Research on Adaptive Impedance Control of Robot Manipulator Based on a New Improved Particle Swarm Optimization Algorithm

To prevent the robot from being damaged and protect human life during its movement, it is of great importance to accurately track the contact force. Besides, to eliminate the steady-state error of position-based impedance control, this paper proposes an adaptive parameter regulation law by using Lyapunov second method to online estimate the environmental position and stiffness. To further improve the transient response performance of force tracking, a new particle swarm optimization (PSO) algorithm is proposed in this paper. Based on the force tracking integral time-weighted absolute error (ITAE) evaluation function, the energy consumption of the manipulator system and the maximum transient contact force are considered. In addition, the parameters of the impedance controller are optimized by designing the inertia weight and learning factors of nonlinear real-time update and considering the local variation method of particles. The simulation results show that this new improved PSO algorithm can help particles jump out of the local optimum and find impedance parameters with better fitness value, thus significantly improving the force dynamic tracking accuracy and overshoot which will make human-machine collaboration more convenient.