Advanced, optimization-based control methods are implemented at each level of industrial production. Although the model predictive control (MPC) represents a state-of-the-art control strategy maximizing profit, and, simultaneously, minimizing the energy losses, its industrial implementation is limited by the requirements on the software and hardware resources. This paper proposes an approximated MPC for such cases when the implementation of implicit MPC has a prohibitive effort on industrial hardware and its explicit counterpart is limited by its memory footprint requirements. The recursive random shooting-based approach is introduced to eliminate the conventional optimization and minimize the memory requirements, meanwhile guarantying the asymptotic stability subject to the physical constraints on control inputs and system states. The benefits of the proposed method are significantly reduced computational complexity by 93%, and decreased energy consumption by 93%, compared to non-recursive random shooting, while the performance loss is approximately 8%.

在工业生产的各个层面实施先进的、基于优化的控制方法。尽管模型预测控制 (MPC) 代表了一种最先进的控制策略，可最大限度地提高利润，同时最大限度地减少能量损失，但其工业实施受到软件和硬件资源要求的限制。当隐式 MPC 的实现在工业硬件上的工作量很大并且其显式对应物受到其内存占用要求的限制时，本文提出了一种近似 MPC。引入基于递归随机射击的方法以消除常规优化并最小化内存需求，同时在控制输入和系统状态的物理约束下保证渐近稳定性。