严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.     

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.     

Decentralized control design for large-scale systems with strong interconnections using neural networks

We propose a decentralized neural network (NN) controller for a class of large-scale nonlinear systems with the strong interconnections. The NNs are used to approximate the unknown subsystems and interconnections. Due to the functional approximation capabilities of NNs, the additional precautions are not required to be made for avoiding the possible control singularity problems. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero. Furthermore, the issue of transient performance of the subsystems is also addressed under an analytical framework.     

Decentralized global robust stabilization of a class of interconnected minimum-phase nonlinear systems

This paper focuses on a class of large-scale interconnected minimum-phase nonlinear systems with parameter uncertainty and nonlinear interconnections. The uncertain parameters are allowed to be time-varying and enter the systems nonlinearly. The interconnections are bounded by nonlinear functions of states. The problem we address is to design a decentralized robust controller such that the closed-loop large-scale interconnected nonlinear system is globally asymptotically stable for all admissible uncertain parameters and interconnections. It is shown that decentralized global robust stabilization of the system can be achieved using a control law obtained by a recursive design method together with an appropriate Lyapunov function.     

Decentralized adaptive tracking control of nonaffine nonlinear large-scale systems with time delays

This paper addresses the problem of decentralized tracking control of large-scale systems with uncertain nonaffine nonlinear isolated subsystems and nonlinear interconnections with time-varying delays. Based on Lyapunov-Krasovskii functional approach and implicit function theorem, a delay-independent decentralized tracking controller is proposed. Due to functional approximation capability of fuzzy logic systems (FLS), neither strict structure restrictions on the isolated subsystems nor a priori knowledge of the strong interconnections with time-varying delays is required in our control design. Furthermore, transient performance of the resulting closed-loop system is also addressed under an analytical framework. Finally, two numerical examples are provided to show the effectiveness of the proposed controller.     

Adaptive fuzzy semi‐decentralized control for a class of large‐scale nonlinear systems with unknown interconnections

In this paper, a general method is developed to generate a stable adaptive fuzzy semi‐decentralized control for a class of large‐scale interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections. In the developed control algorithms, fuzzy logic systems, using fuzzy basis functions (FBF), are employed to approximate the unknown subsystems and interconnection functions without imposing any constraints or assumptions about the interconnections. The proposed controller consists of primary and auxiliary parts, where both direct and indirect adaptive approaches for the primary control part are aiming to maintain the closed‐loop stability, whereas the auxiliary control part is designed to attenuate the fuzzy approximation errors. By using Lyapunov stability method, the proposed semi‐decentralized adaptive fuzzy control system is proved to be globally stable, with converging tracking errors to a desired performance. Simulation examples are presented to illustrate the effectiveness of the proposed controller. Copyright © 2006 John Wiley & Sons, Ltd.     

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     

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.     

Decentralized stabilization of a class of interconnected stochasticnonlinear systems

This paper focuses on a class of large-scale interconnected stochastic nonlinear systems. The interconnections are bounded by strong nonlinear functions that contain first order and higher order polynomials as special cases. The problem we address is to design a decentralized controller such that the closed-loop, large-scale, interconnected stochastic nonlinear system is globally asymptotically stable in probability for all admissible interconnections. It is shown that the decentralized global stabilization via both state feedback and output feedback can be solved by a Lyapunov-based recursive design method     

Decentralized discrete-time quasi-sliding mode control of uncertain linear interconnected systems

In this paper, a new global decentralized discrete-time quasi-sliding mode control of linear interconnected systems is presented. The proposed controller is free of chattering problem and can be applied to a broader class of large-scale systems. Additionally, it is capable to deal with both known and unknown interconnections among the subsystems. Stability of the reduced-order interconnected systems is analyzed using Lyapunov approach. The proposed decentralized controller guarantees the reachability of the connective sliding manifold. The resultant dynamics are proved to be globally asymptotically stable. Furthermore, the controller is made robust to external disturbances by employing a disturbance estimation scheme. The simulations are performed on model of a two-area power generation system and the results show the efficacy of the proposed scheme.     

FUZZY DIRECT ADAPTIVE CONTROL FOR A CLASS OF DECENTRALIZED NONLINEAR SYSTEMS

In this paper, a stable fuzzy direct control scheme is presented for a class of interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections. In this control algorithm, fuzzy logic systems are employed to approximate the optimal controllers, which are designed on the assumption that all dynamics for each subsystem are known; then the fuzzy controllers and adaptation mechanisms for each subsystem depend only on local measurements to provide asymptotic tracking of a reference trajectory. In addition, a fuzzy sliding mode controller is developed to compensate for the fuzzy approximating errors and attenuate the interactions between subsystems. Global asymptotic stability is established in the Lyapunov sense, with the tracking errors converging to a neighborhood of zero. A simulation example is given to illustrate the performance of the proposed method.     

H∞ decentralized fuzzy model reference tracking controldesign for nonlinear interconnected systems

In general, due to the interactions among subsystems, it is difficult to design an H_∞ decentralized controller for nonlinear interconnected systems. The model reference tracking control problem of nonlinear interconnected systems is studied via H_∞ decentralized fuzzy control method. First, the nonlinear interconnected system is represented by an equivalent Takagi-Sugeno type fuzzy model. A state feedback decentralized fuzzy control scheme is developed to override the external disturbances such that the H∞ model reference tracking performance is achieved. Furthermore, the stability of the nonlinear interconnected systems is also guaranteed. If states are not all available, a decentralized fuzzy observer is proposed to estimate the states of each subsystem for decentralized control. Consequently, a fuzzy observer-based state feedback decentralized fuzzy controller is proposed to solve the H_∞ tracking control design problem for nonlinear interconnected systems. The problem of H _∞ decentralized fuzzy tracking control design for nonlinear interconnected systems is characterized in terms of solving an eigenvalue problem (EVP). The EVP can be solved very efficiently using convex optimization techniques. Finally, simulation examples are given to illustrate the tracking performance of the proposed methods     

An efficient decentralized robust adaptive controller for a class of large-scale non-affine nonlinear systems with strong interactions

This paper proposed a novel combination of decentralized robust adaptive radial basis neural network controller for a class of large-scale nonlinear non-affine systems with unknown subsystems and strong interconnections. Some suitable adaptive rules based on neural network are introduced that make all signals bounded, and also Lyapunov theory guaranteed tracking error signal asymptotically reaches zero. To show the effectiveness of the proposed method, some numerical results are presented. Furthermore, to evaluate the performance of the suggested method, a decentralized adaptive method is adopted from the literature and applied for comparison. Simulation results verify the desirable performance of the proposed controller.     

非线性扩展结构大系统自适应神经网络跟踪控制

针对一类非线性关联大系统在结构扩展时的跟踪控制问题, 提出一种采用自适应神经网络的控制方法. 该方法要求在不改变原结构系统控制律的前提下设计新加入子系统的控制律和自适应律, 使扩展后所有子系统都具有很好的跟踪性能. 这里主要利用神经网络的逼近功能以及Backstepping 技术来设计自适应律和控制律, 通过Lyapunov 理论证明在该控制器的作用下闭环系统的所有信号均是有界的, 并可使系统准确跟踪. 仿真结果验证了所提出方法的有效性.

     

双线性系统的静态分散输出控制

研究了一类T-S双线关联系统的静态输出控制反馈问题。应用分散控制理论,得到了闭环关联大系统Lyapunov稳定的充分条件。相应的分散模糊控制器可由线性矩阵不等式(LMD的解得到。最后,由数例仿真验证了所提方法的有效性。     

Decentralized sliding-mode output-feedback control of interconnected discrete-delay systems

Global decentralized discrete sliding mode control of a class of interconnected system using only output information is considered in this paper. Matched and unmatched uncertainties as well as known and unknown interconnections are treated. Bounded time-varying delays are considered within the subsystems and through interconnections. The stability of the reduced-order interconnected systems is analyzed using the Lyapunov–Kraszovskii approach and a novel reachability condition of the composite sliding surfaces is established. The resultant dynamics are proved to be globally asymptotically stable. Simulations show the efficacy of the proposed scheme.     

Decentralized adaptive stabilization of nonlinear systems

Decentralized adaptive control schemes using the principle of dominant subsystems are presented for time-varying nonlinear dynamic large-scale interconnected systems. Sufficient conditions for the existence of local decentralized adaptive control laws stabilizing a given large-scale system (LSS) are derived in terms of controller parameters for incompletely known composite systems. The approach proposed in this paper is applied to nonlinear stabilizing adaptive decentralized control (ADC) of multimachine power systems. The stability of the multimachine power systems with the ADC is illustrated by the simulation results for a two machine system.     

Decentralized control of a class of large-scale nonlinear systems using neural networks

This paper designs a decentralized neural network (NN) controller for a class of nonlinear large-scale systems, in which strong interconnections are involved. NNs are used to handle unknown functions. The proposed scheme is proved guaranteeing the boundedness of the closed-loop subsystems using only local feedback signals.     

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.     

Decentralized output-feedback MRAC of linear state delay systems

In this note, we develop coordinated decentralized output-feedback adaptive controllers for a class of large-scale systems with state time delays in the subsystems and in the interconnections. We present a decentralized model reference adaptive control scheme which requires an exchange of signals between the different reference models, but does not involve the exchange of output signals between the different subsystems. It can not only guarantee closed-loop stability but also asymptotic zero tracking errors when uncertainties and delays are present in the subsystems and interconnections. Closed-loop signal boundedness and asymptotic output-feedback tracking are proven analytically and verified by simulation.