Computationally efficient scheduled model predictive control for constrained nonlinear systems with stability guarantees

We present a computationally efficient scheduled formulation of model predictive control (MPC) for constrained nonlinear systems with large operating regions. We design a set of local predictive controllers with their explicit regions of stability covering the desired operating region, and implement them as a single scheduled MPC which switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm is computationally efficient and provides a general framework for scheduled MPC design.