Robust Bionic Learning System Design Based on FBFN and Its Application to Motion Balance Control

Aiming at the motion balance control problem of a two-wheeled upright robot and combining OCPA(operant conditioning probabilistic automaton) bionic learning system,a robust bionic learning control scheme based on fuzzy basis function is designed.It doesn't require prior knowledge of dynamic system or off-line learning phase.The architecture of the robust bionic learning controller contains a bionic learning unit,a gain controller unit and a robust adaptive control unit The bionic learning unit is realized based on fuzzy basis function network(FBFN),and it is not only employed to generate operant action for approximating nonlinear parts,but also to evaluate the operant action.Based on the error measurement signal provided by performance measurement mechanism,the orientation information is generated to tune operant action generation network.The function of the gain controller unit is to guarantee the stability and performance of system,and the function of the robust adaptive unit is to eliminate the approximation error of the FBFN and external disturbances.Besides, the proposed scheme can significantly shorten the learning time and guarantee the stability of system by on-line tuning all parameters of FBFN based on Lyapunov stability theory.The stability of the robust bionic learning controller is proved in theory,and its feasibility and effectiveness can be demonstrated from the results of simulation experiment.