鲁棒性。     

模糊自适应控制器在过程控制中的应用

研究了T-S模糊推理机的结构,推导出该推理机具有非线性PID控制器的形式,在此基础上设计了基于模糊规则调整的模糊自适应控制器,对设计的模糊自适应控制器进行了仿真。     

基于粒子群优化的锅炉温度模糊PID控制器设计及其仿真

首先讨论了模糊理论和粒子群(PSO)优化算法,然后针对典型的工业过程控制对象,设计了一种基于PSO优化的二维自适应锅炉温度模糊PID控制器,可以对比例因子和量化因子进行优化。对锅炉温度控制对象系统模型进行辨识,得到锅炉温度的传递函数,在此基础上利用MATLAB进行了仿真。仿真结果表明,采用基于PSO优化的锅炉温度控制与PID控制、模糊控制相比,具有超调量小、上升时间短、响应速度快、调节时间短、稳定性能好、抗干扰能力强等优势。