随机Marino-Tomei系统的输出反馈鲁棒自适应跟踪

研究了一类Marino-Tomei非线性模型在不确定噪声干扰下的输出反馈鲁棒自适应跟踪问题.通过滤波变换,采用随机控制Lyapunov设计方法,对于受方差未知的Wiener噪声干扰的Marino-Tomei非线性系统,给出了参数自适应律和控制律,使得闭环系统成为噪声--状态稳定的,并且跟踪误差的4次均方值在时间平均意义下收敛到一个足够小的区域内.     

一类不确定非线性系统的鲁棒自适应控制

针对一类具有一般不确定性和未知参数的非线性系统,设计出一种适用于输出跟踪的鲁棒自适应控制器.该控制器对系统的参数和状态的不确定性具有鲁棒性,能保证闭环系统的全局稳定性,并解决了ε-跟踪问题.仿真实例表明,所设计的鲁棒自适应控制器具有良好的跟踪性能.     

具有不确定未知界的相似组合系统的鲁棒自适应跟踪控制

讨论一类具有相似结构的不确定组合系统的鲁棒自适应跟踪问题。针对系统的不确定性界和扰动界完全未知的情形，首先从理论上证明了可设计鲁棒自适应分散跟踪控制器，确保受控系统的输出渐近跟踪参考模型的输出；进而从工程实际的角度，给出了确保受控系统输出实用跟踪参考模型输出的鲁棒自适应分散跟踪控制器的设计方案。     

一类不确定延迟系统的鲁棒自适应控制

针对一类不确定的延迟系统，提出一种鲁棒的模型参考自适应控制设计方案．它不同于以往将所有延迟部分转化为系统的未建模动态的方案，该方案只是把延迟偏离标称值的扰动转化为系统的未建模动态，而将能够获得的延迟标称值信息加入系统的建模部分，从而使系统的建模更为精确．同时，研究了这种其建模部分含有延迟的系统的稳定性和鲁棒性．仿真结果验证了该方案的有效性．     

不确定非线性系统的模糊鲁棒跟踪控制

提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性.     

严格反馈随机非线性系统的鲁棒自适应逆最优跟踪

针对具有未知定常参数和标准Wiener噪声扰动的严格反馈非线性系统,结合参考信号,构造了误差系统,使用Backstepping算法设计了误差系统的自适应逆最优控制律和参数自适应律,进而解决了原系统的鲁棒自适应逆最优跟踪.     

一类不确定非线性系统的鲁棒自适应ε2输出跟踪控制

针对一类不确定非线性系统，讨论了鲁棒自适应ε-输出跟踪问题．利用Backstepping方法设计了一种自适应光滑状态反馈控制器，使系统输出跟踪给定的C^1参考输出信号．在参考信号及其导数均有界的条件下，得到了全局ε-输出跟踪，且闭环系统所有信号均全局一致有界．仿真结果表明了该设计方法的有效性．     

一类非线性激励器系统的鲁棒自适应跟踪

考虑一类具有非线性激励器不确定系统的鲁棒跟踪问题，其不确定性是部分已知的。所构造的鲁棒自适应控制方案能确保系统的跟踪误差终极一致有界．与已有文献结果相比．未知参数估计的自适应律和控制器是连续的，从而使得所提出的设计方案在实际控制问题中易实现。且与具有线性激励器的系统一样具有较强的鲁棒性．最后通过数值算例进一步说明了该设计方案是有效的。     

一类非线性不确定系统的鲁棒跟踪

讨论一类含有时变不确定性的非线性不确定系统的鲁棒跟踪问题，其中的未知参数变量以非线性形式出现。通过构造适当的Ｌｙａｐｕｎｏｖ函数，给出了该类系统的鲁棒跟踪控制器的设计。     

含时变不确定线性系统的自适应鲁棒跟踪控制

针对系统的不确定性和扰动满足匹配条件,不确定性界和扰动界未知的情形,讨论含时变不确定性线性系统的鲁棒跟踪问题。自适应控制器的设计不涉及确定性界和扰动界,运用变结构方法,可使带扰系统实现渐近跟踪。该方法适合一类较广泛的不确定系统,鲁棒性较强。仿真例子表明了该方法的有效性。     

Adaptive tracking of stochastic nonlinear systems with Prandtl–Ishlinskii hysteresis

This paper deals with adaptive tracking problems for a class of stochastic nonlinear systems with unknown hysteresis nonlinearities. The system considered is in a strict‐feedback form driven by unknown Prandtl–Ishlinskii hysteresis and Wiener noises of unknown covariance. By employing backstepping design techniques and stochastic Lyapunov design method, parameter adaptive laws and control laws are obtained, which ensure that the tracking error can converge to a small residual set around the origin in the sense of mean quartic value. Copyright © 2011 John Wiley & Sons, Ltd.     

Robust adaptive tracking of uncertain nonlinear systems by output feedback

We investigate the problem of robust adaptive tracking by output feedback for a class of uncertain nonlinear systems. Based on the high‐gain scaling technique and a new adaptive law, a linear‐like output feedback controller is constructed. Only one dynamic gain is designed, which makes the controller easier to implement. Furthermore, by modifying the update law, the adaptive controller is robust to bounded external disturbance and is able to guarantee the convergence of the output tracking error to an arbitrarily small residual set. A numerical example is used to illustrate the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

Robust stochastic stability of uncertain discrete-time impulsive Markovian jump delay systems with multiplicative noises

In this paper, the robust stochastic stability is investigated for a class of uncertain discrete-time impulsive Markovian jump delay systems with multiplicative noises. Using the method of stochastic Lyapunov functionals construction, it is shown that impulses can stabilise the original impulse-free unstable systems. Moreover, the stability property of the impulse-free systems can be retained in the cases of appropriately large impulsive time interval. Some numerical examples are exploited to demonstrate the effectiveness and the superiority of the proposed results.     

基于神经网络的不确定随机非线性时滞系统自适应有界镇定

针对一类不确定严格反馈随机非线性时滞系统的自适应有界镇定问题,利用神经网络参数化和Backstepping方法,提出一种新的且含较少学习参数的神经网络自适应控制策略,以保证系统半全局随机有界.稳定性分析证明闭环系统的所有误差信号概率意义下有界.仿真结果表明所提出控制器设计方法的有效性.     

Robust adaptive control of a class of nonlinear uncertain systems

A smooth robust dynamic feedback controller is constructed, and the problem of robust H∞ almost disturbance attenuation with internal stability is solved for high-order nonlinear systems with parameter uncertainties. Finally, illustrative example and simulation results demonstrate the effectiveness of the proposed method.     

On convergence of active disturbance rejection control for a class of uncertain stochastic nonlinear systems

In this paper, the practical mean-square convergence of active disturbance rejection control for a class of uncertain stochastic nonlinear systems modelled by the Itô-type stochastic differential equations with vast stochastic uncertainties is developed. We first design an extended state observer (ESO) to estimate both the unmeasured states and the stochastic total disturbance which includes unknown internal system dynamics, external stochastic disturbance without known statistical characteristics, unknown stochastic inverse dynamics, and uncertainty caused by the deviation of control parameter from its nominal value. The stochastic total disturbance is then cancelled (compensated) in the feedback loop. An ESO-based output-feedback control is finally designed analogously as for the system without uncertainties. The practical mean-square reference tracking and practical mean-square stability of the resulting closed-loop system are achieved. The numerical experiments are carried out to illustrate the effectiveness of the proposed approach.     

Approximation-based disturbance observer approach for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched disturbances

This paper presents an approximation-based nonlinear disturbance observer (NDO) methodology for adaptive tracking of uncertain pure-feedback nonlinear systems with unmatched external disturbances. Compared with existing control results using NDO for nonlinear systems in lower-triangular form, the major contribution of this study is to develop an NDO-based control framework in the presence of non-affine nonlinearities and disturbances unmatched in the control input. An approximation-based NDO scheme is designed to attenuate the effect of compounded disturbance terms consisting of external disturbances, approximation errors and control coefficient nonlinearities. The function approximation technique using neural networks is employed to estimate the unknown nonlinearities derived from the recursive design procedure. Based on the designed NDO scheme, an adaptive dynamic surface control system is constructed to ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a neighbourhood of the origin. Simulation examples including a mechanical system are provided to show the effectiveness of the proposed theoretical result.     

Robust tracking problem for continuous time stochastic uncertain systems

We propose a finite‐horizon robust minimax tracking controller design method for time‐varying continuous time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which stochastic noises, driving the system, are defined. A minimax optimal tracking controller is derived from the solution of a risk‐sensitive linear quadratic Gaussian control problem. Also a numerical example is presented to illustrate the characteristics of proposed tracking controller. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

State tracking model reference adaptive control for switched nonlinear systems with linear uncertain parameters

Model reference adaptive control(MRAC)is considered for a class of switched nonlinear systems in which the unknown parameters appear linearly.The linear uncertain parameters in each subsystem can be expressed as a vector and the uncertain vectors in different subsystems are estimated individually by different vector variables.Update laws are designed such that the parameter estimation will ’freeze’ until its corresponding subsystem is active.Controllers for subsystems are given to ensure asymptotic states tracking under arbitrary switchings.Two examples are presented to validate the proposed method.     

不确定非线性系统全局渐近自适应神经网络控制

针对一类控制增益为一般函数形式的不确定仿射非线性系统,提出一种能够确保全局渐近稳定的自适应神经控制(adaptiveneural control,ANC)方法.为了保证神经网络逼近的适用性,设计一种可变控增益的比例微分(proportionaldifferential,PD)控制器以全局镇定被控对象.利用状态变换解决由未知控制增益函数导致的控制奇异问题.提出一种连续的自适应鲁棒控制项实现闭环系统的渐近跟踪.与现有的全局渐近跟踪ANC方法相比较,本文方法不仅简化了PD增益的选择,而且减轻了控制输入的颤振问题.仿真结果表明了本文方法的有效性.