水轮机调节系统的模糊神经元控制

水轮机调节系统具有非线性和时变性的特性 ,传统的PID控制器无法获得高性能的调速系统。针对水轮机调节系统的上述特性 ,在常规模糊控制的基础上 ,采用径向基函数神经网络来逼近过程模型 ,并设计了自适应神经元来在线优化控制器的输出。理论分析和仿真结果表明 ,这种控制策略与传统的PID控制和模糊控制相比 ,能够得到更快的响应速度和更好的控制效果

Fuzzy-neural control of hydraulic turbine regulation system

With the nonlinear and time variable characteristics of hydraulic turbine regulation system,high qualified speed adjustment system cannot be achieved with the conventional PID controller.In the light of these,we adopt radial basis function neural network to gain on the process model and design adaptive neuron to optimize the output of the controller based on the general fuzzy control.The theoretical analysis and the simulation results show that this approach is superior to the conventional PID controller and fuzzy controller in response and control effectiveness.