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Model-based update in task-level feedforward control using on-line approximation
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DOI：10.1016/S0005-1098(00)00178-3.png)
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Abstract
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This paper proposes and studies an algorithm for task-level control based on a radial. basis function network approximation of the optimal task input vector on parameters of the task. A learning update scheme is proposed for on-line compensation for the inaccuracy of the model used in the controller design. The update approximates the Jacobian of the task input-output mapping using an off-line design model. Deadzone convergence of this learning scheme in the presence of modeling errors is proved and constructive estimates of the convergence robustness parameters are obtained. An application of the proposed algorithm to Feedforward vibration compensation for flexible spacecraft slewing complements the theoretical analysis. Simulations demonstrate practically acceptable performance of the algorithms in this difficult problem. (C) 2001 Elsevier Science Ltd. All rights reserved.
Keywords:
feedforward control
learning algorithm
convergence
neural network approximation
flexible spacecraft
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