A quasi‐differential type event‐triggered model predictive control (ET‐MPC) framework for continuous‐time linear systems with additional disturbances is constructed. Different from the existing ET‐MPC, the triggering condition of the proposed method is focused on the differences of the errors between the actual states and the best prediction sequence at two consecutive sampling moments. Its advantage is that the dynamic characteristics of state changes can be better considered, which will achieve a more effective balance between communication load and system control performance. Then, to deal with persistent disturbances, a time‐varying tightening robustness state constraint is utilized in this scheme, instead of conventional state constraints. Finally, the new ET‐MPC algorithm is designed based on the dual‐mode control framework, and its feasibility, the system closed‐loop stability, as well as avoidance of Zeno behaviour are rigorously verified. Simulations and comparisons are provided to demonstrate the algorithm effectiveness.
An integral‐differential combined (IDC) type event‐triggered model predictive control (ET‐MPC) strategy is constructed for linear time‐invariant (LTI) systems in this paper. Compared with the standard ET‐MPC method, the proposed MPC algorithm is developed based on an IDC type event‐triggering mechanism (ETM), which takes both the integral and change rate of the error between the optimal prediction and the real state into the consideration for the construction of the triggering condition. In this way, the dynamic performance can be considered more comprehensively, for effectively reducing the calculation and communication burdens of the ET‐MPC method. In addition, an improved robust constraint is used to deal with additive disturbances, and the dual‐mode control method is employed in the development of the strategy. Then the parameter conditions guaranteeing the iterative feasibility of the proposed algorithm, the closed‐loop stability of the LTI system, and the avoidance of Zeno behavior are given. Finally, two simulation examples demonstrate the effectiveness of the algorithm.
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