Position/force control optimized by particle swarm intelligence for constrained robotic manipulators

In this paper, position/force stability conditions for constrained robotic manipulators are proposed using a Lyapunov approach. The stiffness control concept is applied to design the force model whereas the controller parameters are optimized using Particle Swarm intelligence. The Mean of Root Squared Error (MRSE) is considered as a fitness function in the task space to design the Particle Swarm Optimization (PSO) algorithm. Simulation results prove the stability and the performances of the proposed approach on a 3DOF robotic arm constrained to a circular trajectory.