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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) in the process industry has been more mature. Its core is to solve the quadratic programming (QP) problem online. When the computing load is prolonged and the computing power of the controller is high, it hinders the MPC from expanding to deeper and wider application fields. In order to solve the above problems, a new scheme of MPC algorithm is proposed from two aspects of algorithm itself and hardware platform. The new MPC algorithm based on Particle Swarm Optimization (PSO) algorithm solves the constrained quadratic programming problem very well and realizes the PSO-MPC controller based on the programmable logic gate array (FPGA). This program makes MPC can be used in the controller volume is limited, high sampling frequency motion control applications.