PID控制器的模糊优化与参数学习自整定(英文)

Fuzzy Optimization and Learning Based Parameter Auto-tuning Method for PID Controller

为了解决一次性整定设置的PID控制参数难以确保控制系统始终处于最佳状态，探讨了PID控制器的模糊优化与参数学习自整定方法。基于控制参数调整的模糊性分析，总结了控制参数的整定原则，研究了参数模糊自整定的机理，讨论了评价函数，定义了奖惩函数，提出了奖惩自学习算法，设计了含参数学习自整定的控制系统结构。采用上述方法以某车间环境温度控制为例，控制系统运行结果显示，可将温度稳定地控制在期望的23—24℃范围内。工程应用效果表明：该方法稳态控制精度高，适应能力强，能较好地满足高精度控制的工况需要。

In order to ensure the best situation of control system all the time, which is difficult to PID control parameter by disposable tuning setting, the paper explored the fuzzy optimization and learning based parameter auto-tuning method For PID controller. Based on the analysis in fuzzifi- cation of control parameter adjusting, it summarized up the tuning principle of control parameter, researched on mechanism of fuzzy auto-tuning of the parameter, discussed the evaluation function, defined the function of reward and punishment, proposed the self-learning algorithm of reward and punishment, and designed the structure of control system with parameter based learn- ing auto-tuning. By means of the above method, the paper took the environment temperature control of a certain workshop as an example, the running result of control system demonstrated that the temperature could stably control in expected range from 23℃ to 24℃. The effect of engineering application shows that it can better satisfy the field demand of the workshop, and it is high in steady state control precision, and strong in adaptive capability.