气动油压伺服系统的智能PID控制研究

针对经典的基于对象精确模型的PID控制方法自适应性差,难以适应具有非线性、时变不确定性的被控对象,提出了一种基于RBF神经网络的、结构简单的PID自适应控制方法。将该智能PID控制应用于气动油压伺服系统中,实验结果表明:具有自学习和自适应能力的RBF网络PID控制方法,能够适应被控对象在较大范围内的变化,具有较强的鲁棒性,其控制品质明显优于常规PID控制方法,将其应用于气动油压伺服系统是可行的。

Study On Intelligent PID Control of Fuel Oil Pressure Pneumatic Servo System

Because Classic PID Control method which was based on precise mathematical model had poor adaptability and was not adaptive to nonlinear and time-variant plants,the PID controlling algorithm which was based on RBF neural network and had a sim-ple structure was provided.When it is applied to the fuel oil pressure pneumatic servo system,the result shows that the PID con-trolling method based on RBF neural network which has self-study and self-adaptability can be adaptive to great change of con-trolled plant,has excellent robustness,and is better than conventional PID control in controlling performance.The PID control that is applied to the fuel oil pressure pneumatic servo system is effective.