状态时滞时变不确定系统的鲁棒H∞ 输出反馈控制器设计

主要研究了存在状态滞后的线性时变不确定时滞系统的鲁棒∞控制分析和综合问题,给出了对所有容许不确定性,被控对象可二次镇定和满足从干扰输入到控制输出的Ｈ∞范数界约束的动态输入出反馈鲁棒Ｈ∞控制分析结果,将不确定时滞系统的鲁棒Ｈ∞输出反馈控制器设问题等价对两个线性时不变系统的状态反馈标准Ｈ∞控制问题,并由此得到反馈阵和观测阵,了终得到鲁棒Ｈ∞控制器综合设计方法。     

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.     

具有滞后输入的不确定系统的鲁棒镇定

本文研究具有滞后输入的不确定系统的鲁棒镇定问题，导出了系统可以用一个无记忆状态反馈控制律鲁棒镇定的条件，据此，提出了一个鲁棒稳定化控制器的设计方法．     

时变时滞不确定系统的鲁棒输出反馈控制

研究了时变时滞不确定系统基于状态观测器的动态输出反馈实现鲁棒镇定的分 析和综合问题.所研究的系统不仅同时包含时变状态时滞和时变控制时滞,而且包含时变未 知且有界不确定参数.提出了确保该系统可通过输出反馈鲁棒镇定的充分条件,并将该充分 条件转化为线性矩阵不等式(LMI)问题,最终通过求解两个LMI来构造输出反馈控制律.     

一类带不确定输入动态非线性系统的鲁棒H∞控制

研究一类带不确定输入动态非线性系统的H∞控制问题.在输入动态存在的条件下, 利用反传设计方法构造了鲁棒状态反馈控制器,使得闭环系统在零初始状态下从干扰到输出的 L2增益任意小,同时在干扰输入恒为零时闭环系统是全局渐近稳定的.     

输出反馈实现一类带不确定输入动态非线性系统的鲁棒几乎干扰解耦

研究一类带不确定输入动态非线性系统的输出反馈干扰抑制问题并基于观测器给出了输出反馈控制器 构造性的设计方法.所设计的控制器具有对可允许不确定动态的鲁棒性.不仅在L₂增益意义上抑制了干扰对输出 的影响,同时在ISS镇定意义上抑制了干扰对状态的影响.     

一类不确定切换系统的鲁棒状态反馈镇定

研究了一类扰动项不满足匹配条件的不确定切换系统的鲁棒镇定问题.在每个子系统均不能镇定的情况下,利用完备性条件和多李雅普诺夫函数方法,分别得到了不确定切换系统可镇定的充分条件.状态矩阵和控制输入矩阵同时带有时变、未知且有界的不确定性,基于凸组合技术和LMI方法,设计出鲁棒状态反馈控制器及相应的切换策略,使得闭环系统在其平衡点处是渐近稳定的.最后仿真结果表明所设计的控制器及切换策略的正确有效性.     

一类不确定线性时滞系统的输出反馈鲁棒镇定

研究一类不确定线性时滞系统的输出反馈鲁棒镇定问题,其中不确定性不必满足匹配条件。以二次Lyapunov泛函保证系统的渐近稳定性,利用线性矩阵不等式给出了系统可以利用动态输出反馈鲁棒镇定的充分条件。当此条件成立时,基于线性矩阵不等式的解构造了全阶动态输出反馈镇定控制器。     

不确定广义模糊系统的鲁棒模糊H∞控制器设计

研究了不确定广义模糊系统鲁棒H∞状态反馈控制器和动态输出反馈控制器设计问题。在E确定其它系数矩阵均存在不确定性情况下，给出鲁棒模糊H∞状态反馈控制器和动态输出反馈控制器存在的充分条件。鲁棒H∞状态反馈控制律的设计可能通过求解线性矩阵不等式得到，而动态输出反馈鲁棒H∞控制器可通过定义新变量得到，所求控制器使闭环系统对所有的不确定性稳定且满足H∞性能指标γ。     

具有扰动输入的不确定性非线性系统的输出调节极限性能

本文研究了一类具有扰动输入的不确定性非线性系统的输出调节问题, 给出了该类系统在最差的不确定性参数和扰动输入情况下系统输出调节的极限性能. 所讨论的非线性系统是可镇定非最小相位系统, 并且该系统的零动态由“鲁棒输入对状态稳定(robust input-to-state stable)部分”和“不稳定但可镇定部分”组成. 假设系统的不确定性参数和扰动输入分别以非线性函数和仿射形式同时出现在系统零动态的鲁棒输入对状态稳定部分和系统的可线性化部分, 而且其可线性化部分的不确定性具有下三角形结构形式. 该系统输出调节问题的性能以其输出信号能量作为度量. 对于上述非线性系统, 在最差的不确定性参数和扰动输入情况下, 输出调节问题的极限性能只取决于镇定其零动态“不稳定部分”所需的最小能量.     

Decentralized robust adaptive control of nonlinear systems withunmodeled dynamics

The authors present a decentralized robust adaptive output feedback control scheme for a class of large-scale nonlinear systems of the output feedback canonical form with unmodeled dynamics. A modified dynamic signal is introduced for each subsystem to dominate the unmodeled dynamics and an adaptive nonlinear damping is used to counter the effects of the interconnections. It is shown that under certain assumptions, the proposed decentralized adaptive control scheme guarantees that all the signals in the closed-loop system are bounded in the presence of unmodeled dynamics, high-order interconnections and bounded disturbances. Furthermore, by choosing the design constants appropriately, the tracking error can be made arbitrarily small regardless of the interconnections, disturbances, and unmodeled dynamics in the system. An illustration example demonstrates the effectiveness of the proposed scheme     

Robust output feedback control for a class of nonlinear systems with input unmodeled dynamics

The robust global stabilization problem of a class of uncertain nonlinear systems with input unmodeled dynamics is considered using output feedback, where the uncertain nonlinear terms satisfy a far more relaxed condition than the existing triangulartype condition. Under the assumption that the input unmodeled dynamics is minimum-phase and of relative degree zero, a dynamic output compensator is explicitly constructed based on the nonseparation principle. An example illustrates the usefulness of the proposed method.     

A note on the robustness of high-gain-observer-based controllers to unmodeled actuator and sensor dynamics

It is shown that a nonlinear output feedback stabilizing controller, which combines a globally bounded state feedback controller with a high-gain observer, is robust with respect to unmodeled fast actuator and sensor dynamics. The actuator and sensor dynamics need to be sufficiently fast relative to the dynamics of the nominal closed-loop system under state feedback, but they need not be faster than the observer dynamics.     

Adaptive fuzzy backstepping output feedback control for strict feedback nonlinear systems with unknown sign of high-frequency gain

In this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of SISO nonlinear strict-feedback systems with unknown sign of high-frequency gain and the unmeasured states. The nonlinear systems addressed in this paper are assumed to possess the unmodeled dynamics, dynamical disturbances and unknown nonlinear functions, where the unknown nonlinear functions are not linearly parameterized, and no prior knowledge of their bounds is available. In the recursive designing, fuzzy logic systems are used to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unmodeled dynamics and the unknown sign of the high-frequency gain, respectively. Based on Lyapunov function method, a stable adaptive fuzzy output feedback control scheme is developed. It is mathematically proved that the proposed adaptive fuzzy control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded, the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples.     

On the design of stabilizing controllers for singularly perturbedsystems

The robustness of output feedback control designs for singularly perturbed systems based on the reduced systems is discussed. A frequency-domain approach is presented to determine the conditions for the stability of linear time-invariant systems subject to neglected high-frequency dynamics. In contrast with the qualitative analyses for robust stability that have appeared in the literature, this approach gives an explicit, computable bound on unmodeled dynamics which does not destabilize the systems. Stability conditions for various feedback control schemes are presented. It is noted that the approach, extended to systems with mixed singular-regular perturbations, can be used to derive the robust stability condition     

Robust stabilization for systems with rank-one uncertainty structure

This paper solves a class of robust control problems, with output feedback, for systems with mixed parametric uncertainty and unmodeled dynamics. This class of problems is characterized by a special rank-one assumption on the transfer matrix of the nominal plant. Under this assumption, the robust stabilization problem is reduced to a convex feasibility problem involving linear matrix inequalities. The data necessary to assemble these inequalities can be readily obtained from a state-space model of the nominal plant.     

Robust adaptive output‐feedback control for nonlinear systems with output unmodeled dynamics

In this paper, for a class of uncertain nonlinear systems in the presence of inverse dynamics, output unmodeled dynamics and nonlinear uncertainties, a robust adaptive output‐feedback controller design is proposed by combining small‐gain theorem, changing supply function techniques with backstepping methods. It is shown that all the signals of the closed‐loop system are uniformly bounded in biased case, and the output can be regulated to a small neighborhood of the origin in unbiased case. Furthermore, under some additional assumptions, an asymptotical result is obtained. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

Adaptive regulation of nonlinear systems with unmodeled dynamics

A feedback linearization design is presented which includes unknown parameters and unmodeled dynamics. An adaptive update law which counteracts the effects of unknown parameters is shown to be robust to the unmodeled dynamics. The proposed design methodology is based on a conceptually simple stability analysis. Conditions are given for global stability of an adaptive control law designed for the reduced-order model of a class of nonlinear plants. In the presence of unmodeled dynamics, the regulation property is preserved in a stability region. The size of the region is estimated using bounds that not only prove robustness, but also allow a comparison between adaptive and nonadaptive nonlinear controls     

Robust adaptive quantized DSC of uncertain pure‐feedback nonlinear systems with time‐varying output and state constraints

In this paper, the problem of neural adaptive dynamic surface quantized control is studied the first time for a class of pure‐feedback nonlinear systems in the presence of state and output constraint and unmodeled dynamics. The considered system is under the control of a hysteretic quantized input signal. Two types of one‐to‐one nonlinear mapping are adopted to transform the pure‐feedback system with different output and state constraints into an equivalent unconstrained pure‐feedback system. By designing a novel control law based on modified dynamic surface control technique, many assumptions of the quantized system in early literary works are removed. The unmodeled dynamics is estimated by a dynamic signal and approximated based on neural networks. The stability analysis indicates that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the output and all the states remain in the prescribed time‐varying or constant constraints. Two numerical examples with a coarse quantizer show that the proposed approach is effective for the considered system.