Finite-horizon tracking control for a class of stochastic systems subject to input constraints and hybrid cyber attacks.

This paper is concerned with the probabilistic-constrained finite-horizon tracking control problem for a class of stochastic systems subject to randomly occurring hybrid cyber attacks and input constraints. Both the randomly occurring denial-of-service (DOS) attacks and randomly occurring deception attacks are considered in an unified framework. The purpose of the current study is to design an observer-based tracking controller such that: over a finite horizon, (1) the variance of the estimation error is less than certain bound at each time step, (2) the probability of the tracking error falling in certain region should larger than a specified value and the region is minimized at each time step. To achieve those purposes, an improved multi-dimensional Chebyshev inequality method is first utilized to convert the probabilistic constraint to a deterministic one. Then an observer-based tracking control method is designed to estimate the state and design the tracking controller, which are realized through solving a set of recursive linear matrix inequalities. By using the proposed algorithm, the observer and tracking controller gains can be solved out in terms of the solution to a convex optimization problem. A simulation example is finally given to demonstrate the effectiveness and applicability of the proposed method.