输入通道有干扰多变量MRAC系统全局稳定化控制

对具有未建模动态且输入通道存在干扰的动态不确定多输入多输出(MIMO)模型参考自适应控制(MRAC) 系统，仅应用系统的输入输出量测数据给出了一种变结构模型跟踪控制器设计机制.通过辅 助信号和带有记忆功能的正规化信号,并适当选择控制器参数, 所提出的变结构控制 (VSC)能保证闭环系统的全局稳定性,且跟踪误差可调整到任意小.     

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.     

一类具有有界扰动的非线性系统的输出反馈自适应控制

该文考虑一类具有有界干扰和未知参数的非线性系统（１）,设计出一种用于输出跟踪的输出反馈鲁棒自适应控制器。该控制器能保证闭环系统的全局稳定性,并且解决了ε－跟踪问题。仿真结果表明,所设计的自适应控制器具有良好的跟踪性能。     

一类不确定非线性系统的适应输出反馈控制

针对一类存在有界干扰的线性参数化非线性系统，给出了一种新的适应输出反馈控制器．该控制器能保证闭环控制系统所有信号的全局有界性．该文将现有的非线性适应输出反馈控制方面的结果，从无扰动情形推广到了存在有界干扰的情形．仿真结果用来说明所设计控制器的有效性．     

输入通道有干扰多变量MRAC系统的全局稳定化控制

对具有未建模动态并且输入通道存在干扰的动态不确定多输入多输出(MIMO)模型参考自适应控制(MRAC)系统,应用输出反馈给出了一种变结构模型跟踪控制器设计．系统的已建模部分有大于1的任意相对阶且已建模部分阶的上界是未知的．通过引入辅助信号和带有记忆功能的正规化信号,以及适当选择控制器参数,保证了闭环系统的全局稳定性,且跟踪误差可调整到任意小．     

多工作点加速度计组合件高精度鲁棒温度控制

将存在多个工作环境的加速度计组合件温度控制受控对象描述为存在有界时变参数摄动和有界干扰的非线性时变不确定系统,提出了一种基于信号补偿的鲁棒温度控制方法,该方法设计的控制器由标称控制器和鲁棒补偿器组成.证明了闭环系统的鲁棒控制特性,实验结果显示所设计的控制系统能够在多个工作环境下实现高精度的鲁棒温度控制.     

一类非线性系统的输出反馈跟踪控制

研究了一类带有动态不确定性的非线性系统鲁棒输出跟踪控制问题.应用结合死区技术的反步方法,提出了一输出反馈跟踪控制方案.设计的鲁棒跟踪控制器能够使得跟踪误差在有限时间后收敛到原点的任意小邻域,同时保证闭环系统的其他信号有界.仿真例子验证了该控制方案的有效性.     

坦克炮控伺服系统的自适应鲁棒控制研究

针对一类坦克炮控伺服系统,充分考虑系统中的未知齿隙非线性及摩擦非线性,将这些未知非线性的影响表示成有界扰动项与未知非线性动态项之和,借助ARC(自适应鲁棒控制)的思想设计控制器,当未知非线性动态项为零时,控制器即为自适应控制器,保持系统稳定的同时实现对参考信号的精确跟踪,而当系统存在未知非线性动态项时,控制器具有很好的鲁棒性,保持系统所有信号有界的同时实现对参考信号的误差跟踪,且跟踪误差可以由设计参数的取值设定来任意调节,与现有结果相比,控制器的设计建立在充分考虑系统未知非线性的影响之上,从而避免了简单的把未知非线性影响简单的考虑成系统"总扰动"所造成的被控系统性能的损失。     

基于RBF神经网络的鲁棒因子滑模变结构多自由度机械臂精确跟踪控制研究

针对具有时变干扰的不确定多自由度机械臂,文章设计了基于RBF神经网络的含有鲁棒因子的滑模变结构高精度跟踪控制方法.针对时变干扰,设计鲁棒因子,将其嵌入滑模变结构控制器,克服了时变干扰对系统跟踪性能的影响.将RBF神经网络控制算法结合鲁棒因子滑模变结构控制,估计多自由度机械手臂系统的不确定因素.采用Lyapunov函数方法,证明了系统的稳定性.对比分析了计算力矩法滑模变结构控制方法,仿真结果证明,基于RBF神经网络的鲁棒因子滑模控制,针对具有时变干扰的含有不确定因素的多自由度机械手臂系统,具有较为精确的跟踪性能.     

基于信息压缩矩阵特征值映射的时变系统UD分解辨识算法

在系统辨识领域遗忘因子UD分解算法(一种通过对系统数据矩阵进行UD分解的在线辨识算法)具有对时变系统阶次和参数同步估计的优异性能,但传统的遗忘策略不能从根本上解决信息压缩矩阵数据过饱和问题,为了拓展现有UD分解算法在时变系统的适用范围,同时针对数据空间分布不均匀性,提出一种基于信息压缩矩阵特征值映射的UD分解辨识算法.从理论上分析辨识算法跟踪能力与参数估计矩阵有界性的对应关系,从而构造出一种基于信息压缩矩阵特征值映射的有界函数,特征值映射函数能够根据系统数据传递过程中信息量的大小动态调整遗忘因子,解决了参数辨识过程中数据过饱和及数据分布不均匀问题.仿真结果表明,相比于常规时变遗忘因子策略,带有特征值映射的UD分解算法能够更加准确跟踪系统参数的变化,且能够保证系统不是2N阶持续激励信号的情况下,也能对时变系统参数进行跟踪.     

Observer-based robust adaptive variable universe fuzzy control for chaotic system

A novel observer-base output feedback variable universe adaptive fuzzy controller is investigated in this paper. The contraction and expansion factor of variable universe fuzzy controller is on-line tuned and the accuracy of the system is improved. With the state-observer, a novel type of adaptive output feedback control is realized. A supervisory controller is used to force the states to be within the constraint sets. In order to attenuate the effect of both external disturbance and variable parameters on the tracking error and guarantee the states to be within the constraint sets, a robust controller is appended to the variable universe fuzzy controller. Thus, the robustness of system is improved. By Lyapunov method, the observer-controller system is shown to be stable. The overall adaptive control algorithm can guarantee the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. In the paper, we apply the proposed control algorithms to control the Duffing chaotic system and Chua’s chaotic circuit. Simulation results confirm that the control algorithm is feasible for practical application.     

Robust Tracking and Model Following Control with Zero Tracking Error for Uncertain Dynamical Systems

The problem of robust tracking and model following for a class of linear dynamical systems with time-varying uncertain parameters and disturbances is considered. A class of continuous (nonlinear) state feedback controllers is proposed for robust tracking of dynamical signals. The proposed robust tracking controllers can guarantee that the tracking error decreases asymptotically to zero in the presence of uncertain parameters and disturbances. A procedure for designing such a zero tracking state feedback controller is also introduced. Finally, a numerical example is given to demonstrate the validity of the results.     

Adaptive compensation of uncertain Euler–Lagrange systems with multiple time-varying actuator faults

This work proposes the command tracking problem for uncertain Euler–Lagrange (EL) systems with multiple partial loss of effectiveness (PLOE) actuator faults. Compared to existing fault-tolerant controllers for EL systems, the proposed adaptive controller accounts for parametric uncertainties in the system and multiple time-varying actuator fault parameters. The proposed method can also handle an infinite number of fault cases. The closed-loop fault-tolerant system is treated as a switched dynamical system, and a switched system stability is established using multiple Lyapunov functions. It is shown that all signals are bounded in each sub-interval and at the switching instances, and asymptotic tracking can be obtained only for a finite number of fault occurrences, whereas tracking error is bounded for the infinite case. Finally, a simulation example on a robotic manipulator is presented to show the effectiveness of the proposed method.     

一类不确定性非线性系统的状态反馈鲁棒自适应控制器的设计与分析

该文考虑一类具有一般不确定性和部分参数未知的非线性系统（1），设计出一种用于跟踪参考信号的状态反馈鲁棒自适应控制器，此控制器对系统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. 仿真结果表明，所设计的鲁棒自适应控制系统具有良好的跟踪性能， 而且控制量在容许控制的范围之内.     

Tracking control and generalized projective synchronization of a class of hyperchaotic system with unknown parameter and disturbance

In this paper, the tracking control and generalized projective synchronization of a class of hyperchaotic system with unknown parameter and disturbance are investigated. Based on the LaSalle’s invariant set theorem, a robust adaptive controller is contrived to acquire tracking control and generalized projective synchronization and parameter identification simultaneously. It is proved theoretically that the proposed scheme can allow us to drive the hyperchaotic system to any desired reference signals, including hyperchaotic signals, chaotic signals, periodic orbits or fixed value by the given scaling factor. The presented simulation results further demonstrate that the proposed method is effective and robust.     

Quantized-states-based adaptive control against unknown slippage effects of uncertain mobile robots with input and state quantization

An adaptive tracking design strategy based on quantized state feedback is developed for uncertain nonholonomic mobile robots with unknown wheel slippage effects. All state variables and control torques are assumed to be quantized by the state and input quantizers, respectively, in a network control environment. Thus, the quantized state feedback information is only available for the tracking control design. An approximation-based adaptive controller using quantized states is recursively designed to ensure the robust adaptive tracking against unknown wheel slippage effects where the quantized-states-based adaptive mechanism is derived to compensate for unknown wheel slippage effects, system nonlinearities, and quantization errors. The boundedness of the quantization errors and estimated parameters in the closed-loop system is analyzed by presenting some theoretical lemmas. Based on these lemmas, we prove the uniform ultimate boundedness of closed-loop signals and the convergence of the trajectory tracking error in the presence of wheel slippage effects. Simulations verify the effectiveness of the resulting tracking scheme.     

Triangular configuration of uncertain systems stabilizable by means of feedback controller

The uncertain linear systems with time-varying delays and limited measurable states variables are shown to have special configuration, called triangular configuration, to be stabilizable by means of state variable feedback controller. This configuration indicates the location of uncertainties among system parameters including input and output coefficients.     

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