一种新的基于PSO的MPC及其硬件实现

PSO Based Model Predictive Control and its Hardware Implementation

模型预测控制(MPC)在流程工业中应用已经比较成熟。其核心为在线求解二次规划(QP)问题,运算负荷大时延长,对控制器的运算能力要求高,阻碍了MPC向更深更广的应用领域拓展。为解决上述问题,从算法本身和硬件平台2个方面入手,提出了MPC算法一种新的实现方案。新的以粒子群优化算法(PSO)为核心的MPC算法很好地解决了带约束的二次规划问题,并且以可编程逻辑门阵列(FPGA)为平台用实现了PSO-MPC控制器。这一方案使得MPC可以应用在控制器体积受限,采样频率高的运动控制场合。

Model predictive control（MPC） is an established control strategy used in the process industry. However, its computational efficiency becomes the main hindrance to its application in higher bandwidth applications, such as in motion control problems. Unlike controllers for process industries, the motion controller must have specific properties, including limited size and high sampling frequen cy. To meet these requirements, this paper explored the implementation of a specified new MPC using PSO as the QP-solver, called PSO-MPC, on a field programmable gate array （FPGA） chip. The PSO-MPC-on-FPGA strategy addresses size constraints and satisfies the need for high sampling frequency by exploiting the parallel features of both the PSO-MPC and the FPGA chip.