基于神经动态优化的离散时滞系统预测控制

Predictive Control of Discrete Time-Delay Systems Based on Neural Dynamic Optimization

文章利用神经动态优化方法研究离散时滞系统预测控制问题,首先将离散时滞系统的模型预测控制问题转化为带约束的优化问题,再采用梯度神经网络进行在线求解。该神经网络具有较少的状态变量,结构简单,优化速度快,能够有效的解决带有约束的规划问题。仿真结果表明该神经动态优化方法可提高模型预测控制的在线计算能力。

In this paper, the neural dynamic optimization method is used to study the predictive control problem for discrete time-delay systems. Firstly, the model predictive control problem for discrete time-delay systems is transformed into an optimization problem with constraints, and then the gradient neural network is used to solve the problem online. The neural network has fewer state variables, simple structure as well as fast optimization speed, and can effectively solve the programming problem with con-straints. The simulation results show that the neural dynamic optimization method can improve the on-line computing ability of the model predictive control.