一类不确定线性系统的鲁棒自适应控制

针对一类同时具有匹配不确定性和不匹配政治面目 确定性的线性系统,首先采用李雅普诺夫稳定性定理,结合基于矩阵不等式的鲁棒控制器设计方法和变结构控制方法,设计鲁棒控制器,使得闭环系统是二次渐近稳定的,然后,利用自适应参数估计方法,设计具有匹配不确定性范数界估计能力的鲁棒自适应控制器,保证闭环系统的一致终结有界,在此基础上,进一步考虑系统中可能的未知不确定性,分析闭环系统的鲁棒性,并得到了相应的控制器设计方法。     

冷带轧机厚控系统自适应鲁棒输出反馈动态控制器设计

冷带轧机厚控系统可被认为是一个受外界干扰的线性不确定时滞系统.本文首先设计了标称系统下的鲁棒输出反馈动态控制器,以改善闭环系统的动静态性能;其次,在系统不需要满足不确定性匹配条件的情况下,将参数和外部扰动不确定性综合考虑.应用Lyapunov稳定性理论设计了系统不确定性上界参数的自适应估计器和系统的自适应控制器,保证了闭环系统的渐近稳定性,减小了设计的保守性;两者结合实现了板带出口厚度的有效控制.最后通过一个仿真实例说明本文所提出的自适应鲁棒控制器的有效性.     

不确定非线性系统的多模反演滑模控制

对一般形式的仿射非匹配不确定非线性系统,研究了一种具有任意小跟踪误差的稳定控制器的新方法,结合反演（backstepping）设计和变结构控制,提出了反演变结构控制策略,对存在非匹配的不确定性和未知干扰的系统,设计的反演结构控制器实现了鲁棒输出跟踪,闭环系统在有限时间进入滑动模态,仿真算例证实了理论结果。     

不确定线性系统的鲁棒切换函数变结构控制

针对工程领域中常见的一类不匹配不确定系统,即不确定性不是发生在控制通道中的系统,如工程中具有执行器的控制系统,提出了一种建立鲁棒切换函数的方法,进面设计滑模变结构鲁棒控制策略,该策略可确保整个闭环系统的轨迹是一致最终有界并具有稳定性,最后以船舶减摇鳍控制系统为例,进行了变结构鲁棒控制器设计。     

基于自组织模糊CMAC神经网络的不确定系统的H∞鲁棒自适应控制

针对一类不确定系统，提出一种基于自组织模糊小脑模型（SOFCMAC）神经网络的H∞鲁棒自适应控制方法，通过设计标称系统的H∞控制器，并采用SOFCMAC神经网络在线对消系统的建模不确定性产生的误差，可保证不确定闭环稳定并具有H∞性能，证明了SOFCMAC神经网络H∞鲁棒自适应控制系统的稳定性，仿真算例表明了该方法的有效性。     

不确定非线性系统自适应鲁棒控制器设计

针对一类含不确定参数且存在未知扰动的非线性系统,设计了一种新型的自适应鲁棒控制器。用反演设计思想获得具有待定系数的控制器表达式,用BP神经网络自适应调节控制器的参数。仿真结果表明该控制器对系统参数的不确定性和未知扰动具有一定的鲁棒性,并能保证闭环系统全局稳定。     

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.     

时滞依赖不确定线性系统的鲁棒H∞控制

针对一类不确定性不满足匹配条件的具有时滞依赖不确定线性系统,研究了鲁棒H∞控制器的设计问题。基于线性矩阵不等式方法,通过构造合适的Lyapunov函数,给出了鲁棒H∞状态反馈控制器存在的充分条件,并通过求解相应的线性矩阵不等式完成了控制器的设计。仿真结果表明,所设计的控制器对所有容许的不确定性不仅使闭环系统渐近稳定,又能保证闭环系统满足一定的H∞性能指标,具有较好的抑制扰动的效果。     

时滞依赖不确定线性系统的鲁棒H_∞控制

针对一类不确定性不满足匹配条件的具有时滞依赖不确定线性系统,研究了鲁棒H∞控制器的设计问题.基于线性矩阵不等式方法,通过构造合适的Lyapunov函数,给出了鲁棒H∞状态反馈控制器存在的充分条件,并通过求解相应的线性矩阵不等式完成了控制器的设计.仿真结果表明,所设计的控制器对所有容许的不确定性不仅使闭环系统渐近稳定,又能保证闭环系统满足一定的H∞性能指标,具有较好的抑制扰动的效果.     

一类具有范数有界的非线性不确定性时滞系统的鲁棒H∞控制器设计

研究一类具有有界范数非线性不确定性的线性时滞系统的鲁棒Ｈ∞控制，提出一种鲁棒Ｈ∞控制器的设计方法。该控制器不仅能鲁棒镇定原不确定系统，还能保证闭环系统一定的Ｈ∞性能。     

Adaptive dynamic surface control for a class of output-feedback nonlinear systems with guaranteed ℒ∞ tracking performance

In this article, an output-feedback adaptive dynamic surface control (DSC) is proposed for a class of nonlinear systems. It is proved that, by using the new scheme, the explosion of the complexity problem in a traditional backstepping design can be eliminated, the semi-global stability of a closed-loop system can be guaranteed and, in particular, by choosing the design parameters and initialising the filters and the update law properly, we show that the ?_∞ performance of the system-tracking error can be achieved without over-parametrisation. Another advantage of the proposed scheme compared with those traditional backstepping control and current adaptive DSC schemes, whose adaptive control law is obtained through a series of steps recursively, is that the adaptive law is needed only at the first design step, and therefore significantly reduces the design procedure.     

一类对偶自适应预测函数控制算法

针对未知参数系统的自适应预测函数控制,模型尚未辨识完成或外界干扰造成的模型不准确,会严重影响控制效果,并产生较大的超调和波动.由此,提出一类对偶自适应预测函数控制(Dtml Adaptive Predictive Function Control,DAPFC)算法.在模型辨识的过程中,通过辨识误差的大小,利用对偶控制方法来调整原有自适应控制律,尽可能地获取未知参数信息并抑制由于模型失配造成的控制量的波动.改善了系统在模型失配时的控制效果,并具有较强的鲁棒性.仿真结果表明,该算法具有良好的控制品质.     

Variable Upper Bounding Approach for Adaptive-Robust Control in Robot Control

This paper presents a new adaptive-robust control law for robot manipulators with parametric uncertainty. Stability of the uncertain system has been guaranteed using the Lyapunov theory and the control law is derived by means of analytical approach. In this scheme, the manipulator parameters are determined with an estimation law, and both adaptive gain and additional control input are also updated as a function of the estimated value. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, and besides it eliminates the disadvantages of them.     

一种受限机械手的自适应力/位置控制方法*

对于受限机械臂,本文提出了一种自适应的力/位置控制方法。其实现是基于给出的新的降阶动力学模型,在反馈信号中引入力的累积误差信号,利用降阶模型的本身特性从而达到自适应力/位置控制的目的。给出的自适应律是通过跟踪误差信号来调节的。仿真结果证实了本方法的正确性。     

无人机三维航路自适应跟踪控制

针对无人机自动驾驶仪参数标称值偏离实际值情况下的航迹跟踪问题,提出一种无人机三维航路自适应跟踪导引律。首先在无人机自动驾驶仪参数无偏离的条件下,推导出能够跟踪三维航路的速度指令、航迹倾斜角指令和航迹方位角指令,并使用Lyapunov稳定性理论证明了跟踪系统全局渐进稳定;之后考虑自动驾驶仪参数标称值偏离实际值的情况,设计自适应算法在线估计自动驾驶仪参数,得到无人机三维航路自适应跟踪导引律。仿真实验表明所提出的自适应跟踪导引律能够使无人机在自动驾驶仪参数偏离条件下有效跟踪三维航迹。     

Model-Reference Fuzzy Adaptive Control as a Framework for Nonlinear System Control

The paper presents a general methodology of adaptive control based on fuzzy model to deal with unknown plants. The problem of parameter estimation is solved using a direct approach, i.e. the controller parameters are adapted without explicitly estimating plant parameters. Thus, very simple adaptive and control laws are obtained using Lyapunov stability criterion. The generality of the approach is substantiated by Stone-Weierstrass theorem, which indicates that any continuous function can be approximated by fuzzy basis function expansion. In the sense of adaptive control, this implies the adaptive law with fuzzified adaptive control parameters. The proposed control algorithm may be viewed as an extension of classical adaptive control for linear plants, but compared to the latter it provides higher adaptation ability and consequently better performance if the plant is nonlinear. The global stability of the control system is assured and the tracking error converges to the residual set that depends on fuzzification properties. The main advantage of the approach is simplicity that suits control engineers since wide range of industrial processes can be controlled by the proposed method. In the paper, the control of heat exchanger is performed.     

电弧炉的反步自适应模糊控制

电弧炉是具有三相强耦合、高度非线性和不确定性的复杂被控对象,并且目前对电弧炉的控制要求越来越严格,为此将反步控制与自适应模糊控制相结合,应用于电弧炉电极调节系统中.给出了反步自适应模糊控制系统的详细设计过程.用递推法设计反步控制量,用自适应模糊控制逼近反步控制量中的不确定项,设计出自适应模糊控制律.通过李亚普诺夫函数推导了模糊规则参数调整的自适应律.最后引入监督控制以减少模糊逼近误差.仿真结果表明:所提出的控制算法能有效地抑制弧长的扰动,具有较强的鲁棒性,从而使电弧炉电极调节系统拥有较好的动静态性能.     

基于间接自适应鲁棒的永磁同步电机电流控制器设计

针对永磁同步电机(PMSM)电流环非理想反电势的抑制问题,本文提出一种基于鲁棒最小二乘(RRLS)自适应律的间接自适应鲁棒控制(IARC)方法.该控制方法基于自适应鲁棒控制(ARC)理论,根据电机状态方程构造最小二乘型自适应律,加入修正因子增强自适应律对系统中扰动的鲁棒性.本文理论证明了该方法的稳定性.通过建立含有非理想反电势的电机模型,设计IARC电流控制器,并分析说明IARC具有比直接ARC更好的输出跟踪性能和扰动抑制能力.最后,通过仿真和实验验证了该方法的有效性.     

Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms

When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law. Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy.     

Adaptive composite suboptimal control for linear singularly perturbed systems with unknown slow dynamics

In this article, using singular perturbation theory and adaptive dynamic programming (ADP) approach, an adaptive composite suboptimal control method is proposed for linear singularly perturbed systems (SPSs) with unknown slow dynamics. First, the system is decomposed into fast‐ and slow‐subsystems and the original optimal control problem is reduced to two subproblems in different time‐scales. Afterward, the fast subproblem is solved based on the known model of the fast‐subsystem and a fast optimal control law is designed by solving the algebraic Riccati equation corresponding to the fast‐subsystem. Then, the slow subproblem is reformulated by introducing a system transformation for the slow‐subsystem. An online learning algorithm is proposed to design a slow optimal control law by using the information of the original system state in the framework of ADP. As a result, the obtained fast and slow optimal control laws constitute the adaptive composite suboptimal control law for the original SPSs. Furthermore, convergence of the learning algorithm, suboptimality of the adaptive composite suboptimal control law and stability of the whole closed‐loop system are analyzed by singular perturbation theory. Finally, a numerical example is given to show the feasibility and effectiveness of the proposed methods.