Move blocked model predictive control with guaranteed stability and improved optimality using linear interpolation of base sequences

Abstract

To mitigate the online computational load of model predictive control, move blocking, which parameterises either the input sequence or offset from the base sequence by fixing the decision variables over arbitrary time intervals, is commonly used. However, existing move blocking schemes use a fixed base sequence only and do not fully exploit the valuable properties from various base sequences. Thus, we propose the interpolated solution-based move blocking strategy which parameterises the offset from the convex combination of two complementary base sequences - infinite-horizon linear quadratic regulator solution and shifted previous solution - and optimises the interpolation parameter as an additional decision variable in the optimal control problem. This allows the controller to exploit the valuable properties from both solutions by choosing the optimal interpolation parameter and blocked offset according to the current state online. The proposed approach efficiently improves the optimality performance whileguaranteeing the recursive feasibility and closed-loop stability.