机械臂系统的自适应跟踪控制

考虑一类非线性机械臂系统的跟踪控制.该系统包含未知的定常参数和多个未知的周期时变参数.提出了一种新的自适应跟踪控制方法,该方法能保证闭环系统的所有信号有界,且跟踪误差趋于零.     

多输入多输出非线性最小相位系统的自适应模糊控制

针对多输入多输出非线性最小相位系统,把自适应模糊控制和自适应模糊辨识结合起来,提出了一种自适应模糊控制方案.设计辨识器用来辨识系统的未知部分;然后由跟踪误差和辨识误差给出了参数调节规律,两种误差同时调节参数改善了系统性能.模糊逻辑系统用来估计未知函数.控制方案保证了系统的稳定性,实现了有界跟踪.仿真结果表明了该方案的可行性.     

非线性多时滞系统的直接自适应模糊控制

针对一类单输入单输出非线性多时滞系统,提出了一种自适应模糊跟踪控制方案.该方案结合了自适应控制和H∞控制.构建了自适应时滞模糊逻辑系统用来逼近未知时滞函数;设计了H∞补偿器来抵消模糊逼近误差和外部扰动.根据跟踪误差给出了参数调节规律.证明了误差闭环系统满足期望的H∞跟踪性能.仿真结果表明了该方案的有效性.     

一类死区非线性输入系统的自适应模糊控制

针对一类具有死区非线性输入的非线性系统,基于滑模控制的基本原理,利用II型模糊逻辑系统对未知函数进行在线逼近,提出了一种具有监督器的自适应模糊滑模控制方法。该方法通过监督控制器保证闭环系统所有信号有界,并通过引入最优逼近误差的自适应补偿项来消除建模误差的影响。通过理论分析,证明了跟踪误差收敛到零。     

舵角输入饱和下的导弹自动驾驶仪系统自适应控制研究

针对一类导弹自动驾驶仪系统,从实际出发考虑系统舵角输入具有饱和非线性特性,在系统建模中利用多项式模型充分描述系统的气动参数不确定性,在此基础上深入研究了系统自适应控制器的设计方法.在控制器设计中,通过引入二阶辅助信号系统,对饱和非线性输入进行精确补偿.针对系统中未知气动参数,通过设计参数的自适应估计率实现对未知参数的精确估计,进而补偿了未知气动参数不确定性影响.自适应控制器在保障系统稳定的同时也实现了系统输出对指令信号的良好跟踪效果.     

航天器逼近自由翻滚目标的自适应相对运动控制

研究含有模型不确定性且受未知外干扰的追踪航天器自主逼近空间中一自由翻滚目标的6自由度(6DOF)相对运动控制问题.首先在追踪器本体系中建立了非线性耦合的6DOF相对运动模型,并将其化为关于追踪器和目标器未知惯性参数和追踪器未知推力偏心距的参数化形式.再基于该参数化模型设计了一种自适应非线性控制器,其中利用基于投影的自适应算法保证了在线估计参数的有界性.所设计的自适应控制器不仅显著减少了系统在线估计的参数数量,且能保证6DOF闭环系统的位置和姿态误差收敛到原点的小邻域内.仿真算例验证了所提出控制器的有效性.     

非线性时滞系统自适应模糊动态面控制

针对未知非线性系统控制器设计过程中引入逼近器过多的问题,提出一种简化的自适应模糊动态面控制器设计方案.在控制器设计过程中,仅采用一个模糊逻辑系统作为逼近器,使得所有的未知项得到补偿,同时采用自适应技术在线辨识未知参数和逼近误差上界.文中的控制方案克服了传统backstepping控制器中"复杂性膨胀"的问题.通过构造合适的Lyapunov函数,证明闭环系统的所有信号为半全局最终一致有界.仿真实例验证所提出的控制方案的有效性.     

基于性能的直流电机系统未知推力波动的自适应补偿控制

针对一类直流电机系统,设计基于性能的未知推力波动的自适应补偿控制器.由于推力波动中含有非线性化的未知参数,因此无法利用常规的自适应方法,通过直接估计这些参数对其影响进行补偿.根据未知推力波动的结构特性,基于Lipshitz条件引入推力波动项的放大技术,设计其内部非线性化未知参数的自适应估计律,精确补偿其影响,从而避免了简单的把波动看作未知扰动所造成的系统性能的损失.有别于现有模型,在模型变换中并未忽略微小等效电感的影响,使得系统模型更具一般性.仿真结果表明该自适应控制器可有效补偿推力波动的影响,实现对指令信号的高精度快速跟踪且具有较强的抗干扰性和鲁棒性.     

自适应模糊变结构控制的研究

本文主要研究一类具有未知常数控制增益的非线性系统的自适应模糊控制问题,提出了一种能够利用专家的语言信息和数字信息的自适应模糊变结构控制器的设计方案。通过理论分析,证明了模糊变结构控制系统是全局稳定的,跟踪误差可收敛到零的一个邻域内     

基于免疫函数的输入死区未知严格反馈非线性系统投影自适应指令滤波反步控制

为解决含有未知项以及输入死区的严格反馈非线性系统跟踪控制问题,提出一种基于免疫函数的投影自适应指令滤波有限时间控制方法.该方法使用免疫函数构造扩张状态观测器对具有输入死区控制系统中的未知项进行逼近,并使用指令滤波解决反步法中微分爆炸问题,建立滤波误差补偿机制降低滤波误差对跟踪精度的影响,同时使用投影算子保证了自适应参数的有界性.与现有文献中基于障碍李雅普诺夫函数的自适应反步约束控制相比较,文章可同时约束系统状态、补偿跟踪误差以及自适应参数在预设的范围内,保证了闭环系统中所有信号有界,结合有限时间控制加快了控制系统的收敛速度.最后仿真结果表明了文章控制方法的有效性.     

A new synchronization algorithm for delayed complex dynamical networks via adaptive control approach

An adaptive control strategy is developed for complex delayed dynamical networks with time-varying coupling strength and time-varying delayed. Using the Lyapunov stability theory, an adaptive control is designed to ensure the asymptotic convergence of the synchronization error, a sufficient condition of the synchronization is obtained. By constructing a Lyapunov–Krasovskii-like composite energy function, we prove the stability of the closed-loop system and the convergence of the error. An example of the complex network is finally used to verify the proposed theoretical result.     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

有限时间迭代学习控制

针对任意初态情形, 借助于初始修正吸引子的概念,讨论不确定时变系统能够达到实际完全跟踪性能的迭代学习控制方法.闭环系统中含有限时间控制作用, 在预先指定的区间上实现零误差跟踪,且起始段的系统输出轨迹也可预先规划.分别讨论部分限幅学习与完全限幅学习, 证明闭环系统中各变量的一致有界性以及误差序列的一致收敛性. 变量有界性证明得益于提出的限幅学习算法,特别是完全限幅学习算法可确保参数估值的变化范围.     

非脆弱离散重复控制的二维模型方法

针对一类线性离散系统,提出一种基于二维模型的非脆弱离散重复控制设计方法.通过独立地考虑重复控制系统的控制与学习行为,建立离散重复控制系统的二维模型. 在此基础上,针对重复控制器和反馈控制器具有不确定性的离散重复控制系统,给出了基于线性矩阵不等式的系统稳定性条件和重复控制律. 最后,数值仿真实例验证了所提方法的有效性.     

Decentralized robust model reference adaptive control for interconnected time-delay systems

In this paper, the problem of decentralized robust model reference control for a class of interconnected time-delay systems is investigated. The interconnections with time-varying time delays considered are high order and the gains are not known. A class of decentralized adaptive feedback controllers are proposed, which can render the resulting closed-loop error system uniformly ultimately bounded stable. A numerical example is given to show the feasibility and effectiveness of the proposed design techniques.     

On the combination of kernel principal component analysis and neural networks for process indirect control

ABSTRACT

A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.     

An online identification algorithm of unknown time-varying delay and internal multimodel control for discrete non-linear systems

In this paper, an online algorithm is proposed for the identification of unknown time-varying input delay in the case of discrete non-linear systems described by decoupled multimodel. This method relies on the minimization of a performance index based on the error between the real system and the partial internal models outputs. In addition, a decoupled internal multimodel control is proposed for the compensation of discrete non-linear systems with time-varying delay. This control scheme incorporates partial internal model controls. Each partial controller is associated to a specified operating zone of the non-linear system. The switching between these controllers is ensured by a supervisor that contains a set of local predictors. A simulation example is carried out to illustrate the significance of the proposed time-varying delay identification algorithm and the proposed internal multimodel control scheme.     

Design of observers for nonlinear systems with H ∞ performance analysis

This paper investigates the problem of observer design for nonlinear systems. By using differential mean value theorem, which allows transforming a nonlinear error dynamics into a linear parameter varying system, and based on Lyapunov stability theory, an approach of observer design for a class of nonlinear systems with time‐delay is proposed. The sufficient conditions, which guarantee the estimation error to asymptotically converge to zero, are given. Furthermore, an adaptive observer design for a class of nonlinear system with unknown parameter is considered. A method of H _∞ adaptive observer design is presented for this class of nonlinear systems; the sufficient conditions that guarantee the convergence of estimation error and the computing method for observer gain matrix are given. Finally, an example is given to show the effectiveness of our proposed approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Decentralized model-reference adaptive control for a class of uncertain large-scale time-varying delayed systems with series nonlinearities

In this paper, the problem of model-reference adaptive control for large-scale time-varying delayed systems with series nonlinearities is investigated. By applying the theory of variable structure control, we propose an adaptive controller, which is both memoryless and decentralized, to derive the error subsystem between the local model state and plant state to zero. The proposed variable structure control is able to ensure the stability of a sliding manifold of the composite system even though the control input is nonlinear. The main difficulty for handling the effects of interconnected terms is well solved by a new proposed adaptation mechanism. Finally, a numerical example is illustrated to demonstrate the validity of the derived controller.     

Robust adaptive neural-fuzzy-network control for the synchronization of uncertain chaotic systems

This paper proposes a robust adaptive neural-fuzzy-network control (RANFC) to address the problem of controlled synchronization of a class of uncertain chaotic systems. The proposed RANFC system is comprised of a four-layer neural-fuzzy-network (NFN) identifier and a supervisory controller. The NFN identifier is the principal controller utilized for online estimation of the compound uncertainties. The supervisory controller is used to attenuate the effects of the approximation error so that the perfect tracking and synchronization of chaotic systems are achieved. All the parameter learning algorithms are derived based on Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. Finally, simulation results are provided to verify the effectiveness and robustness of the proposed RANFC methodology.