It is the first time to study LEII-MPC for switched systems. Compared with existing works about the MPC method, an intelligent MPC controller is presented with tracking experience inheritance from previous iteration rounds, which is an important and valuable work in optimized control for complex systems. Moreover, currently, most network security methods only consider resisting attacks, ignoring data compensation. For complex systems with network transmission, the transferred data are more critical and valuable. In this article, the proposed anti-attack method combines data compensation mechanism for not only resisting the impact of attack but also compensating for the damaged data. This proposed study provides a new method for the network transmission security of control system information.

This article studies learning empirical inherited intelligent model predictive control (LEII-MPC) for switched systems. For complex environments and systems, an intelligent control method design with learning ability is necessary and meaningful. First, a switching law that coordinates the iterative learning control action is devised according to the average dwell time approach. Second, an intelligent MPC mechanism with the iteration learning experience is designed to optimize the control action. With the designed LEII-MPC, sufficient conditions for the switched systems stability equipped with the event-triggering schemes (ETSs) in both the time domain and the iterative domain are presented. The ETS in the iterative domain is to solve unnecessary iterative updates. The ETS in the time domain is to deal with potential denial of service (DoS) attacks, which includes two parts: 1) for detection, an attack-dependent event-triggering method is presented to determine attack sequence and reduc...