Fast predictive control algorithm based on an FPAA analog neural network

Abstract

A fast predictive control algorithm was developed for simpler, faster MPC optimization based on an FPAA analog neural network. An FPAA analog circuit provides the quadratic programming using a continuous neural network which avoids the convergence problem of discrete neural networks in a fast, simple and flexible algorithm that uses less computational resources than previous methods. The signal constraint of the FPAA analog circuit is solved by translation and scaling. The software and hardware design of the FPAA analog neural network predictive control is presented and verified in tests that show that the algorithm is effective. 摘要 针对模型预测控制 (model predictive control, MPC) 优化求解中占用资源较多、实时性较低且实现相对复杂的问题, 该文提出了一种基于现场可编程模拟阵列 (field programmable analog array, FPAA) 模拟神经网络的快速模型预测控制算法。通过FPAA模拟电路来实现基于连续神经网络的二次规划求解, 有效规避了离散神经网络的收敛性问题, 具有求解速度快、占用资源小、简单易实现的特点; 通过平移变换和尺度变换方法, 解决FPAA模拟电路的信号限制。最后该文给出了FPAA模拟神经网络预测控制软硬件设计方案并通过实验验证了该算法的有效性。