In this paper, we propose a new feedback-error-learning controller enhanced by the PaLM tree that is an easy-to-use function approximator developed by our research group. We investigate the ability of our feedback-error-learning controller by applying it to controlling an active camera head to pursuit a moving target with high accuracy and high response. The PaLM-tree learns the inverse model in the feedback-error-learning scheme correctly. Although our active camera head has unknown mechanical friction and our closed-loop control system has a relatively large latency, our active camera head can successfully pursuit eye movement by our feedback-error-learning controller based on the PaLM-tree. Through experiments, we confirmed that our method could achieve high-performance control over the tuned feedback control.
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