A Gradient-Based Event-Driven MPC for Nonlinear Systems With Additive Disturbances Using State-Dependent Thresholds

Abstract

In this article, we propose a gradient-based event-driven model predictive control (GEMPC) algorithm with a state-dependent threshold for nonlinear systems with additive disturbances and input and state constraints. Firstly, a novel gradient-based event-driven strategy is constructed in the light of the error gradient between the optimal prediction of the state and the real one, which could ensure the Zeno-free property via a positive triggering interval. Subsequently, the novel triggering mechanism and the dualmode control are combined to establish a GEMPC framework, to further reduce the computing burden and communication transmission especially when the computational resources are limited. Additionally, the feasibility of the GEMPC algorithm and the input-to-state practical stability (ISpS) property of the considered system have been strictly proved in theory. Finally, the simulation comparison results on control of a perturbed nonlinear system are utilized to show the validity of the GEMPC algorithm.INDEX TERMS Event-driven control, model predictive control (MPC), gradient-based mechanism, constrained systems, state-dependent threshold.

Dual‐mode event‐triggered predictive control for nonlinear systems with bounded disturbances

“…[1][2][3][4] During each control period, the MPC algorithm obtains a series of operational variables by solving an optimal control problem to optimize the future state response of the system. [5][6][7] Owning to its great potential applications, MPC has been continuously developed and improved in practical applications and has been widely used in electrical power systems, chemical industries, aerospace field, and other industrial fields. [8][9][10][11][12] In the conventional MPC, the optimal control problem needs to be solved at every sampling time.…”

“…Model predictive control (MPC), also called receding horizon predictive control, is a control strategy which uses explicit linear or nonlinear dynamics to predict the future state response of the system 1‐4 . During each control period, the MPC algorithm obtains a series of operational variables by solving an optimal control problem to optimize the future state response of the system 5‐7 . Owning to its great potential applications, MPC has been continuously developed and improved in practical applications and has been widely used in electrical power systems, chemical industries, aerospace field, and other industrial fields 8‐12 …”