Companion nonlinear system control based on adaptive RBF network compensation

In order to counteract the nonlinear term in companion nonlinear system,a controller can be designed to precisely linearize the nonlinear system.Generally,there are uncertain factors existing outside the system which lead to the system model uncertainty,so the controller cannot be designed directly.The uncertain term in the system model was adaptively identified by using the principle of that RBF neural network could approximate any continuous function with any precision.The identified result was provided to the controller and it realized the adaptive compensation control of the companion nonlinear system based on neural network.The designed controller was used to control swing angle subsystem of crane-load system.Experiment results showed that the swing angle of the load and the angular velocity of the swing angle were well controlled in about 5s,and the approximation error of the uncertain term in the system model could reach zero at about 5s;the designed controller had strong robustness against the uncertain factors of the system and the change of the system parameters.