Hybrid position/force control of constrained robot manipulator based on a feedforward neural network

In this paper, the control of the constrained robotic manipulators is addressed and the solution of a reduced order model is obtained through a nonlinear transformation. A set of differential-algebraic equations are first derived. Then controllers are designed for position and force control. The position control involves the position and velocity feedback of end-effector, while the force control is developed based on an artificial neural network. The weights of the neural network are updated on-line using the force error as the objective function. An example of a two DOF manipulator system is studied in detail. Comparison between conventional PID controller and the designed controller are made and a practical application is carried out. The results demonstrate remarkable performance of the system.