Search

This paper is concerned with the issue of weighted ${\mathcal{H}_\infty }$ consensus for discrete-time multi-agent systems with time-varying delay and switching topologies. The objective is designing a dynamic output feedback protocol in the presence of external perturbations such that the system achieves consensus in mean square and has a prescribed ${\mathcal{H}_\infty }$ performance level. Firstly, the ${\mathcal{H}_\infty }$ consensus design is converted to a standard ${\mathcal{H}_\infty }$ control problem by reconstructing the closed-loop system as a reduced-order system. Then, by means of the multiple Lyapunov-Krasovskii functional method, the delay-dependent bounded real lemma is derived to guarantee the stability of closed-loop system. Based on these, sufficient conditions for the existence of the required dynamic output feedback protocol is developed in the form of a series of solvable linear matrix inequalities. Finally, numerical example and simulation results are given to illustrate the effectiveness of the obtained results.

The problem of H ∞ consesus control for asynchronously switched continuous-time multi-agent systems is investigated in this paper. There always exists asynchronous switching due to time delay and packet losses of network transmission which means that the switching of controllers will lag behind the switching of system modes, which will lead to performance degradation. First, the model of switched multi-agent systems is established. The H ∞ consensus issue can be converted as a H ∞ control problem via system reduction. Considering asynchronous switching phenomenon, the mean-square asymptotic stability is analyzed by the aid of multiple Lyapunov functional method and average dwell time (ADT) method. The sufficient conditions to ensure the closed-loop stable with prescribed performance are given in the form of linear matrix inequalities (LMIs). In the end , an example is given to verify the feasibility of the proposed approach.

The problem of fault detection for unmanned flight vehicle is investigated in the paper. Firstly, the linear switched model of flight vehicle is obtained via nonlinear dynamic model. The fault detection filter based on robust control theory is designed to generate the residual signal. To achieve better performance, deep reinforcement learning is applied to tune the parameters. The scheduling interval can be viewed as a neighborhood of parameters obtained by robust control theory. The stability of closed-loop system is guaranteed by the aid of average dwell time method and Lyapunov functional method. Simulation results in the end demonstrate the effectiveness and superiority of proposed method.

The problem of intelligent control for unmanned flight vehicles is studied in this paper. The linear model of unmanned flight vehicle is obtained by Jacobian linearization method according to the nonlinear model. The process of controller design can be divided into two steps. Firstly, to ensure the stability and prescribed performance of the closed loop system, the robust controller is given in terms of linear matrices inequalities based on robust control theory. Secondly, the intelligent controller is proposed to improve the transient performance via deep reinforcement learning. The parameters of controller can be tuned automatically in a neighborhood of robust controller's parameters. In the end, the simulation results are given to illustrated the effectiveness and superiority of the proposed method.

The fault detection problem is investigated for the morphing flight vehicle with different switched sweepback during flight. First, the flight dynamics is modeled as a switched system based on the theory of the multi-rigid-body and switched system. Then, the discrete model is presented by the theory of stochastic control theory. Also, the robust fault detection filter for the discrete switched control system is designed with H-/H ∞ performance index. Depending on multiple Lyapunov function and the average dwell-time method, we analyze the system stability and the sufficient existing conditions of filtered are derived in the form of LMIs. Finally, numerical examples based on Fire-Bee morphing flight vehicle are given to illustrate the efficiency of the proposed method.