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.     

Distributed fault diagnosis in a class of interconnected nonlinear uncertain systems

In this article, a distributed fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilising local measurements and certain communicated information from neighbouring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed FDI in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modelling uncertainty. Moreover, the fault detectability and isolability conditions are rigorously investigated, characterising the class of faults in each subsystem that are detectable and isolable by the proposed distributed FDI method. Additionally, the stability and learning capability of the local adaptive fault isolation estimators designed for each subsystem is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the method.     

Quasi-ISS/ISDS observers for interconnected systems and applications

This paper considers interconnected nonlinear dynamical systems and studies observers for such systems. For single systems the notion of quasi-input-to-state dynamical stability (quasi-ISDS) for reduced-order observers is introduced and observers are investigated using error Lyapunov functions. It combines the main advantage of ISDS over input-to-state stability (ISS), namely the memory fading effect, with reduced-order observers to obtain quantitative information about the state estimate error. Considering interconnections quasi-ISS/ISDS reduced-order observers for each subsystem are derived, where suitable error Lyapunov functions for the subsystems are used. Furthermore, a quasi-ISS/ISDS reduced-order observer for the whole system is designed under a small-gain condition, where the observers for the subsystems are used. As an application, we prove that quantized output feedback stabilization for each subsystem and the overall system is achievable, when the systems possess a quasi-ISS/ISDS reduced-order observer and a state feedback law that yields ISS/ISDS for each subsystem and therefor the overall system with respect to measurement errors. Using dynamic quantizers it is shown that under the mentioned conditions asymptotic stability can be achieved for each subsystem and for the whole system.     

Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems

In this paper, a distributed sensor fault detection and isolation (FDI) method is developed for a class of interconnected nonlinear uncertain systems. In the distributed FDI architecture, a FDI component is designed for each subsystem in the interconnected system. For each subsystem, its corresponding local FDI component is designed by utilizing local measurements and certain communicated information from neighboring FDI components associated with subsystems that are directly interconnected to the particular subsystem under consideration. Under certain assumptions, adaptive thresholds for distributed sensor fault detection and isolation in each subsystem are derived, ensuring robustness with respect to interactions among subsystems and system modeling uncertainty. Moreover, the fault detectability condition is rigorously investigated, characterizing the class of sensor faults in each subsystem that is detectable by the proposed distributed FDI method. Additionally, the stability and learning capability of the distributed adaptive fault isolation estimators is established. A simulation example of interconnected inverted pendulums mounted on carts is used to illustrate the effectiveness of the distributed FDI method.     

Resilient decentralized stabilization of interconnected time‐delay systems with polytopic uncertainties

Decentralized delay‐dependent local stability and resilient feedback stabilization methods are developed for a class of linear interconnected continuous‐time systems. The subsystems are time‐delay plants which are subjected to convex‐bounded parametric uncertainties and additive feedback gain perturbations while allowing time‐varying delays to occur within the local subsystems and across the interconnections. The delay‐dependent local stability conditions are established at the subsystem level through the construction of appropriate Lyapunov–Krasovskii functional. We characterize decentralized linear matrix inequalities (LMIs)‐based delay‐dependent stability conditions by deploying an injection procedure such that every local subsystem is delay‐dependent robustly asymptotically stable with an γ‐level ??₂‐gain. Resilient decentralized state‐feedback stabilization schemes are designed, which takes into account additive gain perturbations such that the family of closed‐loop feedback subsystems enjoys the delay‐dependent asymptotic stability with a prescribed γ‐level ??₂‐gain for each subsystem. The decentralized feedback gains are determined by convex optimization over LMIs. All the developed results are tested on representative examples. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust decentralized model reference adaptive control of discrete-time interconnected systems

A robust decentralized model reference adaptive controller is proposed for a class of large-scale systems composed of several interconnected subsystems and described by state space equations. We have formulated a local adaptive controller for each subsystem using only local information such that the state of this subsystem tracks the corresponding state of a reference model. The content of the paper is limited to interconnected subsystems which are described by linear, deterministic, single-input single-output and discrete-time models with unknown and/or slowly time-varying parameters. Sufficient conditions, formulated by utilizing Lyapunov theory, are given for the overall system to be stabilizable by decentralized state feedback adaptive control laws. The results are illustrated by a numerical example.     

Indirect adaptive control for interconnected systems

An indirect adaptive controller for interconnected systems is introduced. Each subsystem is subject to bounded disturbances and to possibly unbounded interconnections with other subsystems. A variable dead zone is incorporated into a gradient estimation scheme to limit the effects of the interconnections and disturbances. An adaptive state feedback control law is described which stabilizes the interconnected system, forcing the input and output signals of each subsystem to remain bounded     

A passification approach to adaptive nonlinear stabilization

In this paper, we unify and extend previously obtained results on adaptive stabilization of nonlinear systems from a passive systems viewpoint. It is shown that, for an interconnected multi-input nonlinear system composed of a chain of feedback strictly passive subsystems, an adaptive controller can be designed to render the closed loop system passive. Further, adaptive regulation is guaranteed if the zero-dynamics equation of each subsystem satisfies an appropriate input/state property. A systematic use of feedback passification in our proposed adaptive control scheme allows a simpler reinterpretation of the backstepping design with tuning functions in Krsti? et al. (1992).     

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.     

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.     

Decentralized stabilization for expanding construction oflarge-scale systems

A decentralized stabilization problem compatible with expanding the construction of large-scale systems is considered. It is assumed that a large-scale system is obtained by adding new subsystems one after another. When a new subsystem is connected to an already interconnected portion, a local controller is designed for the new subsystem so that both the subsystem and the resultant expanded system are stable. The proper-stable-factorization approach is used for calculating the local controllers. Sufficient conditions on subsystems for such stabilization are presented. They are expressed in terms of the solvability of linear matrix equations over the ring of stable rational matrices     

Stability of Large Space Structures Preserved Under Failures of Local Controllers

This note considers position and attitude control of large space structures composed of a number of subsystems (substructures) interconnected by flexible links. A decentralized control law of dynamic displacement feedback compatible with subsystems is applied under the assumption that sensors and actuators are collocated. It has been known that the overall closed-loop system is robustly stable against uncertainties in mass, damping, and stiffness, if rigid modes of each subsystem are controllable and observable. The objective of this note is to derive conditions for the overall system to be stable even when some local controllers fail. The conditions are expressed in terms of the stiffness (or damping) matrices and interconnection location matrices of the subsystems whose local controllers fail     

空间连接系统的稳定与镇定

研究连续时间线性时不变空间连接系统的稳定性分析与镇定控制器设计问题,其具有不同动态特性的子系统之间的连接关系为任意且时不变的.推导空间连接系统稳定性分析易于计算的充分必要条件,并给出系统稳定的基于单个子系统参数的充分条件和必要条件;在此基础上,进行基于单个子系统状态反馈的镇定控制器设计.所提方法能够避免高维矩阵的求逆等运算,而且充分利用了系统参数矩阵的块对角结构和子系统连接矩阵的稀疏特性.仿真结果显示,所得到的条件在大规模网络化系统的分析与综合中,计算效率有很大的提高.     

New overlapped decentralized controller for large-scale systems

This paper is devoted to the development of a new decentralized control approach for large-scale interconnected dynamical systems. The proposed decentralized control structure is based on the concept of overlapping decomposition. The controller gains for the overlapped subsystems are calculated only once and they are easy to compute since all operations are carried out at the subsystems level. Since the proposed decentralized controller operates under a completely decentralized information pattern, the inaccessible states for each augmented subsystem are estimated using a set of overlapped decentralized estimators. A sufficient condition is developed for the stability of the global system with the decentralized control scheme subject to structural perturbations. It is shown that with the satisfaction of this condition, the proposed controller provides a robust design which is insensitive to structural perturbations.     

Dissipativity reinforcement in feedback systems and its application to expanding power systems

This paper focuses on the performance analysis and improvement of interconnected passive systems. We assume that each subsystem has a special passivity property that is characterized by 2 parameters. The parameters are also utilized for evaluating the dissipation performance as the L₂‐gain. Then, the feedback system composed of passive subsystems inherits the parameter‐dependent passivity, and the parameter transition is given. In addition, it is shown that the dissipation performance of the feedback system is strictly improved as compared with that of the subsystems, which is called dissipativity reinforcement in this paper. Furthermore, we expand the feedback system to a larger‐scale system via the iterative feedback connection of the passive subsystems. Then, the performance of the entire system is gradually reinforced with the increase in the number of subsystems. Subsequently, we extend the class of parameter‐dependent passivity to a frequency‐dependent one. Finally, dissipativity reinforcement via an iterative feedback connection is applied to a power system that involves a large number of renewable energy generators. In particular, we propose a strategy for designing the power system, such that the dissipation performance of the entire system is gradually reinforced via scale expansion, ie, with the increase in the amount of energy generators installed.     

Frequency-domain subsystem identification with application to modeling human control behavior

We present a frequency-domain subsystem identification algorithm that identifies unknown feedback and feedforward subsystems that are interconnected with a known subsystem. This method requires only accessible input and output measurements, applies to linear time-invariant subsystems, and uses a candidate-pool approach to ensure asymptotic stability of the identified closed-loop transfer function. We analyze the algorithm in the cases of noiseless and noisy data. The main analytic result of this paper shows that the coefficients of the identified feedback and feedforward transfer functions are arbitrarily close to the true coefficients if the data noise is sufficiently small and the candidate pool is sufficiently dense. This subsystem identification approach has application to modeling the control behavior of humans interacting with and receiving feedback from a dynamic system. We apply the algorithm to data from a human-in-the-loop experiment to model a human’s control behavior.     

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.     

Network‐based robust performance analysis for uncertain systems interconnected over an undirected graph

This article aims at providing tractable conditions of robust performance (RP) analysis for interconnected uncertain system with networked communication, which is composed of heterogeneous uncertain subsystems connected over an undirected graph. Each subsystem communicates with its neighbors through local packet‐based communication network asynchronously and independently. First, the interconnected uncertain system with networked communication is modeled as a hybrid system. Then sufficient conditions in terms of linear matrix inequalities (LMIs), which are related to the interconnection structure and the maximum allowable transmission interval (MATI) of each local network, are derived such that the interconnected uncertain system is robustly asymptotically stable and achieves the desired RP. These conditions only depend on the parameters of individual subsystem and local network. Finally, an example of power system is given to demonstrate the applicability and effectiveness of our results.     

无刷双馈调速电机无源性分析及自适应控制

应用无源性控制理论从能量角度研究了无刷双馈电机控制系统.建立了无刷双馈电机的欧拉方程,并将其分解为电气和机械两个无源子系统的反馈并联,以此说明在设计控制器时只需考虑电气子系统,简化了控制算法.设计了电机的转矩和转速控制器.在此基础上,考虑电机功率及控制绕组电阻在运行中可能发生变化.设计自适应控制器以提高系统的鲁棒性.仿真结果表明此控制策略能快速地跟踪速度给定.动静态响应能力较好,且具有全局稳定、系统鲁棒性好的特点.     

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.