Robustness and fault-tolerance of cubic neural network intelligent control method - comparison with sliding mode control

This study aims at establishing a robust intelligent control method with higher control performance and wider applicable region by extending the cubic neural network (CNN) intelligent control method which consists of multilevel parallel processing on different degrees of abstraction. In particular, this study deals with a nonlinear and failure-proof control problem. In the control, the dynamical energy principle is embedded into an integrator neural network of the integrated CNN (ICNN). The proposed ICNN is applied to a control problem of a swung up and inverted pendulum mounted on a cart for the case that arbitrary initial condition of pendulum angle. In order to confirm the performance of the ICNN controller, computer simulations and experiments using a real apparatus were carried out for the cases of parameter variation and sensor failure. As a result, it is demonstrated that the ICNN controller can stand up the pendulum taking into account the cart position limit at abnormal simulations. Then, the robustness and the fault-tolerance of the proposed CNN controller were verified in comparison with the sliding mode control technique.