A Semismooth Predictor Corrector Method for Suboptimal Model Predictive Control

Understand

Suboptimal model predictive control is a technique that can reduce the computational cost of model predictive control (MPC) by exploiting its robustness to incomplete optimization.

• Instead of solving the optimal control problem exactly, this method maintains an estimate of the optimal solution and updates it at each sampling instance.
• The resulting controller can be viewed as a dynamic compensator which runs in parallel with the plant.
• This paper explores the use of the semismooth predictor-corrector method to implement suboptimal MPC.