模糊自适应PID参数自整定控制器的研究

当控制系统中的被控对象存在纯滞后、时变或非线性等复杂因素时,普通的PID控制器的控制效果很难达到较好的控制效果,针对这一问题,应用模糊控制和自适应控制的知识,设计了模糊自适应PID参数自整定控制器,此控制器的比例系数、积分系数和微分系数可根据模糊推理规则进行在线调整.仿真结果表明,该控制方法提高了系统的动、静态特性,使该系统具有较好的鲁棒性.     

基于模糊控制系统的自整定PID 参数控制器的设计

本文主要讨论了以模糊控制来自动整定PID控制器的三个参数,并与经典PID控制进行了对比,从而认识了两者的优缺点,为实际应用提供了一种新的方法。通过MATLAB／Simulink对系统进行计算机仿真,指出该控制器具有动态范围宽、稳态精度高、响应速度快和超调量小的优点;对于控制对象的变化,该控制器具有良好的适应能力。     

基于PID参数整定的模糊控制器

本文根据文献[1]提出的用于整定PID三个参数的模糊控制器以及大量的仿真实验,总结出一套更好的模糊规则.仿真结果表明,修改了模糊控制规则后的模糊PID控制器能使系统的动静态性能得到提高.     

基于MATLAB的模糊自整定PID参数控制器的设计与仿真

针对在复杂系统中实现自整定参数的PID控制问题,介绍了一种基于模糊控制原理的PID参数自整定控制器的设计,并把MATLAB中的Fuzzy Toolbox和SIMULINK有机结合起来,方便的实现了该模糊自整定PID参数控制系统的计算机仿真.     

基于MATLAB的模糊自整定PID参数控制器的设计与仿真

针对在复杂系统中实现自整定参数的PID控制问题,介绍了一种基于模糊控制原理的PID参数自整定控制器的设计,并把MATLAB中的FuzzyToolbox和SIMULINK有机结合起来,方便的实现了该模糊自整定PID参数控制系统的计算机仿真。     

基于MATLAB的PID参数模糊自整定控制器设计及仿真

本文针对时滞、参数时变和有干扰的控制系统,提出基于MATLAB的PID参数模糊自整定控制器的设计与仿真。本文利用GUI(图形用户界面)建立模糊控制器,仿真模型为二阶延迟系统。仿真结果表明,系统阶跃响应具有较好的动态特性和鲁棒性,表明该PID参数模糊自整定控制器有较高的实用性。     

参数在线调整的模糊自适应PID控制研究

PID是工程中最为广泛应用的控制器技术,因其需要在实际使用中多次凑试来确定参数,这个过程非常耗时.而且凑试出来的参数往往固定,虽然能够使被控系统最终达到控制要求,但收敛过程较慢.模糊PID的提出部分地解决了这些问题,通过偏差量来选择模糊规则表里的不同数值,达到了动态地调整PID参数的效果,使被控系统能够快速收敛.然而,模糊PID依然存在改进的空间,通过对模糊PID的输入论域和输出论域分别引入指数型函数作为伸缩因子的方法,使模糊PID的参数达到了在线调整的效果,实现了模糊自适应PID.通过仿真应用的比较结果,证实了提出的参数在线调整的模糊自适应PID控制方法,相较于普通的模糊PID具有更优的动态特性.     

基于参数模糊自整定PID温度控制系统

针对PVC聚合釜釜内温度控制系统的大时滞、非线性等特点和目前采用釜内温度串级控制系统的不足,提出一种采用基于模糊控制原理的PID参数自整定控制方案,实现PID参数依据不同的生产负荷进行自我调整。实践证明,提高了控制器的动态响应性能和控制器的控制精度。     

一种参数自调整PID模糊控制器

结合传统PID控制原理,提出一种新型模糊控制器结构,即PID模糊控制器。为提高PID控制器性能,设计能在线调整PID参数的模糊控制方法。仿真结果表明,自调整参数PID型模糊控制器使系统在暂态响应及稳态性能方面性能优良。     

一种基于Matlab的参数自调整模糊控制器的设计方法

本文介绍了一种在MATLAB的模糊控制工具箱中,通过编写S函数实现对量化因子和比例因子的在线自动调整来设计模糊控制器,从而有效地实现参数自调整模糊控制器的设计方法。为了验证参数自调整模糊控制器的优越性,分别进行了空调温度控制系统的PID控制、常规模糊控制和参数自调整模糊控制的仿真研究。结果表明,参数自调整模糊控制器较之常规的模糊控制器,在被控对象特性变化或较大扰动的情况下,控制系统能保持较好的性能,是一种较理想的控制方法,具有广阔的发展前景。     

操作器的PID自适应控制

利用操作器动力学模型的性质,提出了一种前馈补偿加PID反馈控制的自适应控制方案.由于在控制力矩中引入积分项,使操作器关节的跟踪精度和抗干扰能力得到提高.本文分析了摩擦干扰和执行机构的惯性对控制方案的影响.本文进一步的研究表明:在许多情况下,只需对操作器动力学中耦合和非线性最强的项进行补偿,然后加PID反馈控制,就能取得较好的控制效果.从而简化了控制方案,以PUMA560的前三个关节的参数作模型,对文中的方法进行了仿真.     

基于模糊控制器的自适应广义通用模型控制

广义通用模型控制(GCMC)方法是一般模型控制(GMC)的改进,适用于相对阶大于1的复杂多输入多输出系统,该控制器参数具有明显的物理意义,但鲁棒性不够强。将模糊控制与广义通用模型控制相结合,构成模型参考自适应控制系统,从而加强了系统的鲁棒性,仿真实验证明了该策略的有效性。     

基于模糊PID的静变电源控制技术研究

为提高静变电源输出电压的质量,研究了一种自整定模糊PID控制方法.该方法将模糊控制优良的动态性能、灵活的控制特性和PID稳态控制性能的优势相结合,实时地对系统控制量进行调整.在Matlab/Simulink环境下,对于模糊PID和常规PID在静变电源控制中的应用分别进行了仿真.仿真结果表明,模糊PID控制器减少了超调量,抗干扰性和鲁棒性强,系统的动态、稳态性能得到了很大程度的提高.     

模糊自整定PID控制在聚合反应中的研究

针对工业生产中常用的反应釜,提出利用模糊自整定PID控制器实现聚合反应温度控制的方法,实现PID控制器参数在线自调整,提高PID控制器的性能和系统的精度.仿真结果表明,该模糊自整定PID对被控系统的适应性强、鲁棒性好、超调小、反应时间快,能很好地适应现实生产过程中的控制要求.     

An adaptive fuzzy sliding mode controller for robotic manipulators

This paper proposes an adaptive fuzzy sliding mode controller for robotic manipulators. An adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller. The adaptive law is designed based on the Lyapunov method. Mathematical proof for the stability and the convergence of the system is presented. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are eliminated and satisfactory trajectory tracking is achieved.     

Robust adaptive fuzzy controller for non‐affine nonlinear systems with dynamic rule activation

This paper describes the design of a robust adaptive fuzzy controller for an uncertain single‐input single‐output nonlinear dynamical systems. While most recent results on fuzzy controllers considers affine systems with fixed rule‐base fuzzy systems, we propose a control scheme for non‐affine nonlinear systems and a dynamic fuzzy rule activation scheme in which an appropriate number of the fuzzy rules are chosen on‐line. By using the proposed scheme, we can reduce the computation time, storage space, and dynamic order of the adaptive fuzzy system without significant performance degradation. The Lyapunov synthesis approach is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as for all other signals in the closed loop. No a priori knowledge of an upper bounds on the uncertainties is required. The theoretical results are illustrated through a simulation example. Copyright © 2002 John Wiley & Sons, Ltd.     

An improved robust fuzzy-PID controller with optimal fuzzy reasoning.

Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.     

An adaptive fuzzy controller based on sliding mode for robotmanipulators

This paper considers adaptive fuzzy control of robotic manipulators based on sliding mode. It is first shown that an adaptive fuzzy system with the system representative point (RP, or as is often termed, a switching function in variable structure control (VSC) theory) and its derivative as inputs, can approximate the robot nonlinear dynamics in the neighborhood of the switching hyperplane. Then a new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics. The system stability and tracking error convergence are also proved by Lyapunov techniques.     

An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot

Pneumatic muscle actuators (PMA) show great potential in wearable and compliant rehabilitation devices as they are flexible and lightweight. However, the varying and non-linear behavior of the actuators imposes modeling and control challenges, which are difficult to comprehend. This research proposes a new wearable ankle rehabilitation robot, first of its kind in the world driven by PMAs in a parallel form. The focus of this presented work is to develop an iterative controller to overcome the challenges for PMA driven devices. A fuzzy feedforward controller is proposed to accurately predict the behavior of PMA. A modified Genetic Algorithm (GA) is developed to identify the optimal set of parameters for the fuzzy controller. The iterative controller has been tested on the proposed PMA driven ankle rehabilitation robot, and is found capable of mapping the complex relationship in length, force and pressure of the PMA with high accuracy. Experimental results show excellent trajectory tracking performance of the controller when given various desired trajectories.