RBF Neural Network PID Decoupling Control Simulation

RBF neural network PID decoupling control simulation represents a highly popular research domain that integrates the characteristics of neural networks with PID control to optimize and decouple control systems. This approach utilizes radial basis function (RBF) neural networks to model nonlinear system components, coupled with traditional PID controllers to achieve precise control of complex systems. Implementation typically involves MATLAB/Simulink coding structures where RBF networks handle nonlinear approximation through Gaussian activation functions, while PID controllers manage linear dynamics using error-calculating algorithms. The method not only enhances system control performance through adaptive weight adjustments but also reduces debugging and maintenance efforts via automated parameter tuning. Consequently, RBF neural network PID decoupling control simulation demonstrates broad application prospects in engineering fields such as industrial automation and robotics.