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.

为了减轻模型预测控制的在线计算负载，通常使用移动阻塞，它通过在任意时间间隔内固定决策变量来参数化输入序列或偏离基本序列。然而，现有的移动阻止方案仅使用固定的碱基序列，并没有充分利用各种碱基序列的有价值的特性。因此，我们提出了基于内插解的移动阻塞策略，该策略将来自两个互补基本序列的凸组合的偏移量参数化 - 无限水平线性二次调节器解和移位前解 - 并优化插值参数作为附加决策变量最优控制问题。这允许控制器通过根据当前在线状态选择最佳插值参数和块偏移来利用两种解决方案的有价值的特性。所提出的方法有效地提高了最优性能，同时保证了递归可行性和闭环稳定性。