Improved Model-Free Adaptive Predictive Control for Nonlinear Systems with Quantization Under Denial of Service Attacks

In this paper, an improved model-free adaptive predictive control method is presented for unknown nonlinear systems with quantization to handle the limited network transmission capacity and denial of service (DoS) attacks. Firstly, to reduce the impact of the DoS attacks on the control system, an attack compensation mechanism is designed, which can be adjusted based on different attack strategies of attackers, and the elastic control of DoS attack can be realized for systems with different complexity and attack intensity. Secondly, a uniform quantizer with encoding and decoding mechanism is presented to tackle the network bandwidth limitation and to reduce the effects of quantization errors. Subsequently, an improved model-free adaptive predictive control method is designed, which can realize the tracking control task of the system with quantization under DoS Attacks. The boundedness of the tracking error of the control system is strictly proved by theoretical analysis. The proposed algorithm has certain symmetry in the prediction time domain and the control time domain. It predicts the state and output of the system at multiple times in the future at each sampling time, and calculates the optimal control sequence according to the predicted results. This kind of symmetric relationship between the prediction of the future and the optimization of the current control is reflected in time, so as to achieve the optimal performance of the system in the whole prediction interval. Finally, the effectiveness and robustness of the presented control method are verified by simulation results under both undisturbed and disturbed conditions.