Shanbin Li

Output Feedback Guaranteed Cost Control for Networked Control Systems With Random Packet Dropouts and Time Delays in Forward and Feedback Communication Links

This study focuses on the output feedback guaranteed cost control issue for networked control systems with random packet dropouts and time delays. The plant considered in this study is characterized by an uncertain linear discrete-time system, wherein random packet dropouts and time delays exist in forward controller-to-actuator and feedback sensor-to-controller communication links that are modeled as unified multiple Markov chains. The resulting closed-loop system is modeled as a Markovian jump linear system. The guaranteed cost control issue is solved via Lyapunov stability theory and the linear matrix inequality approach. The design of the mode-independent robust output feedback controller is derived under the Markov jump unified framework. The closed-loop system is not only stochastically stable but it also guarantees an adequate level of performance. A simulation example illustrates the effectiveness of the proposed method.

H 2 / H ∞ control of networked control system with random time delays

This paper investigates the H2/H∞ control problem for a class of discrete-time networked control systems with random communication time delays. Both sensor-to-controller (S-C) and controller-to-actuator (C-A) random network-induced delays are considered. Two independent Markov chains are used to model the S-C and C-A random delays. The resulting closed-loop system is a jump linear time-delay system induced by two Markov chains. Sufficient conditions for existence of H2/H∞ controller are established by free-weighting matrix and stochastic Lyapunov functions. A simulation example illustrates the effectiveness of the proposed method.

Optimal Filters with Multiple Packet Losses and its Application in Wireless Sensor Networks

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimal filter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs.

Modeling and Output Feedback Control of Networked Control Systems with Both Time Delays; and Packet Dropouts

This paper is concerned with the problem of modeling and output feedback controller design for a class of discrete-time networked control systems (NCSs) with time delays and packet dropouts. A Markovian jumping method is proposed to deal with random time delays and packet dropouts. Different from the previous studies on the issue, the characteristics of networked communication delays and packet dropouts can be truly reflected by the unified model; namely, both sensor-to-controller (S-C) and controller-to-actuator (C-A) time delays, and packet dropouts are modeled and their history behavior is described by multiple Markov chains. The resulting closed-loop system is described by a new Markovian jump linear system (MJLS) with Markov delays model. Based on Lyapunov stability theory and linear matrix inequality (LMI) method, sufficient conditions of the stochastic stability and output feedback controller design method for NCSs with random time delays and packet dropouts are presented. A numerical example is given to illustrate the effectiveness of the proposed method.

Dynamic-group scheduling scheme for maneuvering target tracking in wireless sensor networks

Limited resource allocation and scheduling are important problems in distributed wireless sensor networks (WSNs). Saving energy and real-time performance deserve research for low-cost sensor nodes in the target tracking application. In this paper, we propose a new dynamic-group idea and apply it to our scheduling scheme. An Extended Kalman Filter (EKF) is employed to predict tracking accuracy, and an energy model is adopted to predict the energy consumption. Simulation results show that, compared with the information-driven sensor quering (IDSQ) and the adaptive sensor scheduling strategy, the proposed scheme can achieve significant improvement on real-time property and tracking accuracy for target tracking in WSN. At the same time, it approaches to the adaptive sensor scheduling strategy in energy consumption.

Stability analysis and controller design for networked control systems based on hidden Markov model

In this paper, the problem of stability analysis and controller design for a class of networked control systems (NCSs) with stochastic markovian packet dropouts is investigated. The kind of systems are modeled as Markov jump linear systems with partly unknown Markov states based on the hidden Markov model. We present sufficient condition for the stability and develop the controller design method for NCSs with packet dropouts. The example of numerical simulation illustrates the effectiveness of the results.

Output Feedback Stabilization of Networked Control Systems with Uncertain Transition Probability Matrix

Abstract In this paper, the output feedback stabilization problem for a class of discrete-time networked control systems (NCSs) with uncertain transition probability matrix and random packet dropouts are investigated. Random packet dropouts occurring in the sensor-to-controller (S-C) and controller-to-actuator (C-A) are modeled as Markov chain. The resulting closed-loop systems are modeled as discrete-time Markovian jump linear systems (MJLSs) with uncertain transition probability matrix. Sufficient conditions for existence of a output feedback controller which guarantees the stochastic stability of NCS are established based on stochastic Lyapunov function and linear matrix inequality approach. A simulation example illustrates the validity and feasibility of the results.

Event-triggered resilient control for cyber physical system under denial-of-service attacks

In this paper, we research the resilient control problem for cyber-physical system (CPS) under denial-of-service (DoS) attacks. These malicious DoS attacks aim to impede the communication of measurement data or control data in order to endanger the functionality of the closed-loop system. Meanwhile, in order to save network resources, event-triggered mechanism has been introduced into this CPS. By exploiting the relationship between cyber system and physical system, we aim to design the resilient controller and resilient control strategy to tolerate a class of DoS signals characterised by probability without serious hazard to the stability and performance of CPS. Furthermore, considering that the transition probability of cyber state is unknown, the on-policy reinforcement learning method – SARSA (State-Action-Reward-State-Action) – is used to solve this problem. Thus a resilient control algorithm that integrates game theory, robust control theory, event-triggered control method and SARSA learning method is presented to enhance the security and robustness of the CPS. At last, the numerical simulation and experimental results are given to demonstrate the validity and applicability of the proposed algorithm.

Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems

Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing (CS) is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-singular value decomposition (K-SVD), which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6-bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.

Reconstruction of measurements in state estimation strategy against cyber attacks for cyber physical systems

To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.