SIAM Journal on Matrix Analysis and Applications
Exciting trajectories for the identification of base inertial parameters of robots
International Journal of Robotics Research
Modelling and Identification in Robotics
Modelling and Identification in Robotics
Prediction of geometric errors of robot manipulators with Particle Swarm Optimisation method
Robotics and Autonomous Systems
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control
Expert Systems with Applications: An International Journal
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
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This paper deals with the dynamic modeling and identification of Staubli RX-60 robot. In the robot identification, a least squares (LS) method and particle swarm optimization (PSO) technique were used to estimate the distinct inertia parameters of Staubli RX-60 robot. Several experiments were conducted to have the physical robot data. In identification experiments, the position, velocity, acceleration and torques of the robot joints were measured from the motor encoders, motion analysis system with three cameras and the load-cell sensor. Using experimental data, the inertial parameters of the robot were successfully estimated. The parameters estimated from these methods were verified with experimental results. These experimental results show that the estimated inertial parameters predict robot dynamics well. Moreover, the identification errors for both PSO based identification technique and LS method were computed and were summarized in a table. According to the identification errors, the performance of PSO on the parameter estimation is better than the performance of LS method.