基于模糊逻辑系统的输出跟踪控制问题

针对一类未知的非线性互联大系统，设计间接自适应模糊控制器以实现跟踪控制，采用模糊控制，模糊逻辑逼近和模糊滑模控制相结合的方法，对维数较低的子系统未知动态和维数较高的互联项未知动态分别采用两类模糊规则进行逼近，对系统的外部干扰及模糊逼近误差采用模糊滑模控制予以抵消，基于Lyapunov方法实现模糊系统中的参数自适应律并在线调节，所设计的间接自适应控制器使系统在Lyapunov意义下稳定，且跟踪误差趋近于0，仿真结果表明了该设计方法的正确性。     

一类不确定互联系统的鲁棒分散自适应控制

利用反演法的系统性和结构特点,研究了一类含有非线性参数的不确定非线性互联系统的鲁棒分散自适应控制问题.首先,在较直观、较一般的假定下,根据系统的结构特点利用反演法设计出其控制器和自适应律,并且每个子系统控制器和自适应律的构成只利用了本身系统的状态信息,即所谓的分散控制;其次,利用Lyapunov理论证明了所设计的控制器和自适应律使得被控系统的状态及参数估计误差一致终极有界.最后,算例仿真验证了所设计的控制算法的有效性.     

超级电容负载的耦合并联充电系统无源分布式协同控制

储能式轻轨车辆采用超级电容作为动力源,其并联充电系统每个充电机不可避免的存在着线路不对称、元器件老化及工艺误差等因素,使得输出电流不均衡.本文将并联充电系统的电流均衡问题抽象成一类非线性耦合动态互联系统的输出同步问题,结合系统的无源性,根据最近邻原则,借助饱和函数,设计了保证控制输入有界的输出同步控制器.利用子系统存储...     

一类非线性切换系统任意切换采样控制设计

本文针对一类具有非严格反馈形式的非线性切换系统,在输出只在采样点可获得的情况下,提出了一种基于模糊采样观测器的自适应输出反馈控制方法.该方法降低了现有任意切换控制研究结果中因共同控制思想导致的控制器设计的保守性,避免了迭代过程对虚拟控制的反复求导引发的计算爆炸现象及控制器高增益的弊端.切换的自适应律突显了每个子系统的特性,建立的采样控制器节约了信息传输资源.共同Lyapunov函数理论确保了相应闭环切换系统所有变量在任意切换信号下的一致有界性.     

考虑量化输入和输出约束的互联系统自适应分散跟踪控制

本文考虑具有量化输入和输出约束的一类非线性互联系统的自适应分散跟踪控制设计.分别针对量化参数已知和未知两种情况,基于反推(Backstepping)设计法,利用神经网络逼近特性,设计自适应分散跟踪控制策略.通过定义新的未知常量和非线性光滑函数,设计自适应参数估计项来消除未知互联项对系统的影响.进一步考虑量化参数未知的情...     

互联大系统动态输出反馈多重叠鲁棒分散关联镇定

针对一类具有网型拓扑结构的互联大系统,提出一种动态输出反馈多重叠鲁棒分散关联镇定方法.该方法将系统状态空间加以扩展,在扩展空间中将其分解为按循环逆序排列的一系列两两子系统对,并为每个子系统对分别设计使其关联稳定的鲁棒分散动态输出反馈控制器,将这些多重叠设计的控制器再收缩回原空间,实现控制律的协调.将该方法应用到一个四区域网型电力系统控制设计中,仿真结果验证了所提出方法的可行性和优越性.     

一类非线性互联系统的间接自适应模糊滑模跟踪控制

基于模糊逻辑逼近原理,针对非线性动态未知互联系统,设计一种新的模糊分散控制 器.讨论了互联项满足或不满足一般性约束条件的两种情形,用不同方法来补偿其对大系统的影 响.同时利用模式转换函数来实现间接自适应控制与模糊滑模控制之间的转换,使系统的状态在 有界闭集内变化,并依据Lyapunov方法证明了闭环系统稳定,跟踪误差收敛到零的一个小邻域 内.仿真结果证明所设计的模糊控制器的有效性.     

机器人多级自适应控制器鲁棒性分析

本文给出了机器人关节系统多级自适应控制器的鲁棒性分析.分析表明机器人多级自适应控制系统即使子系统间存在相互关联项和子系统存在未建模特性.采用σ-修正法也能保证系统的输出有界.     

一类互联机器人系统鲁棒分散自适应控制

研究一类存在模型不确定性和外部扰动的互联机器人系统的控制问题.控制器由一般线性控制器,线性自适应控制器和非线性自适应控制器综合构成.通过Lyapunov理论证明设计的鲁棒分散自适应控制器能够有效地克服不确定性对系统的影响,实现闭环系统的渐近轨迹跟踪控制.最后给出一个仿真例子进一步验证控制器的有效性.     

使用低阶参考模型的离散多变量MRACS

本文提出了一种高阶被控对象跟随低阶参考模型的离散多变量模型参考自适应控制系统(MRACS)的设计方法,它利用数字滤波器将系统降阶,通过求解p个一次代数方程而确定的自适应控制输入信号,能够使多变量被控对象的每个多输入单输出(MISO)子系统的输出渐近收敛到对应的单输入单输出(SISO)低阶参考模型的输出,提出的自适应控制器不仅避免了引进正反馈和辅助信号,而且实现了自适应解耦控制,仿真结果表明该控制器具有较好的控制性能。     

Decentralized adaptive iterative learning control for interconnected systems with uncertainties

In many applications,the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them.This decomposition is motivated by the ease and flexibility of the controller design for each subsystem.In this paper,a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties.The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries.The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems.The adaptive parameters are updated along iteration axis to compensate the interconnections among subsystems.It is shown that by using the proposed decentralized controller,the states of the subsystems can track the desired reference model states iteratively.Simulation results demonstrate that,utilizing the proposed adaptive controller,the tracking error for each subsystem converges along the iteration axis.     

ADAPTIVE COORDINATED DECENTRALIZED CONTROL OF STATE DELAYED SYSTEMS WITH ACTUATOR FAILURES

This paper develops an adaptive state feedback coordinated decentralized control scheme for a class of dynamic systems with state delay in subsystems and in the interconnections and in the presence of unknown actuator failures in each subsystem. The main contributions of this paper are the development of a new controller parametrization which attempt to anticipate the future states and failures, the introduction of an appropriate Lyapunov‐Krasovskii type functional to design the adaptation algorithms, and a stability proof.     

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.     

关联大系统的分散自适应控制：孤立子系统动力学已知情况

本文研究了一类关联大系统的分散自适应控制问题，拓广了分散自适应控制的应用范围，在孤立子系统（ＭＩＭＯ）动力学信息已知，关联强度未知的情况下，论文提出了结构相当简单的分散自适应控制策略，它可保证闭环的分散自适应控制系统具有良好的动力学行为；系统的所有信号都全局有界，进一步，在状态调节情况下，关联大系统状态趋向于零；在模型跟踪情况下，跟踪误差可以通过控制器的设计使之任意小。     

Decentralized stabilization of Markovian jump interconnected systems with unknown interconnections and measurement errors

This paper investigates the decentralized output feedback control problem for Markovian jump interconnected systems with unknown interconnections and measurement errors. Different from some existing results, the global operation modes of all subsystems are not required to be completely accessible for the decentralized control system. A decentralized dynamic output feedback controller is constructed using neighboring mode information and local outputs, where the measurement errors between actual and measured outputs are considered. Subsequently, a new design method is developed such that the resultant closed‐loop system is stochastically stable and satisfying an L_∞‐norm constraint. Sufficient conditions are formulated by linear matrix inequalities, and the controller gains are characterized in terms of the solution of a convex optimization problem. Finally, an example is given to illustrate the effectiveness of the proposed theoretical results.     

Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping

An approximation based adaptive neural decentralized output tracking control scheme for a class of large-scale unknown nonlinear systems with strict-feedback interconnected subsystems with unknown nonlinear interconnections is developed in this paper. Within this scheme, radial basis function RBF neural networks are used to approximate the unknown nonlinear functions of the subsystems. An adaptive neural controller is designed based on the recursive backstepping procedure and the minimal learning parameter technique. The proposed decentralized control scheme has the following features. First, the controller singularity problem in some of the existing adaptive control schemes with feedback linearization is avoided. Second, the numbers of adaptive parameters required for each subsystem are not more than the order of this subsystem. Lyapunov stability method is used to prove that the proposed adaptive neural control scheme guarantees that all signals in the closed-loop system are uniformly ultimately bounded, while tracking errors converge to a small neighborhood of the origin. The simulation example of a two-spring interconnected inverted pendulum is presented to verify the effectiveness of the proposed scheme.     

Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems

This paper describes an adaptive fuzzy control strategy for decentralized control for a class of interconnected nonlinear systems with MIMO subsystems. An adaptive robust tracking control schemes based on fuzzy basis function approach is developed such that all the states and signals are bounded. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant adaptive fuzzy decentralized control with multi-controller architecture guarantees stability and convergence of the output errors to zero asymptotically by local output-feedback. An extensive application example of a three-machine power system is discussed in detail to verify the effectiveness of the proposed algorithm.     

时变关联系统的分散自适应输出反馈控制

针对一类具有动静态关联项和未建模动态的时变关联系统,通过引入输入滤波器及一系列坐标变换,给出了一种分散自适应输出反馈控制器的设计方案.当时变参数的变化率属于L1∩L∞,外界干扰属于L2∩L∞,未建模动态的幅值在某砦范围内变化时,证明了闭环系统的稳定性,且每一个子系统的输出收敛于零.仿真例子验证了这一控制方案的有效性.     

Performance improvement in decentralized adaptive control: amodified model reference scheme

A modified decentralized model reference adaptive controller is proposed for a continuous time large scale system composed of N interconnected linear subsystems with unknown parameters. In the case where the high frequency gain of the transfer function of each of the isolated subsystems is known, the modified scheme can be used to improve the bad transient behaviour that typically results from poor knowledge of the subsystem parameters. The transient performance improvement, however, has to be traded off against the “size” of the allowable unmodeled interconnections in such a way that as the unmodeled interconnections disappear, the improvement in transient performance can be made arbitrary. Each local adaptive scheme uses “approximate differentiators,” and so possible sensor noise amplification is expected to be an additional limitation of the proposed scheme     

Decentralized adaptive fuzzy sliding mode control for reconfigurable modular manipulators

A stable decentralized adaptive fuzzy sliding mode control scheme is proposed for reconfigurable modular manipulators to satisfy the concept of modular software. For the development of the decentralized control, the dynamics of reconfigurable modular manipulators is represented as a set of interconnected subsystems. A first‐order Takagi–Sugeno fuzzy logic system is introduced to approximate the unknown dynamics of subsystem by using adaptive algorithm. The effect of interconnection term and fuzzy approximation error is removed by employing an adaptive sliding mode controller. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that resulting closed‐loop system is stable and the trajectory tracking performance is guaranteed. The simulation results are presented to show the effectiveness of the proposed decentralized control scheme. Copyright © 2009 John Wiley & Sons, Ltd.