论文标题
双线性模型预测控制
A Proximal-Point Lagrangian Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control
论文作者
论文摘要
非线性模型预测控制已被广泛采用，以操纵具有包括输入和状态产品的动力学的双线性系统。这些系统在化学过程，机械系统和量子物理学中无处不在。实时运行双线性MPC控制器需要在有限的采样时间内解决非凸优化问题。本文提出了一种新型的平行近端Lagrangian双线性MPC求解器，这是通过交叉隔离式分裂方案。所得算法将非凸MPC控制问题转换为一组可行的小规模的多参数二次二次程序（MPQPS）和相等约束的线性季节调节器问题。结果，MPQP的解决方案可以脱机预计以实现有效的在线计算。在HVAC系统控制的模拟中验证了所提出的算法。它部署在Ti Launchpad XL F28379D微控制器上，以在现场控制的直流电机上执行速度控制，在该电场下，MPC在10 ms上更新，并平均以1.764 ms的速度解决该问题，最多最多为2.088 ms。
Nonlinear model predictive control has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, to name a few. Running a bilinear MPC controller in real time requires solving a non-convex optimization problem within a limited sampling time. This paper proposes a novel parallel proximal-point Lagrangian based bilinear MPC solver via an interlacing horizon-splitting scheme. The resulting algorithm converts the non-convex MPC control problem into a set of parallelizable small-scale multi-parametric quadratic programs (mpQPs) and an equality-constrained linear-quadratic regulator problem. As a result, the solutions of mpQPs can be pre-computed offline to enable efficient online computation. The proposed algorithm is validated on a simulation of an HVAC system control. It is deployed on a TI LaunchPad XL F28379D microcontroller to execute speed control on a field-controlled DC motor, where the MPC updates at 10 ms and solves the problem in 1.764 ms on average and at most 2.088 ms.
