Simultaneous fault detection and control for switched systems with actuator faults

This paper is concerned with the fault detection and control problem for discrete-time switched systems. The actuator faults, especially ‘outage cases’, are considered. The detector/controller is designed simultaneously such that the closed-loop system switches under an average dwell time, and when a fault is detected, an alarm is generated and then the controller is switched to allow the norm of the states of the subsystem to increase within the acceptable limits. Thus, a switching strategy which combines average dwell time switching with event-driven switching is proposed. Under this switching strategy, the attention is focused on designing the detector/controller such that estimation errors between residual signals and faults are minimised for the fulfillment of fault detection objectives; simultaneously, the closed-loop system becomes asymptotically stable for the fulfillment of control objectives. A two-step procedure is adopted to obtain the solutions through satisfying a set of linear matrix inequalities. An example comprising of three cases is considered. Through these cases, it is demonstrated that the fault detection and control for switched systems using a two-stage switching strategy and asynchronous switching are feasible.     

Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi–Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.     

H∞ state estimation of generalised neural networks with interval time-varying delays

This paper focuses on studying the H_∞ state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov–Krasovskii functional are handled by the Jensen’s inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with H_∞ performance. The proposed conditions are represented by linear matrix inequalities. Optimal H_∞ norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples.     

A new approach to robust reliable H∞ control for uncertain nonlinear systems

This paper focuses on the optimal robust reliable H_∞ control for a class of uncertain nonlinear systems with actuator faults. A new method of annihilating uncertain matrix is proposed. Based on this approach, a new method of disposing of the phenomenon of uncertain matrices multiplication is provided. In accordance with the method, the optimal robust reliable H_∞ control problem for uncertain nonlinear systems is settled by employing state feedback, in terms of linear matrix inequality (LMI). Finally, two illustrative examples are given to show the feasibility and validity of the proposed method.     

Reaching a consensus in networks of high-order integral agents under switching directed topologies

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent’s full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent’s (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent’s state indicates the convergence of the original high-order integral agent’s state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be ‘uniformly jointly quasi-strongly connected’, which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.     

过程工业大数据建模研究展望

人们对大数据的认识已从"3Vs" (Volume-大容量; Variety-多样性; Velocity-处理实时性)、"4Vs" ("3Vs"与Value-价值)、到现今的"5Vs" ("4Vs"与Veracity-真实性).在此背景下, 首先分析过程工业大数据的"5Vs"特性; 接下来, 综述现有数据建模方法, 并结合过程工业大数据特有性质 (包括:多层面不规则采样性、多时空时间序列性、不真实数据混杂性) 论述现有数据建模方法应用于工业大数据建模时的局限; 最后, 探讨过程工业大数据建模有待研究的问题, 包括:1) 多层面不规则采样数据的潜结构建模; 2) 用于事件发现、决策和因果分析的多时空时间序列数据建模; 3) 含有不真实数据的鲁棒建模; 4) 支持实时建模的大容量数据计算架构与方法.     

Distributed scheduling with probabilistic and fuzzy classifications of processes

The classification of various types of processes is an important factor in large-scale distributed systems such as, grid and cloud platforms. Moreover, the coordination and control of distributed processes are research challenges in presence of unpredictable network partitioning and distributed semaphores. The process classification is important in order to allocate and schedule distributed processes enhancing overall resource utilization and throughput. The schedulers employ patterns of resource affinities of concurrent processes in order to make scheduling decisions affecting overall resource utilization in a system, where resource affinity patterns of a process may not be static. This paper proposes an estimation model and a classifier algorithm to queuing processes based on respective resource affinities. The kernel-level software architecture is designed to control scheduling of distributed processes based on classification for enhanced throughput. The classifier algorithm tracks the resource affinities of processes based on execution traces and the control algorithm performs process scheduling. Experimental results indicate that the classifier algorithm successfully manages process queues based on resource affinities of processes and, the control algorithm successfully monitors scheduler activation for a set of processes.