Abstract
A new method is presented for extending an incomplete mathematical model - in this case a dynamic model of a six degrees of freedom robotic manipulator. A nonlinear multivariate calibration of input-output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t+1) from known input reference up till and including time (t). A new partial least squares regression (PLSR) based method, nominal PLSR with interactions was developed and used to handle, unmodelled nonlinearities. The performance of the new method is compared with least squares (LS). Different cross-validation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.
Highlights
Control of various mechanical systems, such as robotic manipulators, autonomous ground vehicles (AGV), unmanned aerial vehicles (UAV), and surface vehicles (USV), require good model knowledge for precise and efficient control
We focus on investigating the properties of the error signal generated by the internal controller of an industrial robotic manipulator and a model of the same system based on Euler–Lagrange formulation combined with dynamic parameter identification procedure
The comparison between using no compensation method, standard partial least squares regression (PLSR) and modified PLSR as suggested later in this paper is shown in Table 1 and confirms that the overall improvements are insignificant and it is clear that one has to look further than a standard method
Summary
Control of various mechanical systems, such as robotic manipulators, autonomous ground vehicles (AGV), unmanned aerial vehicles (UAV), and surface vehicles (USV), require good model knowledge for precise and efficient control. In the case of a standard industrial 6-DOF manipulator such as ABB IBR140 or KUKA KR150, non-linearities come from multiple sources, some are taken into consideration during model development stages following Lagrangian formulation (Spong et al, 2006; Siciliano et al, 2009). The dynamic model defines the relationship between joint position qi, angular velocity qi, and angular accelerationqi to torque τ i necessary to achieve desired position, velocity, and acceleration
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