Learning feedforward control based on the available dynamic/kinematic system model and sensor information is generally effective for reducing the tracking error for a learned trajectory. For new trajectories, however, the system cannot benefit from previous learning data and it has to go through the learning process again to regain its performance. In industrial applications, this means production line has to stop for learning, and the overall productivity of the process is compromised. To solve this problem, this paper proposes a feedforward input generation scheme based on neural network (NN) prediction. Learning/training is performed for the NNs for a set of trajectories in advance. Then the feedforward torque input for any trajectory in the predefined workspace can be calculated according to the predicted error from multiple NNs managed with expert logic. Experimental study on a 6-DOF industrial robot has shown the superior performance of the proposed NN based feedforward control scheme in the position tracking as well as the residual vibration reduction, without any further learning or end-effector sensors during operation.
Feedforward Input Generation Based on Neural Network Prediction in Multi-Joint Robots1
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 11, 2012; final manuscript received November 2, 2013; published online January 20, 2014. Assoc. Editor: Won-jong Kim.
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Asensio, J., Chen, W., and Tomizuka, M. (January 20, 2014). "Feedforward Input Generation Based on Neural Network Prediction in Multi-Joint Robots." ASME. J. Dyn. Sys., Meas., Control. May 2014; 136(3): 031002. https://doi.org/10.1115/1.4025986
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