一类不确定非线性系统的自适应输出反馈镇定

研究了一类不确定非线性系统的全局自适应输出反馈镇定问题. 由于不确定控制系数和未知线性增长率的存在, 这个问题比较复杂且很难解决. 本文引入一个新的在线调节的动态增益, 并基于此设计了高增益K-滤波器用于重构系统的状态. 然后, 受广义控制方法的启发, 发展了反推法并设计了自适应输出反馈镇定控制器. 结果表明, 通过选择恰当的设计参数可以保证闭环系统的全局稳定性. 给出的仿真算例验证了本文理论结果的正确性.     

一类不确定系统的动态输出反馈镇定

考察同时含有匹配和非匹配不确定性的一类非线性系统的输出反馈镇定问题。首先讨论仿射非线性的情形,然后讨论一类非仿射非线性情形。分别为所论系统构造了输出反馈动态补偿器,该补偿器可以实现对所论非线性不确定系统的动态输出反馈指数的镇定。     

基于双增益方法的不确定非线性系统的输出反馈调节问题

研究一类具有多种不确定性的非线性系统的全局输出反馈调节问题.所研究系统的一个显著特点是非线性项被未知增长率和多项式形式的输出函数的乘积界定,难点是在输出受不确定参数摄动的情况下如何抑制非线性项.提出一种改进的双增益方法来设计输出反馈控制器,可以确保闭环系统所有信号全局一致有界并且原系统状态收敛到零.最后,采用质量弹簧机械系统的输出反馈镇定问题来说明控制策略的有效性.     

具有输入饱和的非线性关联大系统的分散控制

考虑了一类具有输入饱和的不确定非线性关联大系统的分散输出反馈鲁棒镇定问题,利用Riccati方程的方法和矩阵的Moore-Penrose逆给出了这类系统的一种分散输出反馈鲁棒镇定控制器的设计方法.同时,考虑了一类具有输入饱和的不确定非线性相似关联大系统,利用相似系统的结构特点,简化了分散输出反馈鲁棒镇定的条件.     

一类非线性系统的自适应抗测量噪声的输出反馈镇定

本文研究一类非线性系统的自适应抗测量噪声的输出反馈镇定问题. 所研究的非线性系统输出中存在正的且 有界的乘性噪声. 非线性项的增长率为一个未知常数乘以输出的幂函数加上带有时滞输出的幂函数. 首先, 证明一个矩 阵不等式. 其次, 设计含有3个时变增益的输出反馈控制器, 并给出增益的自适应律, 然后, 构造适当的Lyapunov-Krasovskii 泛函, 给出确保闭环系统渐近稳定的充分条件. 最后, 仿真实验验证该方法的可行性和有效性.     

随机非完整链式系统的自适应状态反馈镇定

基于反步技术,对带有不确定非线性项和不确定非线性系数的随机非完整链式系统,设计了自适应状态反馈镇定器.给出了能够保证系统依概率几乎渐近稳定到平衡点的切换控制.最后用仿真验证了控制器的有效性.     

不确定组合大系统的自适应分散镇定控制

考虑具有非线性关联作用的不确定时变线性组合大系统的自适应分散镇定问题.针对 系统不确定界完全未知的情形,首先从理论上证明了可设计自适应鲁棒分散控制器确保受控系 统渐近稳定;进而从工程实际应用的角度,给出了确保受控系统实用稳定的自适应鲁棒分散控 制器的设计方案.仿真说明该设计方案是有效的.     

模糊不确定时滞系统的静态输出的反馈镇定:迭代线性矩阵不等式方法

对一类不确定非线性时滞系统利用模糊T＿S模型进行建模,研究了静态输出反馈镇定问题.用矩阵不等式的形式给出了模糊T＿S不确定时滞系统可通过静态输出反馈镇定的充分条件.并将矩阵不等式的条件转化为迭代线性矩阵不等式(ILMI),并给出相应的算法.     

一类非线性MIMO不确定系统的动态输出反馈镇定

考察其标称系统的相对阶大于{1,1,…,1}同时含匹配和非匹配不确定性的MIMO 非线性系统的动态输出反馈镇定问题.文中直接用Lyaunov方法构造一类输出反馈动态补 偿器,该补偿器可以实现对所论非线性不确定系统的动态输出反馈渐近镇定.     

一类非线性系统的自适应鲁棒输出反馈镇定

讨论了一类不确定非线性系统的鲁棒输出反馈镇定问题,其不确定性是部分已知 的.文中所得连续自适应鲁棒输出反馈控制器确保闭环系统终极一致有界.与已有文献结果 相比,关于未知参数估计的自适应律是连续的,而且闭环系统解的存在性在通常情况下能被 保证.进而,由于输出反馈控制器和自适应律的连续性,使得自适应鲁棒输出反馈控制器在 实际控制问题中易于实现,且使系统具有良好的品质.最后,通过数值算例进一步说明该文 的设计方案是有效的.     

Robust adaptive output feedback control of a class of discrete‐time nonlinear systems with nonlinear uncertainties and unknown control directions

In this paper, robust adaptive output feedback control is studied for a class of discrete‐time nonlinear systems with functional nonlinear uncertainties of the Lipschitz type and unknown control directions. In order to construct an output feedback control, the system is transformed into the form of a nonlinear autoregressive moving average with eXogenous inputs (NARMAX) model. In order to avoid the noncausal problem in the control design, future output prediction laws and parameter update laws with the dead‐zone technique are constructed on the basis of the NARMAX model. With the employment of the predicted future outputs, a constructive output feedback adaptive control is proposed, where the discrete Nussbaum gain technique and the dead‐zone technique are used in parameter update laws. The effect of the functional nonlinear uncertainties is compensated for, such that an asymptotic tracking performance is achieved, whereas other signals in the closed‐loop systems are guaranteed to be bounded. Simulation studies are performed to demonstrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Robust adaptive fuzzy backstepping output feedback tracking control for nonlinear system with dynamic uncertainties

In this paper, an adaptive fuzzy output feedback control approach based on backstepping design is proposed for a class of SISO strict feedback nonlinear systems with unmeasured states, nonlinear uncertainties, unmodeled dynamics, and dynamical disturbances. Fuzzy logic systems are employed to approximate the nonlinear uncertainties, and an adaptive fuzzy state observer is designed for the states estimation. By combining backstepping technique with the fuzzy adaptive control approach, a stable adaptive fuzzy...     

Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive neural network (NN) backstepping output feedback control approach is proposed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, unmodeled dynamics, dynamical uncertainties and without requiring the measurements of the states. The NNs are used to approximate the unknown nonlinear functions, and a filter observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive NN output feedback control approach is constructed. It is mathematically proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. The simulation example and comparison results further justify the effectiveness of the proposed approach.     

具有未建模动态和输出约束系统的自适应输出反馈控制

针对一类具有未建模动态和输出约束的输出反馈非线性系统, 提出一种自适应输出反馈动态面控制方案. 利用神经网络逼近未知连续函数, 分别设计K滤波器和动态信号估计不可测量的状态, 并处理动态不确定性. 引入障碍李雅普诺夫函数并设计自适应控制器以保证BLF有界, 从而实现输出约束. 理论分析表明, 闭环控制系统是半全局一致终结有界的, 且满足输出约束, 仿真结果验证了所提出方案的有效性.     

Passivity and disturbance attenuation via output feedback foruncertain nonlinear systems

The authors address the problem of disturbance attenuation with internal stability via output feedback for a class of passive systems with uncertainties. The problem is approached by means of adaptive output feedback control which does not require any state observer. The results obtained extend an earlier result of Steinberg and Corless (1985). Sufficient conditions are proposed under which a nonlinear system can be made locally or globally passive via output feedback     

Adaptive actuator failure compensation for multivariable feedback linearizable systems

A feedback linearization‐based adaptive control scheme is developed for multivariable nonlinear systems with redundant actuators subject to uncertain failures. Such an adaptive controller contains a direct adaptive actuator failure compensator to compensate the uncertain actuator failure, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. The key new design feature is the estimation of both the failure patterns and the failure values, for direct adaptive actuator failure compensation, newly developed for multivariable feedback linearizable nonlinear systems. With direct control signal adaptation, the adaptive failure compensation design ensures closed‐loop stability and asymptotic output tracking in the presence of actuator failure uncertainties. Simulation results from an application to attitude control of a near‐space vehicle dynamic model are presented to verify the desired system performance with adaptive actuator failure compensation. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive hierarchical control for output feedback systems

In this paper, we address the problem of adaptive hierarchical control for a class of so-called uncertain output feedback systems. The proposed approach is to design an adaptive output interface dynamic by estimating the uncertainties. With the interface connected to the uncertain nonlinear system and a linear abstract system, the system could track approximately the abstraction. Finally, two examples are presented to illustrate our approach.     

Multiple model-based event-triggered adaptive control of a class of discrete-time nonlinear systems

In this study, the problem of event-triggered-based adaptive control (ETAC) for a class of discrete-time nonlinear systems with unknown parameters and nonlinear uncertainties is considered. Both neural network (NN) based and linear identifiers are used to approximate the unknown system dynamics. The feedback output signals are transmitted, and the parameters and the NN weights of the identifiers are tuned in an aperiodic manner at the event sample instants. A switching mechanism is provided to evaluate the approximate performance of each identifier and decide which estimated output is utilised for the event-triggered controller design, during any two events. The linear identifier with an auxiliary output and an improved adaptive law is introduced so that the nonlinear uncertainties are no longer assumed to be Lipschitz. The number of transmission times are significantly reduced by incorporating multiple model schemes into ETAC. The boundedness of both the parameters of identifiers and the system outputs is demonstrated though the Lyapunov approach. Simulation results demonstrate the effectiveness of the proposed method.     

Adaptive output feedback control for nonholonomic systems with uncertain chained form

The output feedback adaptive control problem is investigated for nonholonomic systems with strongly nonlinear uncertainties and unknown virtual control directions. A nonlinear output feedback switching controller based on the output measurement of the first subsystem is employed in order to make the state scaling effective and ensure the convergence of the system states. The novel observer/estimator is introduced for state and unknown parameter estimates. The integrator backstepping technique by the use of a constructive recursive is applied to the design of the adaptive controller and to overcome the unknown virtual control directions. The simulation result validates the effectiveness of the proposed scheme.     

Adaptive output control of uncertain nonlinear systems with non-symmetric dead-zone input

This paper deals with the adaptive output feedback control problem of a class of uncertain nonlinear systems with an unknown non-symmetric dead-zone nonlinearity. The nonlinear system considered here is dominated by a triangular system without zero dynamics satisfying polynomial growth in the unmeasurable states. An adaptive control scheme is developed without constructing the dead-zone inverse. The proposed adaptive control scheme requires only the information of bounds of the slopes and the breakpoint of dead-zone nonlinearity. The novelty of this paper is that a universal-type adaptive output feedback controller is numerically constructed by using a sum of squares (SOS) optimization algorithm, which ensures the boundedness of all the signals in the adaptive closed-loop without knowing the growth rate of the uncertainties. An example is presented to show the effectiveness of the proposed approach.