Multi‐parametric extremum seeking‐based iterative feedback gains tuning for nonlinear control

We study in this paper the problem of iterative feedback gains auto‐tuning for a class of nonlinear systems. For the class of input–output linearizable nonlinear systems with bounded additive uncertainties, we first design a nominal input–output linearization‐based robust controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model‐free multi‐parametric extremum seeking control to iteratively auto‐tune the feedback gains. We analyze the stability of the whole controller, that is, the robust nonlinear controller combined with the multi‐parametric extremum seeking model‐free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example. Copyright © 2016 John Wiley & Sons, Ltd.     

基于支持向量机的参数自整定PID非线性系统控制

对非线性系统提出了一种基于支持向量机的自整定PID控制新方法.用支持向量机辨识系统的非线性关系,并对之进行线性化,提取出瞬时线性模型,采用最小方差的准则获取PID控制器的最优参数.为改善控制器的性能,提出了一些改进措施,包括使用一阶滤波器、控制器参数更新标准及惩罚系数的调整等.通过对典型非线性系统的仿真,验证了该方法的有效性和可行性.     

A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach

In this paper, we will propose a self-tuning method for a class of nonlinear PID control systems based on Lyapunov approach. The three PID control gains are adjustable parameters and will be updated online with a stable adaptation mechanism such that the PID control law tracks certain feedback linearization control, which is previously designed. The stability of closed-loop nonlinear PID control system is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Finally, a tracking control of an inverted pendulum system is illustrated to demonstrate the control performance by using the proposed method.     

Neural network‐based adaptive dynamic surface control of uncertain nonlinear pure‐feedback systems

In this paper, by incorporating the dynamic surface control technique into a neural network‐based adaptive control design framework, we have developed a backstepping‐based control design for a class of nonlinear systems in pure‐feedback form with arbitrary uncertainty. The circular design problem which may exist in pure‐feedback systems is overcome. In addition, our development is able to eliminate the problem of ‘explosion of complexity’ inherent in the existing backstepping‐based methods. A stability analysis is given, which shows that our control law can guarantee the semi‐global uniformly ultimate boundedness of the solution of the closed‐loop system, and makes the tracking error arbitrarily small. Moreover, the proposed control design scheme can also be directly applied to the strict‐feedback nonlinear systems with arbitrary uncertainty. Copyright © 2010 John Wiley & Sons, Ltd.     

基于支持向量机的一类混沌系统自适应逆控制

本文研究了基于支持向量机回归自适应逆控制的混沌控制方法,用支持向量机建立系统的辨识器,同时在控制过程可逆的条件下设计基于支持向量回归的系统逆控制器.将该自适应逆控制的方法应用于Lorenz混沌系统的控制,仿真结果表明在系统带有不确定性和测量噪声的情况下,该方法可以有效的将混沌系统的状态控制到给定状态.     

基于PID参数整定的无模型自适应控制器参数整定方法

本文针对一阶线性时不变(FOLTI)系统, 将常用的比例–积分–微分(PID)控制器参数整定方法推广到无模型自适应控制(MFAC)参数整定中, 利用PID控制参数整定方法整定直接型MFAC方案的伪梯度初值, 以期改变MFAC控制律初始参数的试凑式选取, 提升MFAC应用的方便性和可接受性. 通过仿真, 验证了所提伪梯度初值整定方法的有效性, 并对比了FOLTI系统的系统参数和系统结构发生变化后, 应用MFAC与PID控制的性能差异.     

一种基于DMC的新型预测PID控制器及其整定(英文)

本文提出一种基于动态矩阵控制(DMC)算法预测特性的新型PID控制方法.在考虑将来的输出期望偏差罚函数最小的前提下,由DMC计算出控制变量的值.继而构造基于DMC的预估器用以预测将来时刻的系统输出.根据将来时刻的多步预测偏差,PID控制器产生当前时刻的实际控制增量.文中也给出了基于DMC的预估器及PID控制器的参数整定方法.仿真结果表明,与常规的PID控制和DMC控制相比,所提方法具有良好的控制性能,扰动抑制尤其优良.     

Formation control for a class of nonlinear multiagent systems using model‐free adaptive iterative learning

In this paper, the problem of formation control is considered for a class of unknown nonaffine nonlinear multiagent systems under a repeatable operation environment. To achieve the formation objective, the unknown nonlinear agent's dynamic is first transformed into a compact form dynamic linearization model along the iteration axis. Then, a distributed model‐free adaptive iterative learning control scheme is designed to ensure that all agents can keep their desired deviations from the reference trajectory over the whole time interval. The main results are given for the multiagent systems with fixed communication topologies and the extension to the switching topologies case is also discussed. The feature of this design is that formation control can be solved only depending on the input/output data of each agent. An example is given to demonstrate the effectiveness of the proposed method.     

基于调节函数的一类三角结构非线性系统的自适应滑模控制

针对一类三角结构的非线性系统,基于状态参考自适应控制算法和滑模控制技术,研究了其在非匹配未知参数和不确定性干扰下的跟踪控制问题,提出了自适应滑模控制策略,实现了不确定非线性系统的鲁棒输出跟踪.与一般自适应控制相比,允许系统存在非参数化的不确定性和未知扰动,增强了控制系统鲁棒性.仿真算例证明了理论研究成果的正确性和可行性.     

optimization‐based fractional‐order PID controllers design

In this paper we propose a fractional‐order proportional‐integral‐derivative controller design based on the solution of an model matching problem for fractional first‐order‐plus‐dead‐time processes. Starting from the analytical solution of the problem, we show that a fractional proportional‐integral‐derivative suboptimal controller can be obtained. Guidelines for the tuning of the controller parameters are given in order to address the robust stability issue and to obtain the required performance. The main differences with respect to the integer‐order case are highlighted. Simulation results show that the design methodology is effective and allows the user to consider process with different dynamics in a unified framework. Copyright © 2013 John Wiley & Sons, Ltd.     

A practical iterative PID tuning method for mechanical systems using parameter chart

In this paper, we propose a method of iterative proportional-integral-derivative parameter tuning for mechanical systems that possibly possess hidden mechanical resonances, using a parameter chart which visualises the closed-loop characteristics in a 2D parameter space. We employ a hypothetical assumption that the considered mechanical systems have their upper limit of the derivative feedback gain, from which the feasible region in the parameter chart becomes fairly reduced and thus the gain selection can be extremely simplified. Then, a two-directional parameter search is carried out within the feasible region in order to find the best set of parameters. Experimental results show the validity of the assumption used and the proposed parameter tuning method.     

Deterministic learning from neural control for uncertain nonlinear pure‐feedback systems by output feedback

The essence of intelligence lies in the acquisition/learning and utilization of knowledge. However, how to implement learning in dynamical environments for nonlinear systems is a challenging issue. This article investigates the deterministic learning (DL) control problem for uncertain pure‐feedback systems by output feedback, which achieves the human‐like learning and control in a simple way. To reduce the complexity of control design and analysis, first, by combining an appropriate system transformation, the original pure‐feedback system is transformed into a simple normal nonaffine system. An observer is then introduced to estimate the transformed system states. Based on the backstepping and dynamic surface control techniques, a simple adaptive neural control scheme is first developed to guarantee the finite time convergence of the tracking error using only one neural network (NN) approximator. Second, through DL, the exponential convergence of the NN weights is obtained with the satisfaction of partial persistent excitation condition. Thus, locally accurate approximation/learning of the transformed unknown system dynamics is achieved and stored as constant NNs. Finally, by utilizing the stored knowledge, an experience‐based controller is constructed and a novel learning control scheme is further proposed to improve the control performance without any further adaptation online for the estimate neural weights. Simulation results have been given to illustrate that the proposed scheme not only can learn and memorize knowledge like humans but also can utilize experience to achieve superior control performance.     

基于内模法的PID控制器自整定算法

为解决传统工业控制中比例-积分-微分(PID)控制器参数整定的问题,提出了一种基于内模法(IMC)以及系统辨识的控制器参数确定算法。该方法首先利用被控过程在开环阶跃信号激励下,输入与暂态输出的对应关系,将被控对象辨识为一阶加滞后(FOPDT)或二阶加时滞(SOPDT)的模型;再利用IMC算法确定控制器的参数。对于在内模法中引入的滤波器参数λ的确定问题,提出通过引入γ和σ两个参数,并与输出误差的平方建立关系来确定λ 的方法。仿真显示,对于输出误差绝对值之和(IAE)这个指标,该种算法与传统基于IMC的PID控制算法相比,在无输入扰动时可提高20%左右,在有输入扰动时可提高10%左右。仿真结果表明:在用单位阶跃信号激励系统时,提出的整定方法在保证了系统鲁棒性的前提下,提高了系统的瞬态响应速度,并有效抑制了系统输出的超调。     

基于LS-SVM的一类非线性系统直接自适应H∞输出反馈控制

针对一类状态不可测的单输入单输出非线性不确定系统,提出了一种基于最小二乘支持向量机（LS-SVM）的直接自适应H∞输出反馈控制方法．该方法首先设计一种误差观测器,间接地估计出系统的状态;然后利用LS-SVM构造白适应控制器,并给出了LS-SVM权向量和偏移值的在线学习规则,通过引入如控制器减弱外部干扰及LS-SVM近似误差对输出误差的影响,利用李亚普诺夫理论证明了整个闭环系统的稳定性．仿真研究表明了该控制方案的可行性和有效性．     

基于改进的LSSVM辨识动态非线性时变系统

针对非线性时变系统难以辨识的问题,提出了一种基于改进最小二乘支持向量机的辨识新方法。该方法在加权最小二乘支持向量机的基础上,引入用矢量基学习和自适应迭代相结合的方式得到一个小的支持向量,同时采用加权方法确定权值系数以减小训练样本中非高斯噪声的影响。通过对动态非线性时变系统的仿真,结果表明该算法具有较好的鲁棒性、支持向量稀疏性和动态建模实时性。     

一类未知非线性系统的智能迭代学习控制

从自适应的角度设计迭代学习控制，将神经网络引入迭代学习控制中。学习控制与自适应控制相结合，使得对网络权值的学习和跟踪控制同时进行，克服 了经典迭代学习控制的一些缺陷。基于Lyapunov直接方法，证明了整个控制系统的稳定并实现了任意精度的跟踪。实例仿真结果说明了算法 的有效性及其所具有的优点。     

New tuning conditions for a class of nonlinear PID global regulators of robot manipulators

Several nonlinear proportional-integral-derivative (PID) controllers for robot manipulators that ensure global asymptotic stability have been proposed in the literature. However, the tuning criteria obtained are expressed in terms of conditions so restrictive that they have avoided, until now, carrying out experimental tests with such controllers. Tuning criteria of some PID controllers for robot manipulators with conditions more relaxed than those presented previously in the literature have been proposed in two recent works by the authors. This was achieved by setting the tuning conditions individually for each joint instead of general conditions for the whole robot. In this paper we extend these results to a class of nonlinear PID global regulators for robot manipulators. The obtained tuning criteria are given in terms of conditions so relaxed that they have allowed to carry out, for the first time, experimental essays with these controllers. Such experiments are presented in this paper using a two-degrees-of-freedom robot manipulator.     

Observer‐based adaptive neural control for a class of nonlinear singular systems

The problem of observer‐based adaptive neural control via output feedback for a class of uncertain nonlinear singular systems is studied in this article. The nonlinear singular systems can be regarded as two subsystems that are coupled with each other: differential subsystem and algebraic subsystem. The differential systems can be nonstrict feedback structures. To guarantee that the singular system is regular and impulse‐free, two new conditions are proposed. By the conditions, the linear controller and observer, which are used to estimate the immeasurable state variables, are obtained. Then, an output feedback scheme through adaptive neural backstepping is proposed to ensure that all states of the closed‐loop system are semiglobally uniformly ultimately bounded and converge to a small neighborhood of the origin. Simulation examples illustrate the effectiveness of the presented method.     

一类非线性系统的自适应神经网络控制

针对一类具有非仿射函数和下三角结构的、受干扰未知的非线性系统,提出一种新的自适应神经网络控制方法．它是严格反馈不确定系统和纯反馈系统的更一般化表达．在Backstepping设计思想基础上,证明了闭环信号的半全局最终一致有界性,并很好地处理了控制方向和控制奇异问题．通过仿真验证了该方法的有效性．