This paper studies neural learning control. Based on an earlier result for deterministic learning of unknown system dynamics from a stable control process, this paper provides detailed analysis on how the learned knowledge can be effectively exploited to achieve stability and improved control performance. Comparisons on neural learning control with adaptive neural control and linear control are also included. The effectiveness of the neural learning control approach is demonstrated using simulations.
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