Neural network supervisory control with parameter optimization on line
Using the full-state dynamic linearization with auxiliary variables,the nonlinear system is ap-proximated,and a neural network supervised control prediction model was built.The model parameter was es-timated by nonlinearity recursive least squares method.Transient process was built by linear tracking differen-tiator.Using linear expanded observer to estimate output predicative value and its differential,a linear PID control algorithm was obtained.Modified control object function by direct inverse control built from diagonal regression neural network.The parameter of PID control and connect weight of the diagonal regression neural network were optimized on line by nonlinearity recursive least squares method.When error of system control is greater than setting value,parameter of PID control would be resettled.In summary of study above,an algo-rithm of neural network supervisory control with parameter optimization on line has been developed,which overcomes the problem presented in the already present neural network supervisory.Simulation result indi-cates that response of the algorithm has excellent performance.
neural network supervised controlnonlinear systemlinear PID controlfull from dynamic linearizationdiagonal regression neural networknonlinearity recursive least squares method