A quasi‐differential type event‐triggered model predictive control for perturbed continuous linear systems with constraints

Abstract

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.

Event-Triggered Second-Order Sliding Mode Controller Design and Implementation

“…The key difference between ST and MPC is the intention of triggering mechanism relaxation without further controller move optimization. Regarding the latter, the MPC is subsequently the objective of the ET design studied in works [23][24][25]. The accuracy of the prediction is the subject of much research.…”

Event‐triggered MPC for linear systems with bounded disturbances: An accumulated error based approach

“…By comparing the state discrepancy between the event‐triggered and the time‐triggered method, the stability property was obtained. For continuous‐time linear systems with additive disturbances, a quasi‐differential type ET‐MPC algorithm was designed in [14], in which the triggering condition was based on the differences of the errors between the real and the optimal prediction states at two continuous sampling times. In [15], an ET‐MPC of the linear continuous‐time system was studied, in which the recalculation of the control signal was determined by the error between the state real value and predicted value.…”

“…Therefore, it is desired that MPC be combined with eventtriggering strategy to save communication resources and online computing resources. There are many research results related to event-triggered MPC (ET-MPC), mainly divided into two categories, that is, ET-MPC for linear systems [13][14][15][16][17][18] and for nonlinear systems [19][20][21][22][23][24][25]. More specially, in [13], for discrete-time linear systems with constraints and external disturbances, an ET-MPC method was proposed.…”