非匹配不确定系统的终端滑模分解控制

针对非匹配多变量模型不确定系统,提出了一种终端滑模分解控制方法.通过状态变换和去耦合处理将系统转换为块能控标准型,它由匹配扰动的值域空间子系统和稳定的非匹配扰动的零动态子系统组成.提出了特殊的终端滑模超曲面,采用滑模控制策略,使值域空间子系统的状态在有限时间内收敛至平衡点,随后非匹配扰动的零动态子系统渐近收敛至平衡点附近的邻域内,且建立了该邻域的范围与系统的非匹配不确定性范围之间的数学关系,并用于系统的设计与分析.所提方法对于维数较高的非匹配不确定系统的控制具有较大的意义,可简化设计,实现递阶控制.仿真实     

四旋翼飞行器的轨迹跟踪抗干扰控制

针对四旋翼飞行器轨迹跟踪问题中系统存在模型不确定和易受到外界扰动的情况,提出了基于切换函数的扩张状态观测器设计方法来对系统中的扰动进行估计,并将估计值与滑模控制器的设计相结合,实现了对系统中非匹配不确定性和匹配不确定性的抑制且实现了系统跟踪误差的一致最终有界.首先,根据变量间的耦合关系将飞行器系统模型分解为两个子系统模型,设计扩张状态观测器对子系统中的非匹配不确定性进行估计,并将估计值作为变量加入到切换函数的设计中;进而基于切换函数设计扩张状态观测器以估计经切换函数重构系统中的扰动,并在控制器中对扰动进行补偿.最后通过李雅普诺夫理论证明了控制系统的稳定性.通过仿真验证了本文提出的方法能够有效实现飞行器轨迹跟踪控制且能够抑止传统滑模控制的抖振现象.     

不确定控制系统的输出反馈镇定

本文研究具有匹配和非匹配不确定性系统的输出反馈镇定。在对不确定性的增长性作适当限制的条件下，利用本文引入输出反馈严格正实系统的概念，构造了保证团环系统在开球Ｂ上或在Ｒ＾ｎ上指数式稳定的光滑或解析输出反馈控制律。     

一类不确定非线性大系统的分散鲁棒控制器设计

基于Backstepping方法，设计了一类具有不确定性扰动和不确定性关联项的非线性大系统的分散鲁棒稳定控制器。非线性大系统的关联项为时变有界非线性函数且不确定性扰动以仿射非线性方程的形式引入。为了提高系统的控制效果。将：Backstepping递推设计方法与L2增益控制相结合，所设计的分散鲁棒控制器不仅使每个子系统的状态向量跟踪一个指定的期望轨迹。而且还使系统的不确定性干扰具有L2增益控制。     

带有界扰动的一类大型互联非线性系统的鲁棒分散控制

研究带有界扰动的一类大型互联非线性系统的鲁棒分散控制问题, 该系统的第i个子系统的标称模型具有相对阶r_i及指数稳定的零动态, 且每个子系统的互联项满足匹配条件. 通过子系统状态的线性变换得到鲁棒分散状态反馈控制器, 当该控制律作用于系统时, 系统的状态能够收敛到原点的一个小邻域内, 并给出仿真算例说明该结论的可行性和有效性.     

一类非匹配不确定性非线性系统的鲁棒镇定

研究了非线性系统存在非匹配不确定性时控制器的鲁棒镇定问题. 基于对象的模糊动态模型, 提出了一种状态反馈控制器的设计, 给出控制器在建模不确定性等各种非匹配不确定性存在下仍能够镇定非线性系统的一个充分条件. 仿真结果表明了设计方法的正确性.     

非完整移动机器人的连续时变鲁棒K指数镇定

针对带有参数不确定性动态非完整移动机器人的镇定问题,提出一个全局连续的时变鲁棒控制律.首先,使用全局可逆变换将系统的动力学模型转换成一个不确定性线性子系统和一个不确定性非线性子系统;然后,引入一个与初值有关的指数衰减项,将非线性子系统转换成带扰动项的线性系统;最后,设计鲁棒控制律将整个系统镇定到原点.与已有的控制器相比,所提出的控制器能同时获得连续性、渐近性和指数收敛速度,仿真结果也验证了这一点.     

一类非匹配不确定性系统的变结构控制

研究一类非匹配不确定性系统的变结构控制问题. 引入适当的状态变换, 将非匹配不确定系统描述成具有分级形式的两个子系统. 在保证第一个子系统二次渐近稳定的条件下, 利用线性矩阵不等式方法得到第二个状态变量的期望值, 并由第二个状态变量与其期望值之差构造滑动模, 并利用滑模运动的到达条件, 得到变结构控制律.     

一类非匹配不确定性系统的变结构控制

研究一类非匹配不确定性系统的变结构控制问题.引入适当的状态变换,将非匹配不确定系统描述成具有分级形式的两个子系统.在保证第一个子系统二次渐近稳定的条件下,利用线性矩阵不等式方法得到第二个状态变量的期望值,并由第二个状态变量与其期望值之差构造滑动模,并利用滑模运动的到达条件,得到变结构控制律.     

有向网络下仿射非线性多个体协同动力学系统的整体行为

本文讨论有向通讯网络条件下仿射非线性多个体动力学系统的结构及其整体行为．系统可以分解为独立基本子系统和非独立基本子系统．每个独立基本子系统将趋于自身的一致状态,非独立子系统的个体状态将趋于独立基本子系统一致状态为顶点的凸集内．系统所有个体的状态趋于一致的充分必要条件是存在唯一的独立基本子系统．这一结果涵盖了线性多个体动力学系统此前的研究结果．     

Decentralized robust controller design for a class ofinterconnected uncertain systems: with unknown bound of uncertainty

In this paper, a simple decentralized robust control scheme is proposed for a class of interconnected time-varying systems with uncertainties. The uncertainties may appear in the interconnections between the subsystems and also within the subsystems, and they are possibly nonlinear and time-varying. The uncertainties are bounded, but the bounds of the uncertainties are unknown in controller design. When certain matching conditions are satisfied for the uncertain interconnections and the uncertainties within the subsystems, the proposed decentralized control scheme guarantees the controlled system to converge exponentially with a prescribed degree toward the equilibrium of the system, or a residual ball around the equilibrium     

High gain observer for a class of non-triangular systems

This paper presents a high gain observer for a class of MIMO nonlinear systems involving some uncertainties. The latter is particularly composed of cascade subsystems where each subsystem is associated with a subset of the output variables, and assumes a triangular dependence on its own state variables and may depend on the state variables of all other subsystems. The main contribution consists in extending the available results to allow more interconnections between the subsystems. Of fundamental interest, it is shown that the underlying observation error exponentially converges to zero in the absence of uncertainties. Moreover, the observation error can be made as small as desired by properly specifying the observer design parameter in the case where uncertainties are considered.     

Pseudo-decentralized adaptive stabilization of large-scale feedforward nonlinear systems

In this paper, we propose a pseudo-decentralized adaptive control scheme for a class of large-scale feedforward nonlinear systems with unknown nonlinear effects within subsystems and unknown nonlinear interactions among subsystems. The local controller of each subsystem takes a nested saturation feedback, using the state of its own subsystem, and the saturation levels are tuned online in a switching manner via a set of switching logics, which requires some binary flag communication among subsystems. Global asymptotic regulation of the closed-loop states is achieved.     

Decentralized adaptive output-feedback control for a class of nonlinear large-scale systems with unknown time-varying delayed interactions

In this paper, the authors investigate a decentralized adaptive output-feedback controller design for large-scale nonlinear systems with input saturations and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions including all states of subsystems. This point is a main contribution of this paper compared with previous output-feedback control approaches which assume that the time-delayed bounding functions only depend on measurable output variables. The bounding functions are compensated by using appropriate Lyapunov–Krasovskii functionals and the function approximation technique based on neural networks. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.     

Decentralized sliding mode control for a class of nonlinear interconnected systems by static state feedback

In this paper, a class of interconnected systems is considered, where the nominal isolated systems are fully nonlinear. A robust decentralized sliding mode control based on static state feedback is developed. By local coordinate transformation and feedback linearization, the interconnected system is transformed to a new regular form. A composite sliding surface which is a function of the system state variables is proposed and the stability of the corresponding sliding mode dynamics is analyzed. A new reachability condition is proposed and a robust decentralized sliding mode control is then designed to drive the system states to the sliding surface in finite time and maintain a sliding motion thereafter. Both uncertainties and interconnections are allowed to be unmatched and are assumed to be bounded by nonlinear functions. The bounds on the uncertainties and interconnections have more general forms when compared with existing work. A MATLAB simulation example is used to demonstrate the effectiveness of the proposed method.     

Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

This paper presents a novel decentralized filtering adaptive constrained tracking control framework for uncertain interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, a piecewise constant adaptive law will generate total uncertainty estimates by solving the error dynamics between the host system and decentralized state predictor with the neglection of unknowns, whereas a decentralized filtering control law is designed to compensate both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. In the control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low‐pass filters, while the trade‐off between tracking and constraints violation avoidance is formulated as a numerical constrained optimization problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. The uniform performance bounds are derived for the system states and control inputs as compared to the corresponding signals of a bounded closed‐loop reference system, which assumes partial cancelation of uncertainties within the bandwidth of the control signal. Compared with model predictive control (MPC) and unconstrained controller, the proposed control architecture is capable of solving the tracking control problems for interconnected nonlinear systems subject to constraints and uncertainties.     

Neural-Network-Based Decentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems With Unknown Time-Varying Delays

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov–Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.      

An algorithm for the robust exponential stabilization of a class of switched systems

In this paper, we consider continuous‐time switched systems whose subsystems are linear, or, more generally, homogeneous of degree one. For that class of systems, we present a control algorithm that under certain conditions generates switching signals that globally exponentially stabilizes the switched system, even in the case in which there are model uncertainties and/or measurement errors, provided that the bounds of that uncertainties and errors depend linearly on the norm of the state of the system and are small enough in a suitable sense. We also show that in the case in which the measurement errors and the model uncertainties are bounded, the algorithm globally exponentially stabilizes the system in a practical sense, with a final error which depends linearly on the bounds of both the model uncertainties and the measurement errors. In other words, the closed‐loop system is exponentially input‐to‐state‐stable if one considers the perturbations and output measurements bounds as inputs. For switched linear systems, under mild observability conditions, we design an observer whose state‐estimation drives the control algorithm to exponentially stabilize the system in absence of perturbations and to stabilize it in an ultimately bounded way when the perturbations and the output measurement errors are bounded. Finally, we illustrate the behavior of the algorithm by means of simulations. Copyright © 2014 John Wiley & Sons, Ltd.