Adaptive neural dissipative control for markovian jump cyber-Physical systems against sensor and actuator attacks

Abstract

This work addresses the issue of adaptive neural dissipative control for Markovian Jump Cyber-Physical Systems subject to output-dependent sensor and state-dependent actuator attacks. Attackers can inject false information into feedforward and feedback signals to degrade system performance or even destabilize the system. To identify and approximate attack signals, neural network technique is employed. Attacks are successfully withstood by constructing the estimated signals of these approximate functions. New adaptive state and output feedback controllers are being developed in the meantime. Then, by adapting Lyapunov function technique, sufficient conditions are provided to achieve the stochastic stability of the considered system with extended dissipation. Last, two practical examples are applied to elucidate the effectiveness of the devised adaptive neural control approaches.