An Explicit Dual Control Approach for Constrained Reference Tracking of Uncertain Linear Systems

A finite horizon optimal tracking problem is considered for linear dynamical systems subject to parametric uncertainties in the state-space matrices and exogenous disturbances. suboptimal solution proposed using a model predictive control (MPC) based explicit dual approach, which enables active uncertainty learning. novel algorithm design of robustly invariant online terminal sets controllers presented. Set membership identification used update parameter online. predicted worst-case cost MPC optimization effect input. The cost-to-go estimated contractivity set remaining time horizon, so that optimizer can estimate future benefits exploration. ensures robust constraint satisfaction recursive feasibility, navigates exploration-exploitation tradeoff performance metric.