Constrained Multilegged Robot System Modeling and Fuzzy Control With Uncertain Kinematics and Dynamics Incorporating Foot Force Optimization
研究了多足机器人在运动学和动力学不确定下的足力优化分配与控制，提出基于模糊系统和自适应机制的混合任务空间轨迹与力跟踪方法，仿真验证了有效性。
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.