基于神经网络的电动加载系统

 针对电动加载系统中多余力矩的干扰 ,提出了基于RBF(径向基函数 )神经网络的新型复合控制策略 ,与传统的BP神经网络相比 ,没有局部最小问题。由于系统非线性和时变性 ,特别在多余力干扰下传统控制方法如PID很难得到满意的控制效果。提出的复合控制方法主要由神经网络PID和前馈补偿器组成 ,通过仿真与试验 ,控制器有效的减少了多余力矩对系统的影响 ,改善了加载系统的动态性能。

MOTOR-DRIVEN LOAD SYSTEM BASED ON NEURAL NETWORKS

Aiming at the disturbance of the extraneous force in the motor-driven load system, a new composite control strategy based on Radial Basis Function (RBF) networks is proposed. Compared with the controllers based on conventional BP networks, the presented algorithm is much more efficient without the problem of local minima. The motor driven load system is highly nonlinear and includes delays in the control loop. It is difficult for the traditional control method such as PID to improve the performance, especially under the disturbance of movement,the so called extraneous force problem. The proposed composite control scheme consists of NN PID and feedforward compensator. The experimental result shows that the scheme compensates the extraneous force effectively, and improves the dynamic performance of the load system.