Quantum Particle Swarm Optimization (QPSO)-Based Enhanced Dynamic Model Parameters Identification for an Industrial Robotic Arm

Accurate parameter identification in dynamic models of robotic arms is essential for performing high-performance control and energy-efficient procedures. However, classic methods often encounter difficulties when modeling nonlinear, high-dimensional systems, particularly in the presence of real-world uncertainties. To address these challenges, this study focuses on identifying mass center positions and inertia matrix elements in a six-jointed industrial robotic arm and comparing the influence of optimized algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum-behaved Particle Swarm Optimization (QPSO). The robot’s kinematic model was validated by comparing it with actual motion data, utilizing a high-precision neural network to ensure accuracy before conducting a dynamic analysis. A comprehensive dynamic model was created using Computer-Aided Optimization (CAO) in SolidWorks Premium 2023 to simulate realistic mass parameters, thereby validating the model’s reliability in a practical setting. The real (Referenced) and optimized dynamic models of the robot arm were validated using trajectory tracking simulations under sliding mode control (SMC) to assess the impact of the optimized model on the robot’s performance metrics. Results indicate that QPSO estimates inertia and mass center parameters with Mean Absolute Percentage Errors (MAPE) of 0.76% and 0.43%, outperforming PSO significantly and delivering smoother torque profiles and greater resilience to external disturbances.