自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

基于自适应模糊逻辑的死区补偿控制研究

采用自适应模糊控制方法研究了死区补偿问题。通过引入系统当前状态与理想状态的归一化距离，构造了调节权函数，实现对模糊控制输入变量量化因子的自动调节，使得当系统处于死区时量化因子显著增加，使控制器的输出摆脱死区范围，形成对死区的自适应补偿。仿真结果表明该方法能够有效地控制系统稳态性能，同时不会引起系统的震荡。     

A two-stage design of adaptive fuzzy controllers for time-delay systems with unknown models

In this article, a systematic two-stage design method for adaptive fuzzy controllers is presented. The proposed control scheme has low computational complexity. Moreover, the exact mathematical model of the plant to be controlled is not required. The fuzzy controller under consideration is based on the proportional-derivative fuzzy control scheme and triangular membership functions. In the design procedure, the domain intervals of the input and output variables are selected with a heuristic approach to minimize a cost function under the constraint of tolerable overshoots in the response curve. A learning scheme is then proposed to automatically adjust the parameters in the fuzzy controller to reduce the error of the system. It can also be used adaptively to improve the system performance of a time-varying system. Simulations and comparisons are included to demonstrate the effectiveness of the proposed method.     

基于模糊神经网络的模型参考自适应控制

用模糊神经网络作为控制器,依靠参考模型产生理想的控制系统闭环响应,从而随时得 到控制系统的输出误差.用梯度法实时修正模糊控制器的输入和输出隶属度参数,得到一种 在线模糊自适应控制的新方法.通过倒立摆的仿真实验表明,该方法是可行的并能适应对象 特性的大范围变化.     

A Combined Adaptive Law for Fuzzy Iterative Learning Control of Nonlinear Systems With Varying Control Tasks

To deal with the iterative control of uncertain nonlinear systems with varying control tasks, nonzero initial resetting state errors, and nonrepeatable mismatched input disturbance, a new adaptive fuzzy iterative learning controller is proposed in this paper. The main structure of this learning controller is constructed by a fuzzy learning component and a robust learning component. For the fuzzy learning component, a fuzzy system used as an approximator is designed to compensate for the plant nonlinearity. For the robust learning component, a sliding-mode-like strategy is applied to overcome the nonlinear input gain, input disturbance, and fuzzy approximation error. Both designs are based on a time-varying boundary layer which is introduced not only to solve the problem of initial state errors but also to eliminate the possible undesirable chattering behavior. A new adaptive law combining time- and iteration-domain adaptation is derived to search for suitable values of control parameters and then guarantee the closed-loop stability and error convergence. This adaptive algorithm is designed without using projection or deadzone mechanism. With a suitable choice of the weighting gain, the memory size for the storage of parameter profiles can be greatly reduced. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. Moreover, the norm of tracking state error vector will asymptotically converge to a tunable residual set even when the desired tracking trajectory is varying between successive iterations.     

Backstepping sliding mode control with FWNN for strict output feedback non-smooth nonlinear dynamic system

An output feedback backstepping sliding mode control scheme was developed for precision positioning of a strict single-input and single-output (SISO) non-smooth nonlinear dynamic system that could compensate for deadzone, dynamic friction, uncertainty and estimations of immeasurable states. An adaptive fuzzy wavelet neural networks (FWNNs) technique was used to provide improved approximation ability to the system uncertainty. The adaptive laws were derived for application to estimate the deadzone and friction parameters using recursive backstepping controller design procedures. In addition, the sliding mode control method was also combined to enforce the robustness of the output feedback backstepping controller against disturbance. The Lyapunov stability theorem was used to prove stability of the proposed control system. The usefulness of the proposed control system was verified by simulations and experiments on a robot manipulator in the presence of a deadzone and friction in the actuator.     

A new approach to adaptive fuzzy control: the controller outputerror method

The controller output error method (COEM) is introduced and applied to the design of adaptive fuzzy control systems. The method employs a gradient descent algorithm to minimize a cost function which is based on the error at the controller output. This contrasts with more conventional methods which use the error at the plant output. The cost function is minimized by adapting some or all of the parameters of the fuzzy controller. The proposed adaptive fuzzy controller is applied to the adaptive control of a nonlinear plant and is shown to be capable of providing good overall system performance.     

Direct fuzzy model‐reference adaptive control

Intelligent systems may be viewed as a framework for solving the problems of nonlinear system control. The intelligence of the system in the nonlinear or changing environment is used to recognize in which environment the system currently resides and to service it appropriately. This paper presents a general methodology of adaptive control based on multiple models in fuzzy form to deal with plants with unknown parameters which depend on known plant variables. We introduce a novel model‐reference fuzzy adaptive control system which is based on the fuzzy basis function expansion. The generality of the proposed algorithm is substantiated by the Stone‐Weierstrass theorem which indicates that any continuous function can be approximated by fuzzy basis function expansion. In the sense of adaptive control this implies the adaptive law with fuzzified adaptive parameters which are obtained using Lyapunov stability criterion. The combination of adaptive control theory based on models obtained by fuzzy basis function expansion results in fuzzy direct model‐reference adaptive control which provides higher adaptation ability than basic adaptive‐control systems. The proposed control algorithm is the extension of direct model‐reference fuzzy adaptive‐control to nonlinear plants. The direct fuzzy adaptive controller directly adjusts the parameter of the fuzzy controller to achieve approximate asymptotic tracking of the model‐reference input. The main advantage of the proposed approach is simplicity together with high performance, and it has been shown that the closed‐loop system using the direct fuzzy adaptive controller is globally stable and the tracking error converges to the residual set which depends on fuzzification properties. The proposed approach can be implemented on a wide range of industrial processes. In the paper the foundation of the proposed algorithm are given and some simulation examples are shown and discussed. © 2002 Wiley Periodicals, Inc.     

利用模糊系统的直升机的自适应控制

针对直升机动力学为非线性,且存在不确定因素和状态变化,设计利用模糊系统的自适应控制器.设计的控制器是系统的输出跟踪参考模型输出的直接调整模糊控制器参数的自适应控制器.又利用Lyapunov函数保证了闭环控制系统的稳定性并推导最优的自适应规律.实验结果表明,有外部扰动的情况下所设计的自适应控制器比模糊控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

高超声速飞行器纵向通道分层模糊自适应H∞控制

针对具有参数不确定性特点的高超声速飞行器输出跟踪问题,提出了一种基于分层模糊系统的自适应H_∞控制器的设计方法。为了解决模糊控制器中规则数目随系统变量个数呈指数增长的问题,减少在线辨识参数的数量,增强控制系统的实时性,设计了一种基于分层模糊系统的间接自适应控制器;同时为了减少模糊系统逼近误差、参数不确定性和系统外部干扰对控制系统稳定性造成的影响,引入鲁棒补偿项,提高控制器的H_∞性能,并利用Lyapunov理论分析证明了整个系统的稳定性能。仿真结果表明,该方法不仅能够保证高超声速飞行器具有良好跟踪性能,而且具有很强的鲁棒性。     

A systematic method for design of multivariable fuzzy logic controlsystems

This paper proposes a systematic method to design a multivariable fuzzy logic controller for large-scale nonlinear systems. In designing a fuzzy logic controller, the major task is to determine fuzzy rule bases, membership functions of input/output variables, and input/output scaling factors. In this work, the fuzzy rule base is generated by a rule-generated function, which is based on the negative gradient of a system performance index; the membership functions of isosceles triangle of input/output variables are fixed in the same cardinality and only the input/output scaling factors are generated from a genetic algorithm based on a fitness function. As a result, the searching space of parameters is narrowed down to a small space, the multivariable fuzzy logic controller can quickly constructed, and the fuzzy rules and the scaling factors can easily be determined. The performance of the proposed method is examined by computer simulations on a Puma 560 system and a two-inverted pendulum system     

基于控制器输出误差方法的自适应模糊控制及其在机器人轨迹跟踪控制中的应用

本文提出一种自适应模糊控制器并将之用于机器人轨迹跟踪控制 ,该控制器采用控制器输出误差方法 (COEM) ,根据控制器的输出误差而不是对象的输出误差来在线地调整模糊控制器的参数 ,无须对对象进行辩识 .仿真结果表明该控制器用于机器人轨迹跟踪控制具有很好的性能 ,是一种有效的控制器     

一类具有未知死区MIMO系统的自适应模糊控制

针对一类具有未知死区并具有下三角函数控制增益矩阵的不确定MIMO非线性系统, 根据滑模控制原理, 并利用Nussbaum函数的性质, 提出了一种自适应模糊控制器的设计方案. 该方案取消了函数控制增益符号已知和死区模型参数上界、下界已知的条件. 通过引入积分型李亚普诺夫函数及最优逼近误差与死区扰动上界的自适应补偿项，证明了闭环系统是稳定的，跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

Flexible complexity reduced PID-like fuzzy controllers

In this paper, a flexible complexity reduced design approach for PID-like fuzzy controllers is proposed. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controllers is significantly reduced. However, the performance of the complexity reduced fuzzy PID controller may be degraded since the degree of freedom is decreased by the combination of input variables. To alleviate the drawback and improve the performance of the complexity reduced PID-like fuzzy controller, a flexible complexity reduced design approach is introduced in which the functional scaling factors are heuristically generated. Since the functional scaling factors are heuristically created, they can be easily adjusted for the flexible complexity reduced PID-like fuzzy controller without a priori knowledge of the exact mathematical model of the plant. Moreover, heuristic scaling factors are implemented as functionals. Therefore, the complexity of the flexible PID-like fuzzy controller will not be increased. Further, the stability of the fuzzy control system with a flexible complexity reduced PID-like fuzzy controller is discussed. Finally, the simulation results are also included to show the effectiveness of the PID-like fuzzy controller designed with the flexible complexity reduced approach.     

基于模糊控制的直流电机死区补偿器的设计

本文提出了一种基于模糊控制的直流电机死区补偿器的设计方法。该方法将模糊控制函数的优化和参数的自调整集中于一体，因而能自适应地进行死区补偿来消除跟踪误差。仿真结果表明跟踪误差很小，设计方案行之有效。     

Integration of PSO and GA for optimum design of fuzzy PID controllers in a pendubot system

In this paper, a novel auto-tuning method is proposed to design fuzzy PID controllers for asymptotical stabilization of a pendubot system. In the proposed method, a fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than the conventional ones with fixed gains. To tune the fuzzy PID controller simultaneously, an evolutionary learning algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA) methods is proposed. The simulation results illustrate that the proposed method is indeed more efficient in improving the asymptotical stability of the pendubot system. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

具有输入死区的非线性系统的鲁棒重复控制

针对一类输入含死区非线性特性的周期时变系统, 在周期时变参数不可参数化的情形下设计鲁棒重复控制器. 采用微分自适应律估计未知死区参数, 剩余的有界项通过鲁棒方法予以消除, 为避免出现颤振现象, 采用饱和函数替代符号函数. 在系统输出跟踪周期轨迹的情形下, 将非参数化不确定项转化为含周期时变参数的形式, 以达到利用周期学习律进行估计的目的. 理论分析与仿真结果表明, 采用部分饱和或全饱和学习算法均能实现输出误差有界收敛, 并保证闭环系统所有信号有界.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

串级连续搅拌反应釜的有限时间命令滤波控制

针对串级连续搅拌反应釜系统的快速精准跟踪控制问题,利用自适应反步控制方法、模糊逻辑系统、命令滤波器以及有限时间控制技术设计串级连续搅拌反应釜系统的有限时间命令滤波控制器.其中,自适应反步方法使系统控制器的设计更简单;模糊逻辑系统通过逼近系统模型中的复杂非线性函数使控制器的在线计算量更小;命令滤波器解决了经典反步法带来的“计算爆炸”的问题;有限时间控制方法能够使系统被控量更迅速地跟踪其参考值; Lyapunov稳定性分析证明了系统的稳定性.通过Matlab实例仿真验证所设计控制器的有效性和可行性,为有限时间命令滤波控制技术在实际串级连续搅拌反应釜过程中的应用提供指导.与现有控制方法相比,所提出的控制策略具有控制器结构简单、在线计算复杂度小、跟踪速度快以及无静差的优点.     

非线性离散系统的自适应PID控制器

针对非线性离散系统设计了利用TSK(Takagi Sugeno Kang)模糊模型的自适应PID控制器。利用模糊模型预测控制信号误差,通过控制信号误差自适应PID控制器参数。比较系统输出和模糊模型输出自适应模糊模型的参数。该方法可以弥补系统参数的模糊性、数学模型的模型误差和系统参数的变化。非线性离散系统的仿真实验验证了所设计的自适应PID控制器对非线性离散系统控制的有效性。