Resolved motion model based predictive control of redundant robots

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Abstract

Flexibility and versatility are two basic requirements for industrial robots used in manufacturing areas. In the present paper, a kinematic transformation for redundant robots between Cartesian and joint coordinates is achieved in the trajectory planner. This problem is treated by taking into account both the dynamics of the system expressed by its Lagrangian and the kinematic constraints of the robot. Given the joint trajectories as above, a control law for joint trajectory tracking is generated here using the Model Based Predictive Control (MBPC) approach. The MBPC strategy is based on an explicit robot model to predict the process output over a long-range time period. The postulated control law is constructed such that to fit a desired command acceleration for the servo motors of the robotic system. The actual control law is calculated by minimizing a suitable objective function. A numerical example concerning a robot with one degree of redundancy illustrates the method and shows the excellent performance of the proposed MBPC scheme. The drifting problem that emerges in the resolved motion control of redundant robots is successfully faced.

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