Dynamic parameter identification of upper-limb rehabilitation robot system based on variable parameter particle swarm optimisation

Abstract

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots, a dynamic parameter identification method based on variable parameters particle swarm optimisation (PSO) is developed. Based on the dynamic model of the system, the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations. It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm, which greatly improves its identification accuracy. Finally, through the comparison and analysis of the simulation results, compared with those of the least square (LS) and unmodified PSO identification algorithms, a great improvement in the identification accuracy of the algorithm is achieved. The control effect in the actual control system is also much better than those of the LS and PSO algorithms.

Keywords

• parameter estimation
• medical robotics
• patient rehabilitation
• particle swarm optimisation
• upper-limb rehabilitation robot system
• variable parameter particle swarm optimisation
• uncertain parameters
• dynamic modelling
• upper-limb rehabilitation robots
• dynamic parameter identification method
• variable parameters particle swarm optimisation
• dynamic model
• algorithm changes
• inertia parameter
• learning law
• basic pso algorithm
• fixed-parameter
• identification accuracy