Constrained Multilegged Robot System Modeling and Fuzzy Control With Uncertain Kinematics and Dynamics Incorporating Foot Force Optimization

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 46; no. 1; pp. 1 - 15
• Main Authors: Li, Zhijun, Xiao, Shengtao, Ge, Shuzhi Sam, Su, Hang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Constraints; Dynamical systems; Dynamics; Foot; Force; Forces distribution; Fuzzy logic; fuzzy-based motion/force control; Kinematics; kinematics and dynamics uncertainties; Legged locomotion; multilegged robot; Neural networks; Nonlinear dynamics; Optimization; Robots; Uncertainty; multilegged robot; Forces distribution; fuzzy-based motion/force control; kinematics and dynamics uncertainties
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2015.2422267

This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.