基于迭代算法和神经网络的PID控制参数的优化与应用

Optimization and application of PID control parameter based on iterative algorithm and neural network

根据传统PID控制技术中的参数优化难题以及就PID控制用于轨迹跟踪时存在收敛速度慢的问题,提出了采用BP网络以及迭代算法相结合,为PID控制提供最优参数。通过利用神经网络具有自学习、自组织和并行处理等功能和对复杂系统控制可以达到满意效果的优势以及基于迭代学习算法,使PID控制更加精确。在车型机器人中进行了仿真实验并验证了该方法的有效性。

According to the conventional PID control parameters in the optimization problem and the PID control for trajectory tracking of the existence of slow convergence problem,the method combining BP network with iterative algorithm was proposed,in order to provide optimal parameters for PID control.By using the neural network with self learning,self organization,parallel processing function,being able achieving satisfactory effect and advantage based on iterative learning algorithm,thus the PID controls were more precise.The simulation experiments were conducted with the car-like robot and the effectiveness of the method was verified.