摘要
弹性轴类零件液压伺服扭转振动试验机在实验过程中,由于系统非线性及负载变化或干扰因素的影响,其控制系统参数及数学模型易发生改变,导致控制效果变差。针对该试验机的控制系统,提出了基于BP神经网络(BPNN)的PID自适应控制算法。利用MATLAB/Simulink工具箱对该算法进行仿真实验。结果表明:结合了神经网络特点的智能PID控制器具有响应快、精度高、鲁棒性好和抗干扰能力强等优点,改善了控制系统的动态性能。
The hydraulic servo control system of torsional vibration testing machine used on flexible shaft doesn＇t perform good enough in the process of the test. The parameters or mathematic model of the electro-hydraulic servo control system could be changed by the nonlinear, change of load or noise from outer space, leading to bad control effect of the system. Based on the control system of the testing machine, an a- daptive PID control algorithm is provided based on back propagation （BP） neural network. And a simulation experiment is given using the Simulink tool box of MATLAB. The results show that the PID controller based on BP neural network can get better control characteristics and adaptability, rapid response, high precision, strong robustness and good antl-jamming ability. The dynamic performance of the control system has been improved.
出处
《世界科技研究与发展》
CSCD
2013年第6期709-711,716,共4页
World Sci-Tech R&D
基金
国家自然科学基金(51175525)
重庆大学机械传动国家重点实验室自主研究基金(0301002109137)资助
