Title: Embedded Model Predictive Control Using Robust Penalty Method Show Chinese title

Title: 基于鲁棒惩罚方法的嵌入式模型预测控制

Abstract: Model predictive control (MPC) has become a hot cake technology for various applications due to its ability to handle multi-input multi-output systems with physical constraints. The optimization solvers require considerable time, limiting their embedded implementation for real-time control. To overcome the bottleneck of traditional quadratic programming (QP) solvers, this paper proposes a robust penalty method (RPM) to solve an optimization problem in a linear MPC. The main idea of RPM is to solve an unconstrained QP problem using Broyden Fletcher Goldfarb Shannon (BFGS) algorithm. The beauty of this method is that it can find optimal solutions even if initial conditions are in an infeasible region, which makes it robust. Moreover, the RPM is computationally inexpensive as compared to the traditional QP solvers. The proposed RPM is implemented on resource-limited embedded hardware (STM32 microcontroller), and its performance is validated with a case study of a citation aircraft control problem. We show the hardware-in-the-loop co-simulation results of the proposed RPM and compared them with the active set method (ASM) and interior point method (IPM) QP solvers. The performance of MPC with the aforementioned solvers is compared by considering the optimality, time complexity, and ease of hardware implementation. Presented results show that the proposed RPM gives the same optimality as ASM and IPM, and outperforms them in terms of speed. Show Chinese abstract

Abstract: 模型预测控制（MPC）由于其能够处理具有物理约束的多输入多输出系统，已成为各种应用的热门技术。 优化求解器需要大量时间，限制了它们在实时控制中的嵌入式实现。 为了克服传统二次规划（QP）求解器的瓶颈，本文提出了一种鲁棒惩罚方法（RPM），以在线性MPC中解决优化问题。 RPM的主要思想是使用Broyden Fletcher Goldfarb Shannon（BFGS）算法求解无约束QP问题。 这种方法的美妙之处在于，即使初始条件处于不可行区域，它也能找到最优解，这使其具有鲁棒性。 此外，与传统QP求解器相比，RPM计算成本较低。 所提出的RPM在资源有限的嵌入式硬件（STM32微控制器）上实现，并通过一个引用飞机控制问题的案例研究验证了其性能。 我们展示了所提出的RPM的硬件在环联合仿真结果，并将其与活动集方法（ASM）和内点法（IPM）QP求解器进行了比较。 通过考虑最优性、时间复杂性和硬件实现的难易程度，比较了上述求解器的MPC性能。 结果显示，所提出的RPM在最优性方面与ASM和IPM相同，并在速度方面优于它们。