Stability properties of the adaptive horizon multi-stage MPC

This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical stability (ISpS). The proposed algorithm employs parametric nonlinear programming (NLP) sensitivity and terminal ingredients to determine the minimum stabilizing prediction horizon for all the scenarios considered in the subsequent iterations of the multi-stage MPC. This technique notably decreases the computational cost in nonlinear model-predictive control systems with uncertainty, as they involve solving large and complex optimization problems. The efficacy of the controller is illustrated using three numerical examples that illustrate a reduction in computational delay in multi-stage MPC. •We present an adaptive horizon multi-stage MPC for fast computation times.•Recursive feasibility is guaranteed for a fully branched scenario tree.•With guaranteed robust constraint satisfaction, the controlled system is ISpS stable.•Numerical examples show faster solve times and near-identical control performances.