Robust adaptive sliding mode control for uncertain delta operator systems

In this paper, a robust adaptive sliding mode controller is presented for delta operator systems with mismatched uncertainties and exogenous disturbances. The parameters of the delta operator system are taken for norm‐bounded uncertainties. The exogenous disturbance is also assumed to be bounded. After the statement of a sufficient condition for the existence of linear sliding surface based on linear matrix inequality technique, a robust reaching motion control method for delta operator systems is presented. Afterwards, an adaptive sliding mode controller for delta operator systems is designed. A bridge between the robust adaptive sliding mode control and the delta operator system framework is made. Numerical example is given to illustrate the effectiveness of the developed techniques. Copyright © 2009 John Wiley & Sons, Ltd.     

随机最优非线性网络控制系统设计

针对网络控制非线性系统中存在的不确定时延,利用Delta算子方法,研究了基于T-S模糊模型的随机最优网络控制问题。采用T-S模型模糊动态逼近非线性系统,将非线性模型模糊化为局部线性模型,设计了本质为非线性的具有时延补偿功能的状态反馈控制器,并进行了稳定性分析,并仿真。结果表明,所提出的建模方法是可行的,实质为非线性的状态反馈的控制器能够有效地补偿时延对系统性能的影响,且补偿效果好。     

Fault detection filter design for networked control systems with time‐varying sampling periods and packet dropouts

This paper investigates the problem of fault detection for networked control systems under simultaneous consideration of time‐varying sampling periods and packet dropouts. By taking time‐varying sampling periods into consideration, a new closed‐loop model for the considered networked control systems is established. The sampling period switching‐based approach and the parameter uncertainty‐based approach are adopted to deal with time‐varying sampling periods. Based on the established model, the observer‐based fault detection filter design criteria are proposed to asymptotically stabilize the residual system in the sense of mean‐square. The designed observer‐based fault detection filter can guarantee the sensitivity of the residual signal to faults. The simulation results illustrate the effectiveness of the obtain results. Copyright © 2015 John Wiley & Sons, Ltd.     

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

This paper deals with the problem of fault estimation and accommodation for a class of networked control systems with nonuniform uncertain sampling periods. Firstly, the reason why the adaptive fault diagnosis observer cannot be applied to networked control systems is analyzed. Based on this analysis, a novel robust fault estimation observer is constructed to estimate both continuous‐time fault and system states by using nonuniformly discrete‐time sampled outputs. Furthermore, using the obtained states and fault information, a nonuniformly sampled‐data fault tolerant control law is designed to preserve the stability of the closed‐loop system. The proposed scheme can not only guarantee the impact of continuous‐time uncertainties and discrete‐time sampled estimation errors on the faulty system to satisfy a H _∞ performance index but also repress the negative effect of the unknown intersample behavior of continuous‐time fault by use of an inequality technique. Finally, simulation results are included to demonstrate the feasibility of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Limits of control performance for distributed networked control systems in presence of communication delays

In terms of computational complexity and fault tolerance, distributed networked control systems (DNCSs) is favorable for large‐scale processes. However, it poses additional limitations on the achievable control performance, especially when communication delay is present. The conventional minimum variance (MV) benchmarks mainly consider the limitations caused by the system itself and can give overly estimates of achievable performance when applied to the systems under distributed networked control. This paper proposes a solution to the MV benchmark for DNCSs considering both system time delays and time‐invariant communication delays. Furthermore, lower and upper bounds of the MV benchmark are proposed to assess the performance of DNCSs when there are time‐varying communication delays. These results are useful for evaluating the potential performance improvement if a DNCS is implemented to replace a decentralized control system. The proposed results are illustrated by a simulation example.     

Modeling and design of networked control systems using a stochastic switching systems approach

A networked control system (NCS) is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. In this paper, we consider NCSs modeled by stochastic switching systems, and propose a new method for modeling and optimal control. First, a recursive representation of the expected value of the state is proposed. Next, after an over‐approximation of this recursive representation is derived, the optimal control problem is reduced to a linear programming problem. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method provides us an easy‐to‐use control method for NCSs. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Traffic scheduling in distributed networked control systems with packet losses and delays for power systems voltage stabilization

In this paper, medium access control (MAC) sublayer data traffic scheduling in distributed networked control systems (DNCSs) with networked induced packet losses and delays is investigated for stabilization and control of system dynamics. An effective‐information‐directed distributed sensor selection algorithm is proposed to select sensors to participate in delay‐tolerant information filtering for state estimation. The distributed scheduling algorithm is based on the total amount of effective information for all measurements stored in the sensors. We consider packet loss in wireless networks as a special type of delay, so the impact of packet losses on effective information amount can also be studied for the formulation of distributed scheduling strategy in the presence of packet losses. The distributed data traffic scheduling considering packet losses and delays is carried out in a networked system with data packet dropout governed by a Markov process for voltage regulation and stabilization with distributed energy resources (DERs). Experimental results demonstrate that the scheduling algorithm with awareness of the system dynamic state can well control the power system dynamics. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Neural adaptive control for leader–follower flocking of networked nonholonomic agents with unknown nonlinear dynamics

This paper is concerned with the leader–follower flocking problem of networked nonholonomic multi‐agent systems with non‐identical unknown nonlinear dynamics. The leader motion to be synchronized is also nonlinear and unknown. By employing the graph theory and a pinning control technique, a distributed neural adaptive control design is developed for the agents to achieve motion synchronization with the leader. The design is for a directed communication graph with a fixed topology. A collective potential function is used to maintain cohesion between the agents. On the basis of Lyapunov analysis, the developed neural flocking algorithm guarantees that all the agents’ headings and speeds are synchronized with the leader and collisions between the agents can be avoided. An illustrative example is given to show the effectiveness of the proposed control strategy. Copyright © 2013 John Wiley & Sons, Ltd.     

Delta算子离散模型辨识的性能分析

研究了Delta算子描述的离散系统参数辨识问题，基于Delta算子矩阵求逆引理，给出Delta算子递推最小二乘(DRLS)估计公式；分析了DRLS算法的参数误差和预报误差特性。所得结论将连续与离散模型辨识的有关结果统一于Delta算子框架。     

具有非线性扰动的网络化随机系统鲁棒控制

针对具有非线性扰动的网络化随机系统的鲁棒控制问题,考虑到反馈控制环中由于实时通讯网络的存在会不可避免地出现网络诱导时延和数据丢失现象,建立了连续时间网络化随机系统模型.在此基础上,设计了状态反馈控制器,使得闭环系统最终均方有界.利用广义系统变换和Lyapunov-Krasovskii泛函方法,得到了闭环系统最终均方有界的充分条件,证明了理想的状态反馈控制器可以通过求解线性矩阵不等式得到.该方法可推广到以双线性随机系统为受控对象的网络化控制系统的镇定控制器设计中.     

Fault‐tolerant control for wireless networked control systems with an integrated scheduler

This paper addresses a study of fault‐tolerant control (FTC) for wireless networked control systems (WNCSs) in industrial automatic processes. The WNCSs is composed of many subsystems, which operate with different sampling cycles. In order to meet the real‐time requirements and ensure a deterministic data transmission, the time division multiple access (TDMA) mechanism is adopted in WNCSs. The data in WNCSs are transmitted following a TDMA‐based scheduler. According to the periodicity, WNCSs integrated with the scheduler is first formulated as discrete linear time periodic systems (LTPSs). Afterwards, a fault estimation method for LTPSs is developed under a H_∞ performance specification with a regional pole constraint. With the achieved state observer and fault estimator, an FTC strategy for LTPSs is explored. Finally, the proposed methods are verified on a physical experimental WiNC platform. Copyright © 2016 John Wiley & Sons, Ltd.     

Iterative learning control for nonlinear dynamic systems with randomly varying trial lengths

In this paper, we introduce an iterative learning control (ILC) scheme based on an iteratively moving average operator for nonlinear dynamic systems with randomly varying trial lengths. By using the iteratively moving average operator, the proposed ILC algorithm overcomes the limitation of traditional ILC that all trial lengths must be identical. It is shown that for nonlinear affine and non‐affine systems, the proposed learning algorithm works effectively to nullify the tracking error. In the end, two illustrative examples are presented to demonstrate the performance and the effectiveness of the proposed ILC scheme for nonlinear dynamic systems. Copyright © 2015 John Wiley & Sons, Ltd.     

Fault diagnosis and fault-tolerant control of uncertain network control systems

In this article, the equivalent control strategy based on Laplace transform is proposed for the problem of stochastic delay in networked control systems. The original system is transformed into an equivalent control system without random delay block by Laplace transform. Then, a new augmented variable and equivalent control system is introduced to construct an augmented system. An adaptive fault diagnosis observer is designed based on the augmented system. The adaptive turning rate of the observer is obtained by solving the corresponding linear matrix inequality. Based on the information of online fault diagnosis and state estimation, a fault-tolerant controller based on PI control strategy is designed to compensate the fault. Finally, a model of the switched reluctance motor system is considered to show the effectiveness of this method.     

Finite‐time dissipativity‐based filter design for networked control systems

This paper investigates the problem of finite‐time boundedness and dissipativity‐based filter design for networked control systems together with parameter uncertainties and random packet dropouts. The packet transmission information is defined by using Bernoulli distributed white sequence which characterizes the measurement conditions. Some new sufficient conditions are established to ensure that the filtering error system is stochastically finite‐time bounded and strictly finite‐time dissipative. These sufficient conditions to design the filter parameters are derived by using linear matrix inequalities and reciprocally convex approach. Finally, an example is given to validate the effectiveness of the proposed filter design.     

Geometry of adaptive control: optimization and geodesics

Two incompatible topologies appear in the study of adaptive systems: the graph topology in control design, and the coefficient topology in system identification. Their incompatibility is manifest in the stabilization problem of adaptive control. We argue that this problem can be approached by changing the geometry of the sets of control systems under consideration: estimating n_p parameters in an n_p‐dimensional manifold whose points all correspond to stabilizable systems. One way to construct the manifold is using the properties of the algebraic Riccati equation. Parameter estimation can be approached as an optimal control problem akin to the deterministic Kalman filter, leading to algorithms that can be used in conjunction with standard observers and controllers to construct stable adaptive systems. Copyright © 2004 John Wiley & Sons, Ltd.     

An approach to digital implementation of continuous backstepping adaptive control for linear systems

This paper presents a solution to the problem of digitally implementing backstepping adaptive control for linear systems. The continuous‐time system to be controlled is given a discrete‐time representation in the δ‐operator. A discrete adaptive backstepping controller is then designed for such a discrete‐time model. The effect of the modelling error, generated by the sampling process, is accounted for in the parameter update law by a σ‐modification. It is shown that all the signals (discrete and continuous) of the closed loop are uniformly bounded, with a region of attraction which is a K_∞ function of the sampling rate. An upper bound on the asymptotic tracking error is then given, and shown to be proportional to the sampling period. Copyright © 1999 John Wiley & Sons, Ltd.     

列车网络控制系统的调度与控制协同设计方法

网络控制系统存在调度与控制相互约束的问题,针对CAN作为总线的局部列车网络控制系统,为提高其在调度与控制相互约束下的综合性能,以消息传输时延和丢包率为优化目标,可调度条件和控制系统稳定性为约束,将调度与控制协同设计转化为关于传输周期的多目标约束优化问题,并利用遗传算法求解所述问题,得到优化的传输周期,最后给出了优化实例。结果表明,得到的传输周期可以在约束下使网络性能达到最优,满足列车对消息传输实时可靠的要求。在该列车网络控制系统中,所述调度与控制协同设计方法是可行的。     

Guaranteed cost control for large‐scale systems under control gain perturbations

The guaranteed cost control problem of the decentralized robust control for large‐scale systems with the norm‐bounded time‐varying parameter uncertainties and a given quadratic cost function is considered. Sufficient conditions for the existence of guaranteed cost controllers are given in terms of linear matrix inequality (LMI). It is shown that decentralized local state feedback controllers can be obtained by solving the LMI. The problem of guaranteed cost control for large‐scale systems under the gain perturbations is also considered. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 146(4): 43–57, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10265     

Adaptive iterative learning control of discrete‐time varying systems with unknown control direction

Without using Nussbaum gain, a novel method is presented to solve the unknown control direction problem for discrete‐time systems. The underlying idea is to fully exploit the convergence property of parameter estimates in well‐known adaptive algorithms. By incorporating two modifications into the control and the parameter update laws, respectively, we present an adaptive iterative learning control scheme for discrete‐time varying systems without the prior knowledge of the sign of control gain. It is shown that the proposed adaptive iterative learning control can achieve perfect tracking over the finite time interval while all the closed‐loop signals remain bounded. An illustrative example is presented to verify effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Hybrid online learning control in networked multiagent systems: A survey

This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous‐time dynamical systems and discrete‐time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning‐based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous‐time dynamical systems and discrete‐time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems.