具有降阶模型的多变量鲁棒自适应前馈控制器

本文提出了一种多变量鲁棒自适应前馈控制器。该控制器采用低阶模型来控制参数未知的高阶多变量系统时,在可测干扰和有界不可测干扰作用下能保证自适应系统稳定运行。当可测干扰与系统输出之间存在未建模动态时,它可以对可测干扰实行有效的动静态补偿。当只有有界干扰作用时,它能使有界干扰对系统产生的影响最小。本文还给出了采用所提出的控制器控制某钢厂间歇式余热锅炉的仿真结果。     

具有强相容参数估计的多变量自校正前馈控制器

本文提出了一种新的自校正前馈控制器.该控制器具有如下优点:1)可以控制具有任意 传输延时结构的随机多变量系统;2)可以控制开环不稳定或非最小相位系统;3)不加积分作用 可以自适应消除偏差和可测干扰对输出的影响,消除跟踪误差;4)具有全局稳定特性;5)参数 估计具有强相容性.     

基于变量梯度法的磁悬浮控制系统的状态稳定性

磁悬浮系统是个典型的非线性系统,将非线性系统进行线性化后设计磁悬浮控制器,可以保证系统在平衡点附近稳定,但是当存在较大的干扰时,系统有可能失去稳定.本文分析单支点悬浮系统的稳定性,运用变量梯度法构造李雅普诺夫函数,推导出系统相对于平衡点的状态稳定保守区间,给出了系统的一个抗干扰程度,当磁悬浮系统受到较大干扰而偏离这一区间时,就应采取必要的自适应控制措施.最后用试验验证了这种控制方案的可行性.     

不确定离散时滞系统具有H_∞干扰抑制的保成本控制

针对一类用矩阵凸多面体形式表示的不确定线性离散时滞系统 ,提出一种通过无记忆状态反馈实现的具有 H∞ 干扰抑制的保成本控制。当对象存在不确定性以及外部干扰统计特性未知时 ,该控制器能保证闭环系统稳定和一定的线性二次型性能指标上界 ,同时具有 H∞ 范数下的干扰抑制作用。基于L yapunov稳定性理论 ,控制器设计可转化为线性矩阵不等式的可解性问题。数值仿真表明 ,该控制器实际是对最优保成本上界和干扰抑制能力的一种折衷     

多时滞不确定系统的模糊保性能H∞控制

针对多时滞不确定系统,利用T-S模型进行建模,提出了模糊保性能H∞控制问题.基于Lyapunov稳定理论,得到一种通过模糊状态反馈实现的具有干扰抑制的保性能H∞控制.当被控对象存在多时滞和不确定性及外部干扰统计特性未知时,该模糊控制器能保证闭环系统稳定和一定的二次型性能指标上界,同时具有H∞下的干扰抑制作用.控制器的设计可通过转化为线性矩阵不等式的求解问题.数值仿真表明,该控制器具有所期望设计的性能.     

一种新的自校正前馈控制器*

本文提出了一种新的单输入单输出自校正前馈控制器。该控制器采用了对系统输入、输出、参考输入以及可测干扰和偏差加权的二次性能指标。该控制器不仅不加积分作用可以消除可测干扰的影响,消除稳态跟踪误差和偏差,而且即使用于非最小相位系统也具有全局收敛特性。     

多变量自校正前馈控制器及其应用

本文提出了一种新的多变量自校正前馈控制器.该控制器不仅能完全消除可测干扰的影 响,而且能应用到多变量系统,实现自造应解耦控制.本文还介绍了该控制器如何实现多变量 解耦控制及其在多变量电加热炉上的应用.     

Duffing混沌系统的追踪控制

对Duffing系统进行控制,使之能够追踪任意参考信号.首先,在系统状态全部可测的情况下设计出控制器,并用Lyapunov方法证明对此控制器闭环系统大范围渐近稳定.然后考虑到实际中系统状态不完全可测,通过观测器获得误差信号及其对时间的导数,且观测器结构设计独特,并再次通过Lyapunov方法证明此时的控制器保证闭环系统大范围渐近稳定.最后对以上过程分别进行了数值仿真,进一步证明了该方法的有效性.     

不确定离散时滞系统具有H ∞干扰抑制的保成本控制

针对一类用矩阵凸多面体形式的不确定线性离散时滞系统，提出一种通过无记忆状态反馈实现的具有H∞干扰抑制的保成本控制。当对象存在不确定性以及外部干扰统计特性未知时，该控制器能保证闭环系统稳定和一定的线性二次型性能指标上界，同时具有H∞范数下的干扰抑制作用。基于Lyapunov稳定性理论，控制器设计可转化为线性矩阵不等式的可解性问题。数值仿真表明，该控制器实际是对最优保成本上界和干扰抑制能力的一种折衷。     

非最小相位逐维定位多模型自适应解耦控制器

针对多变量系统中多个参数同时跳变导致模型数目巨大、计算时闻长等问题.提出了采用逐维定位技术的多模型自适应解耦控制器.该方法将多维空间的并行寻优问题转化为多个一维空间的串行寻优问题,可大大减少系统模型集的数量.针对非最小相位系统,将系统的耦合作用视为可测干扰,采用前馈方法予以消除.最后给出全局收敛性分析.仿真结果表明当采用相同数目的模型时,其控制效果明显优于常规多模型控制器.     

Composite adaptive control for Euler-Lagrange systems with additive disturbances

In a typical adaptive update law, the rate of adaptation is generally a function of the state feedback error. Ideally, the adaptive update law would also include some feedback of the parameter estimation error. The desire to include some measurable form of the parameter estimation error in the adaptation law resulted in the development of composite adaptive update laws that are functions of a prediction error and the state feedback. In all previous composite adaptive controllers, the formulation of the prediction error is predicated on the critical assumption that the system uncertainty is linear in the uncertain parameters (LP uncertainty). The presence of additive disturbances that are not LP would destroy the prediction error formulation and stability analysis arguments in previous results. In this paper, a new prediction error formulation is constructed through the use of a recently developed Robust Integral of the Sign of the Error (RISE) technique. The contribution of this design and associated stability analysis is that the prediction error can be developed even with disturbances that do not satisfy the LP assumption (e.g., additive bounded disturbances). A composite adaptive controller is developed for a general MIMO Euler-Lagrange system with mixed structured (i.e., LP) and unstructured uncertainties. A Lyapunov-based stability analysis is used to derive sufficient gain conditions under which the proposed controller yields semi-global asymptotic tracking. Experimental results are presented to illustrate the approach.     

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.     

HGO-based decentralised indirect adaptive fuzzy control for a class of large-scale nonlinear systems

In this article, a novel high gain observer (HGO)-based decentralised indirect adaptive fuzzy controller is developed for a class of uncertain affine large-scale nonlinear systems. By the combination of fuzzy logic systems and an HGO, the state variables are not required to be measurable. The proposed feedback and adaptation mechanisms guarantee that each subsystem is able to adaptively compensate for interconnections and disturbances with unknown bounds. It is ascertained using a singular perturbation method that all the signals of the closed-loop large-scale system stand uniformly ultimately bounded and the tracking errors converge to tunable neighbourhoods of the origin. Simulation results of correlated double inverted pendulums substantiate the effectiveness of the proposed controller.     

一种新的间接自适应前馈控制算法及其应用

本文提出的单变量随机间接自适应控制算法适于控制大滞后的系统.它不仅能消除可测 干扰的影响,而且可以应用到多变量系统实现自适应解耦控制.该算法即使用于非最小相位 系统也具有全局收敛特性.本文还介绍了该算法在多变量电加热系统中的应用.     

一种新的多模自适应前馈控制器

本文针对高频焊管焊接过程的控制,提出了一种适用于具有多个确定性扰动对象的多模自适应前馈控制器,它的特点是综合了从参考模型取状态的MRAC、多输入前馈控制和开关-比例-保持模式控制各自的优点。该控制器不仅可以消除可测干扰的影响,具有较好的控制性能,而且也适用于非最小相位系统。文中针对焊接过程的数学模型,给出了仿真实例研究。     

Adaptive control of rigid body satellite

The minimal controller synthesis （MCS） is an extension of the hyperstable model reference adaptive control algorithm. The aim of minimal controller synthesis is to achieve excellent closed-loop control despite the presence of plant parameter variations, external disturbances, dynamic coupling within the plant and plant nonlinearities. The minimal controller synthesis algorithm was successfully applied to the problem of decentralized adaptive schemes. The decentralized minimal controller synthesis adaptive control strategy for controlling the attitude of a rigid body satellite is adopted in this paper. A model reference adaptive control strategy which uses one single three-axis slew is proposed for the purpose of controlling the attitude of a rigid body satellite. The simulation results are excellent and show that the controlled system is robust against disturbances.     

Adaptive fault‐tolerant control for a nonlinear flexible aircraft wing system

Fault‐tolerant control problems have been extensively studied in all kinds of control systems. However, there is little work on fault‐tolerant control for distributed parameter systems. In this paper, a novel adaptive fault‐tolerant boundary control scheme is proposed for a nonlinear flexible aircraft wing system against actuator faults. The whole system is regarded as a distributed parameter system, and the dynamic model of the flexible wing system is described by a set of partial differential equations (PDEs) and ordinary differential equations (ODEs). The proposed controller is designed by using the Lyapunov's direct method and adaptive control strategies. Based on the online estimation of actuator faults, the adaptive controller parameters can update automatically to compensate the actuator faults of the system. Besides, a fault‐tolerant controller is also developed for this system in the presence of external disturbances. Differing from existing works about adaptive fault‐tolerant control, the adaptive controller presented in this paper is designed for a distributed parameter system. Finally, numerical simulations are carried out to illustrate the effectiveness of the proposed control scheme.     

Adaptive tracking control of manipulators: Theory and experiments

This paper considers the trajectory tracking problem for uncertain robot manipulators and proposes two adaptive controllers as solutions to this problem. The first controller is derived under the assumption that the manipulator state is measurable, while the second strategy is developed for those applications in which only position measurements are available. The adaptive schemes are very general and computationally efficient since they do not require knowledge of either the mathematical model or the parameter values of the manipulator dynamics, and are implemented without calculation of the robot inverse dynamics or inverse kinematic transformation. It is shown that the control strategies ensure uniform boundedness of all signals in the presence of bounded disturbances, and that the ultimate size of the tracking errors can be made arbitrarily small. Experimental results are presented for a PUMA 560 manipulator and demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed controllers.