Model predictive control for perturbed continuous piecewise affine systems with bounded disturbances

Continuous piecewise-affine systems are a powerful tool for describing or approximating both nonlinear and hybrid systems. In this paper, we extend the model predictive control (MPC) framework for continuous piecewise-affine systems that we have developed previously to deterministic uncertainty. We show that the resulting MPC optimization problem can be transformed into a sequence of linear optimization problems (LP), which can be solved very efficiently.