A New Learning Algorithm to Control an Autonomous Biped Robot
نویسندگان
چکیده
In this paper an adaptive neural-fuzzy walking control of an autonomous biped robot is proposed. This control system uses a feed forward neural network based on nonlinear regression. The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system. The membership functions used in the antecedent part of the fuzzy system are asymmetric and with varying shapes which is less common in the fuzzy modelling literature. The neural network uses a new iterative grid partition method for the initial structure identification of the controller parameters. The proposed new approach is tested with a biped robot developed in the Institute of Systems and Robotics of the University of Coimbra, Portugal. Comparison results are done between the proposed method and the ANFIS tool provided in the fuzzy MATLAB toolbox. This robot uses an inverted pendulum for his balance. Therefore, the control system uses the balance of the gaits for the correction of the lateral and longitudinal angle of the pendulum. This robot can walk in horizontal, sloping planes, and to go up and down stairs. Mathematical models of the static and dynamic walking of the biped robot are also presented. With these models it is possible to simulate the movement of the robot and test the control algorithms. The effectiveness of the proposed control system is demonstrated by simulation and experimental tests. Key-Words: Neuro-fuzzy Systems, Biped robot, Control, Inference, Learning, Zero Moment Point.
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