Closed‐Loop Identification for Control of Linear Parameter Varying Systems

This paper deals with system identification for control of linear parameter varying systems. In practical applications, it is often important to be able to identify small plant changes in an incremental manner without shutting down the system and/or disconnecting the controller; unfortunately, closed‐loop system identification is more difficult than open‐loop identification. In this paper we prove that the so‐called Hansen scheme, a technique known from linear time‐invariant systems theory for transforming closed‐loop system identification problems into open‐loop‐like problems, can be extended to accommodate linear parameter varying systems as well. We investigate the identified subsystem's parameter dependency and observe that, under mild assumptions, the identified subsystem is affine in the parameter vector. Various identification methods are compared in direct and Hansen Scheme setups in simulation studies, and the application of the Hansen Scheme is seen to improve the identification performance.     

一类离散时间时变系统的自适应辨识与控制

考虑一类离散时间时变系统（ＴＶＳ）的建模、系统参数时变描述及其自适应控制问题,提出一种新的能结合“快”、“慢”时变特性的ＴＶＳ参数时变描述方法,基于零极点配置给出自适应辨识和控制算法及其控制系统的稳定性。仿真结果说明该控制策略是有效的。     

非线性系统的神经网络鲁棒自适应跟踪控制

针对一类具有未知非线性函数和未知虚拟系数非线性函数的二阶非线性系统,提出了一种神经网络鲁棒自适应输出跟踪控制方法.用李雅普诺夫稳定性分析方法证明了本文的神经网络自适应控制器能够使受控系统内的所有信号均为有界.选择的神经网络权值调整规律可以防止自适应控制中的参数漂移.     

一类多变量非线性动态系统的模糊自适应控制

对一类非线性多变量未知动态系统，提出了一种模糊处在适应控制策略。证明了该控制算法能保证闭环系统稳定，跟踪误差收敛。     

Adaptive regulation—Rejection of unknown multiple narrow band disturbances (a review on algorithms and applications)

The paper addresses the problem of attenuation (rejection) of unknown and time varying multiple narrow band disturbances without measuring them. In this context the disturbance model is unknown and time varying while the model of the plant is known (obtained by system identification) and almost invariant. This requires to use an adaptive feedback approach. The term “adaptive regulation” has been coined to characterize this control paradigm. Application domains include: mechanical and mechatronics systems, active vibration and noise suppression systems, some types of bio-chemical reactors.The paper reviews the various techniques proposed for solving this problem. It will focus on the presentation of the direct and indirect adaptive regulation strategies using the internal model principle and the Youla–Kucera parametrization which have been extensively used in applications.The paper also reviews a number of applications including: active suspension systems, active vibration control systems, active noise control, bio-chemical reactors, distributed flexible mechanical structures and Blu-ray disc drives. Real time results obtained on various applications will illustrate the methodology.     

一类不确定非线性系统的鲁棒自适应控制

针对一类输入带有一定的不确定性的非线性系统 ,设计了一种鲁棒自适应控制器。该控制器作用与系统 ,能保证闭环系统信号的全局有界性 ,又能使闭环系统输出任意小。     

一类非线性系统的自适应模糊控制

针对一类非线性系统,利用模糊推理系统对非线性函数的逼近能力,导出基于Ｌｙａｐｕｎｏｖ稳定性理论的自适应控制器,不但能解决这类非线性系统的跟踪问题,而且可保证闭环系统的稳定性。仿真结果表明这一算法的有效性。     

一类离散时间非线性系统不依赖受控系统数学模型的学习自适应控制

给出了一类离散时间非线性系统的不依赖受控系统数学模型的学习自适应控制方案，它不需要受控系统的结构信息、数学模型、外部试验信号和训练过程，仅用受控系统的I／O数据来设计，传统的未建模动态不存在，所给出的计算机仿真结果说明了所给出的方案的正确性和有效性。     

基于神经网络的一类非线性系统自适应H∞控制

基于神经网络提出一种自适应Ｈ∞控制方法。控制器由等效控制器和Ｈ∞控制器两部分组成,用神经网络逼近未知非线性函数,Ｈ∞控制器用于减弱外部及神经网络逼近误差对跟踪误差的影响。所设计的控制器不仅保证了闭环控制系统的稳定性,而且使外部干扰及神经网络逼近误差对跟踪误差的影响减小到预定的性能指标。     

Generalized Nonlinear Stiffness Identification on Controlled Mechanical Vibrating Systems

A new algebraic parametric identification method in time domain for multiple degrees‐of‐freedom mechanical vibrating systems with high‐order nonlinear stiffness is proposed. Parameters of mass, damping and linear and nonlinear stiffness are estimated on‐line and, simultaneously, using transient real‐time position measurements and active control force signals. Parametric identification can be applied for real‐time estimation of both symmetrical and non‐symmetrical stiffness. Parametric identification is combined with adaptive planned motion control on Multiple‐Input‐Multiple‐Output nonlinear mechanical vibrating systems. Analytical and numerical results prove the effectiveness and efficiency of the proposed on‐line algebraic parametric identification approach.     

基于自适应律修改的MIMO非线性系统模糊控制

针对一类不确定多输入多输出(MIMO)非线性系统,提出了一种新的间接自适应模糊控制设计方案,解决了提高模糊控制精度问题。该方法对模糊逻辑系统中的未知参数设计了一种新的自适应学习律,证明了在此自适应律作用下,不但能使跟踪误差收敛到原点的小邻域内,而且通过适当增大设计参数值,可使跟踪误差减小,提高了控制精度。通过对连续发酵过程控制的仿真研究验证了该方法的有效性。     

基于递归神经网络的一类非线性无模型系统的自适应控制

给出了基于递归神经网络非线性无模型的自适应控制方案，它具有灵活、简单、方法等特点，可以处理传统方法和非线性无模型系统自适应控制方法不能控制或控制效果不理想的非线性对象。理论分析和仿真结果证明了这种方法的优越性。     

一种新的数据驱动的非线性自适应切换控制方法

针对一类非线性离散时间动态系统, 提出了一种新的非线性自适应切换控制方法. 该方法首先把非线性项分解为前一拍可测部分与未知增量和的形式, 并充分利用被控对象的大数据信息和知识, 把非线性项前一拍可测数据与未知增量都用于控制器设计, 分别设计了线性自适应控制器, 带有非线性项前一拍可测数据补偿的非线性自适应控制器以及带有非线性项未知增量估计与补偿的非线性自适应控制器. 三个自适应控制器通过切换函数和切换规则来协调控制被控对象. 既保证了闭环系统的稳定性, 同时又提高了闭环系统的性能. 分析了闭环切换系统的稳定性和收敛性. 最后, 通过水箱液位系统的物理实验, 实验结果验证了所提算法的有效性.     

An Adaptive Control Using Fuzzy Basis Function Expansions for a Class of Nonlinear Systems

An adaptive control using fuzzy basis function expansions is proposed for a class of nonlinear systems in this paper. It is shown that two system uncertainty bounds are approximated in a compact set by using fuzzy basis function expansion networks in the Lyapunov sense, and the outputs of the fuzzy networks are then used as the parameters of the controller to adaptively compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to unknown system dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can be guaranteed to asymptotically converge to zero. Simulation results are provided to demonstrate the effectiveness, simplicity and practicality of the proposed control scheme.     

Robust Adaptive Identification of Nonlinear System Using Neural Network

It is well known that disturbance can cause divergence of neural networks in the identification of nonlinear systems. Sufficient conditions using so‐called modified algorithms are available to provide guaranteed convergence for adaptive system. They are dead zone scheme, adaptive law modification, and σ‐modification. These schemes normally require knowledge of the upper bound of the disturbance. In this paper, a robust weighttuning algorithm is used to train the multi‐layered neural network with an adaptive dead zone scheme. The proposed robust adaptive algorithm does not require knowledge of either the upper bound of the disturbance or the bound on the norm of the estimate parameter. A complete convergence proof is provided based on Lyapunov theorem to deal with the nonlinear system. Simulation results are presented to show good perfor‐mance of the algorithm.     

Adaptive Fuzzy PID Control Method Based on Identification Structure

I. INTRODUCTION There are more or less non-linear systems in industry process and sometimes it is hard or hardly to find an accurate model, so the common PID control cannot realize the accurate control on these conditions. Fuzzy control does not need to find an accurate mathematic model, so it is applied more and more widely in industry process and many other realms [1,2]. This paper use identification structure at first to establish the mathematics model, and then use logical reasoning …     

一类非匹配不确定性非线性系统子波网络稳定自适应控制

针对一类非匹配不确定性非线性系统,提出一种基于子波网络的稳定自适应跟踪控制方案,对子波网络逼近误差界进行自适应处理,不需要逼近误差界的先验信息,仿真结果表明所提出的方案是有效的。     

一类非线性参数系统的鲁棒自适应控制

针对一类具有非线性参数和未知非线性的非线性系统, 提出了一种鲁棒自适应控制设计方法, 该方法能保证所有信号全局一致有界, 并且使所研究的非线性系统的范围大大扩大.     

可补的非时变非线性系统的自适应控制算法

本文对确定的非线性提出了非时变非线性可补性的概念，并应用于自适应控制器算法中，以实现补偿非线性的目的。系统性能逼近于线性系统，并且保证在确定和随机的情况下，控制算法均具有大范围渐近稳定性。     

一类虚拟控制系数未知的随机非线性时滞大系统的适应镇定控制

研究了一类具有未知虚拟控制系数和未知噪声协方差的随机非线性时滞大系统的适应镇定问题. 首先, 针对系统的未知虚拟控制系数和未知噪声协方差, 选取了相应的估计参数; 然后, 针对时变时滞对闭环系统稳定性的影响, 构造了适当形式的Lyapunov-Krasovskii泛函, 采用积分反推方法给出了无记忆状态反馈控制律的系统设计过程. 在一定条件下, 证明了闭环系统平衡点依概率全局稳定, 且除参数估计以外的所有闭环信号几乎均收敛到零点. 仿真算例验证了所给方法的有效性.