Adaptive Critic Neuro-fuzzy Control of Two-wheel Vehicle

This paper presents an adaptive critic neuro-fuzzy control design, which enables learning from scratch to achieve the control objective. The learning algorithm is derived from the Dual Heuristic Programming (DHP) method to approximate optimal control. The learning structure contains the action, critic and verification neuro-fuzzy networks each corresponding to the first-order Sugeno fuzzy model. The critic and verification networks together forms a reinforcement learning scheme to estimate the future derivative Bellman function. The transparency of the network sensitivity function makes the update rule of the action network simple. Simulation results show that the adaptive critic neuro-fuzzy control system enables the motorized vehicle with simply two coaxial wheels learning from scratch to achieve velocity control while keeps the intermediate body in balance.