An Optimized Neuro-Fuzzy Controller Design for Bipedal Locomotion

al. [3] in the case of a humanoid robot based on hybrid fuzzy control methods. This paper proposes a bipedal locomotion controller using an optimized Neuro-Fuzzy inference structure. In which, it is based on an adaptive neuro-fuzzy inference identifier (ANFII) for modeling the inverse dynamics of the biped robot. The proposed learning scheme is based on the zero moment point (ZMP); while the modeling is accomplished, the parameters of the bipedal locomotion controller will be duplicated by those of the ANFII respectively to provide favorable control actions. As the simulation results show, the proposed controller can generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory whenever the robot stays in different postures or moves on a rugged surface. The approach proposed by Juang [4,5] rests on the incorporation of adaptive neural networks and various fuzzy control schemes, all of which emphasize synthesis of the robotic gait and dynamic controls. On the other way, the study carried out by Szumiński and Kapitaniak [6] focuses on identifying ways of avoiding dangerous situations that occur during periodic robot motion and that are caused by different kinds of vibrations. Two methods are commonly proposed to produce suitable mathematical models of biped robot dynamics: the Lagrange Equation and the Newton-Euler Method. However, these methods and their corresponding analyses run the risk of failing in cases where the biped robot faces complex or undetectable environmental conditions. Fortunately, the ZMP (Zero Moment Point) trajectory proposed by Vukobratović et al. [7] has proven effective in estimating robotic stable states. The ZMP refers to the point of contact between the foot and the ground at which the dynamic reaction force produces no moment, that is, the point at which the total inertia force equals zero. The ZMP can also be regarded as the center-of-gravity position for walking biped robots.