Bugong Xu

Robust control for a networked direct-drive linear motion control system

This paper investigates the robust control method for networked dynamic systems and its application for a direct-drive linear motion control system in a network environment. The unavoidable network-induced random delays are modeled as Markov chains. The control object of the linear motion control system in this study is a double-sided linear switched reluctance machine (DLSRM). To tackle the inherent uncertainties in the DLSRM, a robust control strategy is designed by proposing a new Lyapunov function and applying the free-weighting matrix technique. A state feedback robust controller is designed such that the closed-loop direct-drive linear motion control system over a network is stochastically robust stable. The robust controller can be conveniently obtained by solving a set of linear matrix inequalities. The numerical simulation of an angular positioning system is presented to illustrate the effectiveness of the proposed robust control method. Furthermore, the experimental tests on the networked direct-drive linear motion control system verify the practicability of the proposed method.

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.

Distributed light control using wireless sensor and actuator networks

Wireless sensor and actuator networks (WSANs) emerging from WSN conduce to many control applications like light control. This work focuses on meeting human preferences and energy cost. In this paper, a distributed light control system with WSANs considering daylight is constructed. Sensors measure illuminations while actuators make decisions and carry out appropriate actions. The feedback from sensors makes the system achieve a target illumination accuracy and thus human perceptions are improved. The control inputs are determined by local interaction between sensors and actuators. Thus, our method is distributed and hence the system can be easily extended to a large scale. Simulation results verify the effectiveness of our design.

Autonomous platoon control with multiple disturbances

This paper concerns multiple disturbance propagation in a platoon of vehicles that consists a leader and many followers. It is known that a small disturbance acting on leader or some follower may cause large effect on other vehicles, and even propagates incrementally along the platoon. To make the effect of disturbances as far as possible little, we design a control law that uses only information of vehicle itself and its immediate predecessor, and derive the string stability conditions and the upper bounds of the transfer function matrix Gde from disturbances to spacing errors as well as the transfer function matrix Gue from leader input control to spacing errors. These derived bounds are independent of the number of the platoon. Simulations verify the proposed method is effective.