Bicycle Robot Balance Control Based on a Robust Intelligent Controller

In recent years, human assistant transportation systems have received much attention. Based on a microprocessor, a bicycle robot (BR) is designed and implemented in this work. In this study, a robust intelligent backstepping tracking control (RIBTC) system combined with an adaptive output recurrent Takagi-Sugeno-Kang type cerebellar model articulation controller (AORTSKCMAC) and a robust controller for BRs is implemented. The bicycle robot can maintain balance when subjected to a disturbance and still go forward. The proposed RIBTC is proposed to control the lean angle of the BR. The BRs can stably stand even with external disturbances. The development of the proposed controller is combined with a backstepping technique, the adaptive output recurrent TSKCMAC and a robust control method. The adaptive output recurrent TSKCMAC is used to mimic the ideal backstepping controller (IBC) because an accurate mathematical model of the system is hard to obtain. In general, the optimal values of system parameters can be calculated based on system dynamics when the system model is always known. However, the exact system dynamics are not always known. The parameters of the adaptive output recurrent TSKCMAC are tuned online in this study. The robust controller is designed to attenuate the effect of the residual approximation errors. Moreover, the Lyapunov function is used to verify the stability and convergence of the controller. The main contributions of this work include (1) the successful design and implementation of BR hardware and (2) the successful realization of the proposed RIBTC control scheme to control the BR. Finally, the experimental results demonstrate the effectiveness of the proposed control scheme for bicycle robot systems with unknown dynamic functions.