Neural network for bicycle robot system identification

Due to the theory that the neural network can approach any nonlinear function by any precision and possesses inherent characteristics of adaptive capacity. Based on two nonlinear system models, the network structure identification of a typical nonlinear, unstable, and strong coupling bicycle robot system is established, which explains the relationship between handlebar angle and the inclination angle of bicycle during bicycle robot running stably. By comparing of the identified results, the simulation results show that it is effective for neural network to identify the nonlinear bicycle robot system.