一类不确定非线性系统的鲁棒自适应控制

针对一类具有未知控制方向、未知参数以及未建模动态的非线性系统,提出了一种带有死区修正的鲁棒自适应递推控制策略.该策略不需要控制方向符号的先验知识.根据参数的上下界先验信息,分别将光滑投影算法和非连续投影算法与参数自适应律结合起来,既抑制了参数的漂移,又使估计参数达到了最小.算法保证了闭环系统所有信号的有界性,同时使得跟踪误差收敛于零的任意小邻域内.     

一类离散非线性系统反步递推黄金分割自适应控制

针对一类含参数不确定的离散时间严反馈非线性系统，提出了反步递推（Backstepping）算法与黄金分割原理相结合的新型控制策略。采用反步递推控制方法处理前n-2阶子系统，利用其结果重构余下的二阶非线性子过程，再通过黄金分割自适应算法对二阶子过程进行控制。该策略一方面解决了单纯反步递推算法在过渡阶段参数失配时，控制量剧烈波动的问题；另一方面减少了反步递推算法的计算量。给出了闭环系统有界稳定性的证明，并通过仿真实例验证了该反步递推黄金分割自适应控制方法的有效性和优越性。     

基于T-S模糊模型的离散不确定时滞系统的容错控制

研究了一类离散不确定时滞模糊系统的容错控制问题。针对非线性离散系统，构造T-S模型，引入参数不确定项和时滞项，使得模糊模型能够更精确逼近原系统。基于T-S模糊模型，利用Lyapunov方法，证明了所采用的模糊控制律使得闭环模糊系统对于执行器故障以及传赢器故障具有完整性和鲁樟挂。不确定项具有范数有界时，利用求解线性矩阵不等式（LMI）的方法，得出了闭环模糊系统在执行器故障以及传意器故障时具有完整性的充分条件。仿真结果说明了该方法的有效性。     

欠控制方向非线性系统的鲁棒自适应控制

针对一类控制方向未知且具有未建模动态的非线性系统,提出了一种鲁棒自适应控制策略.算法推广了已有的鲁棒自适应非线性控制方法.控制算法保证闭环系统所有信号是一致终值有界的.同时,通过调整控制器设计参数,闭环系统能够实现期望的性能要求.仿真结果进一步说明了理论分析的有效性.     

Adaptive Tracking Control for Mobile Manipulators with Stochastic Disturbances

This paper investigates adaptive tracking control for mobile manipulators with stochastic disturbances and parametric uncertainties. Based on an appropriate reduced dynamic model and an adaptive law, a controller, which is provided by incorporating stochastic control theory with related adaptive technique, overcomes the problem of over-parametrization. It is shown that the designed state-feedback controllers can guarantee that the mean square of the tracking errors can be made arbitrarily small by choosing suitable design parameters. Simulation studies on the control of 2-DOF mobile manipulator shows the effectiveness of the proposed scheme.     

The further result on global practical tracking for high-order uncertain nonlinear systems

This paper extends the unknown control coefficients with lower and upper constant bounds to the ones which may take arbitrarily large and /or small values.Since the existing methods are no longer applicable and the technical obstacles caused by the extensions are essential,new control design scheme should be exploited to the global practical tracking.By the approaches of Nussbaum-gain and adding a power integrator,the authors successfully propose the design scheme of the adaptive practical tracking controller for the systems.It is shown that the designed controller guarantees that all the closed-loop system states are bounded and the tracking error becomes prescribed arbitrarily small after a finite time.     

基于高增益观测器的非线性系统自适应模糊滑模控制

针对一类有界的不确定非线性系统,基于高增益观测器并结合自适应模糊逻辑系统和滑模控制,提出了一种基于高增益观测器的自适应模糊滑模控制方案。该方案不需要系统状态可测的条件,而是通过设计高增益观测器来估计系统的状态并能保证观测误差一致最终有界。基于李亚普诺夫函数方法,给出了自适应模糊滑模控制律以及在线调节的参数自适应律。所提出的控制方案不但能使闭环系统稳定,而且保证了跟踪误差的一致最终有界性。仿真结果进一步验证了该控制方案的实用性和有效性。     

Robust adaptive neural network control with supervisory controller

The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed.The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.Simulation results demonstrate the effectiveness of the approach.     

高阶随机非线性系统状态反馈镇定

针对一类高阶下三角随机非线性系统,提出一种新的镇定方案,采用增加一个幂积分的工具构建了一个全局稳定的光滑状态反馈控制律。通过反馈占有设计方法和Young不等式处理随机系统的非线性项,取消了对随机非线性系统反馈线性化和控制输入呈线性的要求,基于Lyapunov方法,证明了闭环系统依概率全局渐近稳定。最后,仿真实例说明了算法的可行性。     

基于后推技术的直接鲁棒自适应模糊控制

针对一类带摄动的严格反馈非线性系统,基于后推设计方法,利用第一类模糊系统的逼近能力,提出了一种新的直接自适应控制方案。该方案中引入连续鲁棒项对系统的摄动部分进行抑制,并在自适应律中利用了leakage项以防止参数漂移。通过理论分析,证明了闭环系统是半全局一致终结有界的,跟踪误差收敛到一个小的残差集内。仿真结果表明了该方法的有效性。     

非线性时滞系统自适应输出反馈跟踪控制

针对一类参数化非线性时滞输出反馈系统,提出了一种自适应输出跟踪控制器的设计方案。引入时滞滤波器估计未知系统状态,采用backstepping技术设计控制律和参数自适应律,运用占优方法处理非线性时滞项,从而放宽了对非线性时滞函数的要求,实现了对给定目标轨线的全局渐近跟踪,同时保证闭环系统所有信号一致有界。基于Lyapunov-Krasoviskii泛函方法证明了整个闭环系统的稳定性。实例仿真说明了该方案的可行性。     

增益符号未知的鲁棒自适应模糊控制

针对一类具有函数控制增益符号未知的不确定非线性系统,根据滑模控制原理和模糊系统的逼近能力,提出了一种自适应模糊控制器设计新方案。该方案利用Nussbaum函数的性质,取消了常用的函数控制增益符号已知这一条件。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和参数估计误差的影响。理论分析证明了闭环系统的有界性和跟踪误差收敛到零。仿真结果表明了该方法的有效性。     

STABILIZATION OF A KIND OF PREY-PREDATOR MODEL WITH HOLLING FUNCTIONAL RESPONSE

The stabilization problem of a kind of prey-predator model with Holling functional responseis investigated.By approximate linearization approach,a feedback control law stabilizing the closed-loop system is obtained.On the other hand.by exact linearization approach,a suitable change ofcoordinates in the state space and a feedback control law render the complex nonlinear system to bea linear controllable one such that the positive equilibrium point of the closed-loop system is globallyasymptotically stable.     

Neuromorphic Continuous-Time State Space Pole Placement Adaptive Control

Abstract: A neuromorphic continuous-time state space pole assignment adaptive controller is proposed, which is particularlyappropriate for controlling a large-scale time-variant state-space model due to the parallely distributed nature ofneurocomputing. In our approach, Hopfield neural network is exploited to identify the parameters of a continuous-timestate-space model, and a dedicated recurrent neural network is designed to compute pole placement feedback control law inreal time. Thus the identification and the control computation are incorporated in the closed-loop, adaptive, real-timecontrol system. The merit of this approach is that the neural networks converge to their solutions very quickly andsimultaneously.     

新型变结构控制律在导弹姿态控制系统中的应用

在对变结构控制理论研究的基础上，首先介绍了一种新型的变结构控制律的设计方法，该控制律在设计中引入了相平面中的状态轨迹与滑动平面的夹角，兼具连续控制律与非连续控制律的优点。本文中对该变结构控制方法进行了改进，扩展了其应用范围。最后将其应用于导弹姿态控制系统中的设计中，取得了良好的效果。     

未知非线性系统的神经网络跟踪控制与仿真研究

应用输入／输出反馈线性化方法和李亚普诺夫方法,研究了一类具有未知非线性函数的非线性动态系统的自适应鲁棒输出跟踪控制问题。首先通过坐标变换和输入变换,将非线性系统变换为部分线性可控系统。接着采用多层前向神经网络来逼近未知非线性函数,网络的权值根据李亚普诺夫原则来在线修正,这样就克服了多神经网络控制系统中存在的稳定性问题。同时,为了减少权值学习时间,应用遗传算法预先离线训练网络权值。最后提出了一个基于神经网络建模的自适应鲁棒控制律,给出了李亚普诺夫意义下的稳定性证明。所提出的控制律可确保相应闭环系统的状态及跟踪误差一致最终有界。所给的Van der pol系统的例子说明了所提控制方案的有效性与鲁棒性。     

车辆悬挂系统的鲁棒主动控制设计

基于特征结构配置参数化结果,提出了车辆悬挂系统的鲁棒主动控制设计方法,其目的是设计状态反馈控制律,使得闭环系统具有希望极点且使得干扰对系统的影响达到最小.此外,该方法直接基于车辆悬挂系统的参数矩阵,便于应用.最后, 车辆悬挂系统的仿真实例,表明该鲁棒主动控制设计方法的简单性和有效性.     

直线不稳定船舶航迹控制的稳定性研究

针对直线不稳定船舶的全局直线航迹控制,提出了一类航迹非线性状态反馈控制算法。采用Lya-punov直接法分析系统的稳定性,得到了保证闭环控制系统全局渐近稳定的充分条件,从而解决了直线不稳定船舶航迹闭环控制系统全局稳定性的判定问题。数值仿真和模拟试验结果验证了该充分条件的正确性。     

基于鲁棒控制Lyapunov函数的非线性自适应跟踪

研究了一类带有未知参数以及扰动的多输入非线性系统的自适应跟踪问题，利用控制Lyapunov方法设计出一种自适应控制器。该控制器对系统的参数和状态的不确定性具有鲁棒性，并且能保证系统闭环全局稳定。仿真结果表明，该控制方案具有良好的跟踪性能、鲁棒性及参数辨识能力。     

Global asymptotic regulation control for nonlinear systems with iISS inverse dynamics

This paper investigates a global asymptotic regulation control problem for a class of nonlinear systems with dynamic uncertainties.The requirement of a priori knowledge of control directions is removed and the inverse dynamics satisfy the weaker integral input-to-state stable condition.By application of the changing supply rates and the Nussbaum-type gain techniques,a partial state-feedback regulator is constructed.The main results demonstrate that the designed controller ensures the system state converges to the origin whereas the other signals of the closed-loop system are bounded. Simulation results are illustrated to show the effectiveness of the proposed approach.