System parameter identification experiment based on hopfield neural network for self balancing vehicle

In recent years, the system parameter identification attracts attention of scholars and engineers, as the system control precision can be improved with it. In this paper, we take advantage of Hopfield neural network to identify the system parameters. Firstly, a kind of self balancing vehicle dynamic model is proposed. Secondly, the subsystem of the self balancing vehicle is analyzed in the form of Hopfield neural network. Finally, taking the actual parameter into the dynamic model, the simulation of the system parameter identification is presented. From the simulation result, there are errors between the actual value deviation and the system parameter identification result achieved in the paper, but it is within the acceptable range, so the identification results are acceptable.