Key result
The proposed robust adaptive model predictive control framework guarantees robust constraint satisfaction and convergence for nonlinear systems with bounded parametric uncertainty.
The proposed robust adaptive MPC framework ensures constraint satisfaction and convergence for uncertain nonlinear systems.
May support robust control in uncertain devices; leaves open clinical validation before adoption.
We present a robust adaptive model predictive control (MPC) framework for nonlinear continuous-time systems with bounded parametric uncertainty and additive disturbance. We utilize general control contraction metrics (CCMs) to parameterize a homothetic tube around a nominal prediction that contains all uncertain trajectories. Furthermore, we incorporate model adaptation using set-membership estimation. As a result, the proposed MPC formulation is applicable to a large class of nonlinear systems, reduces conservatism during online operation, and guarantees robust constraint satisfaction and convergence to a neighborhood of the desired setpoint. One of the main technical contributions is the derivation of corresponding tube dynamics based on CCMs that account for the state and input dependent nature of the model mismatch. Furthermore, we online optimize over the nominal parameter, which enables general set-membership updates for the parametric uncertainty in the MPC. Benefits of the proposed homothetic tube MPC and online adaptation are demonstrated using a numerical example involving a planar quadrotor.
No takes yet. Share an insight, caveat, or question.
Sasfi et al. (2022) studied this question. Robust adaptive model predictive control (MPC) was evaluated. The proposed robust adaptive model predictive control framework guarantees robust constraint satisfaction and convergence for nonlinear systems with bounded parametric uncertainty.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: