Model predictive position/force control of an anthropomorphic robotic arm

When a robotic manipulator executes a task in restricted and unknown environments, it is necessary to implement a position/force control in free space and constrained space. In classic controllers, position/force control may be carried out by means a controller switching, once the contact point has been reached. In this paper, a new position/force predictive controller for an anthropomorphic robotic arm is proposed. This work proposes an unique control law for controlling both variables (position or force). In a model predictive framework, where several goals have to be reached, it is known that cost function includes several parameters (weights) whose modification allows to prioritize goals to achieve. This fact has been taken into account to set up the controller. The proposed control law is validated by experiments involving a 7-dof anthropomorphic robotic arm executing a trajectory tracking task in a restricted environment. As experimental results show, it is possible to control position and force by adjusting controller weights.