基于改进型神经动态规划算法的单容液位优化控制

单容液位控制系统是一个强非线性、多约束、时滞的复杂系统,传统的PID控制算法很难对其进行精确自适应优化控制。介绍了一种改进型的神经动态规划(NDP)算法,其中模型网络用小波神经网络来替代,并针对单容液位控制系统的液位进行自适应优化控制。仿真结果表明,基于神经网络的NDP算法在鲁棒性、控制精度和控制效果都优于传统的PID算法。

Optimal control based on improved neural dynamic programming algorithm for single-tank liquid level

Single-tank liquid level system is a complex system which is nonlinear, multi-constraint, and delays. It is difficult of traditional PID control algorithm to adaptively adopt optimal control on single-tank liquid level system. In this paper, an improved neural dynamic programming (NDP) algorithm which the model network is wavelet neural network is introduced to solve the problem adaptively adopt optimal control on liquid level of single-tank liquid level system. The results show that NDP algorithm has the better robustness, control accuracy and control effects than traditional PID control algorithm.