Efficient scheduled stabilizing model predictive control for constrained nonlinear systems

We present a computationally efficient scheduled model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local predictive controllers with estimates of their regions of stability covering the desired operating region, and implement them as a single scheduled MPC which on‐line 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 the scheduled MPC design. The algorithm is illustrated with two examples.