2013
DOI: 10.1504/ijaac.2013.055097
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Neural network-based optimal control of a DC motor positioning system

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Cited by 12 publications
(9 citation statements)
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“…However, upon decreasing the time step to 0.35, the deterministic artificial intelligence approach outperforms the recursive least square with exponential forgetting method in both mean error and standard deviation, thereby validating the effectiveness of the former approach. It is worth noting that the output of deterministic artificial intelligence exhibits an overshoot at discontinuities and tracks the input signal with a small tracking error, consistent with the observations made by Koo [9]. Moreover, it is worth highlighting that the increase in output observed upon decreasing the step size from 0.4 to 0.35 is not indicative of a decrease in performance but rather reflects the fact that more time steps are required to output larger values.…”
Section: Discrete Deterministic Artificial Intelligence With Differen...supporting
confidence: 81%
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“…However, upon decreasing the time step to 0.35, the deterministic artificial intelligence approach outperforms the recursive least square with exponential forgetting method in both mean error and standard deviation, thereby validating the effectiveness of the former approach. It is worth noting that the output of deterministic artificial intelligence exhibits an overshoot at discontinuities and tracks the input signal with a small tracking error, consistent with the observations made by Koo [9]. Moreover, it is worth highlighting that the increase in output observed upon decreasing the step size from 0.4 to 0.35 is not indicative of a decrease in performance but rather reflects the fact that more time steps are required to output larger values.…”
Section: Discrete Deterministic Artificial Intelligence With Differen...supporting
confidence: 81%
“…Control of DC motors is a classic topic with recent novel methods such as deterministic artificial intelligence (DAI) presented here, neural networks [7][8][9][10][11][12], and reinforcement learning [13] and other instantiations of neural network-based, stochastic artificial intelligence [14][15][16].…”
Section: (A) (B)mentioning
confidence: 99%
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“…It is well-known that a digital control system can provide a fast transient without overshoot in a finite number of control steps equal to the order of the plant. The variable gain method was proposed in [14] to calculate the appropriate control steps, and as was shown in [15], this method is well suited for calculating the reference control steps for the ANN training procedure.…”
Section: Ann Controller Designmentioning
confidence: 99%