Design, Simulation, Comparison and Evaluation of Parameter Identification Methods for an Industrial Robot

This study discusses the design and assessment of different parameter identification methods applied to robot systems, such as least squares, extended Kalman filter, Adaptive Linear Neuron (Adaline) neural networks, Hopfield recurrent neural networks and genetic algorithms. First, the characteristics of the methods above mentioned are described. Second, using the software MatLab/Simulink, a simulation of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3 Degrees of Freedom (DOF) is carried out by applying these parameter identification methods, thereby obtaining the performance indicators of the algorithms that allow for parameter identification. Therefore, this study enables the adequate selection of identification methods to obtain parameters that characterize the dynamics of industrial robots, particularly of the SCARA type. Hence, having the values of the base parameters of a robot contributes to the design of new control methods, since the robot characteristic dynamic model is known. © 2016 Elsevier Ltd