Steering and balance controls of an electrical bicycle using integral sliding mode control

In this paper, steering and balance controls of an unmanned electrical bicycle (e-bicycle) using an integral sliding mode control scheme including adaptive neural network disturbance elimination is proposed. The e-bicycle has three degrees of freedom in the space, and they are driven by an in-wheel brushless motor setup in the rear wheel and a servomotor on the saddle seat post steers for handlebar grip control via a planar four-bar linkage, while keeping the lean angle to be zero. Some unknown underactuated uncertainties are approximated by the adaptive neural network. The constrained Lagrangian's equations are derived to simulate the motion of the e-bicycle in state-space form with uncertainty. Based on Lyapunov stability theorem, the proposed control scheme can ensure the stability of the system and parameter estimations convergence, and achieve steering and balance controls. Finally, some simulation examples are given to show the effectiveness and validity of the proposed control scheme.