A Computationally Efficient Robust Model Predictive Control Framework for Uncertain Nonlinear Systems

In this paper, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal input) constraints. To facilitate efficient implementation, shape of is based on offline computed incremental Lyapunov function with corresponding (nonlinear) incrementally stabilizing feedback. Crucially, optimization only implicitly includes these functions terms scalar bounds, which enables implementation. Furthermore, account evaluation worst case disturbance, simple that upper bounds possible disturbance realizations neighbourhood given point open-loop trajectory. The resulting MPC scheme ensures constraint satisfaction practical asymptotic stability moderate increase computational demand compared MPC. We demonstrate applicability proposed comparison state art approaches benchmark example. paper extended version [1], contains further details additional considers: continuous-time systems (App. A), more constraints B) special cases (Sec. IV).