Nonlinear system identification of bicycle robot based on adaptive neural fuzzy inference system

Abstract

System identification is the basis of designing control system. The bicycle robot is an under-actuated, non-linear, non-integrated system with lateral instability, it's two wheels are longitudinal and has non-sliding contact with the ground, meanwhile it's dynamic characteristics are complicated. So it is very difficult to set up more precise dynamics model. While precise model of complex system often requires more complex control design and calculation. In this paper, linear ARX model and nonlinear ANFIS model are proposed. The identifications of bicycle robot system are completed through the data of handlebar angle and those of inclination angle which are gathered when bicycle robot is stable. Simulation result by ANFIS based on T-S model could be very similar to the actual test data of bicycle robot sysytem, and it's identification precision is higher than that of linear ARX model. The obtained conclusions of fuzzy inference between input and output by above identificaton methods can provide some reference value for effective control on bicycle robot system in future. © 2010 Springer-Verlag.