Adaptive torque control using RBF neural networks for nonlinear DC chassis dynamometer drive

For the DC chassis dynamometer, a nonlinear mathematical model was established based on the analysis of the transmission system of the DC dynamometer, and an adaptive controller based on RBF NN (radial basis function neural network) was proposed to control a dynamometer to load resistance intelligently to achieve stepless simulation of inertia. By using the Lyapunov synthesis approach, it was proved that the closed-loop system is uniformly ultimately bounded in the presence of bounded neural network approximation error and bounded disturbance force. Simulation results show that the developed controller can offer a good control performance.