Neural network-based optimal control of a DC motor positioning system

Khomenko, Maksym; Voytenko, Volodymyr; Vagapov, Yuriy

doi:10.1504/ijaac.2013.055097

Cited by 12 publications

(9 citation statements)

References 6 publications

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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%

“…Khomenko et. al, [9] sought optimal control of motor positioning using a combination of artificial neural network and state space method with variable gain yielding a transient process close to optimal without overshoot. The approach proposed in this manuscript has similar efficacies.…”

Section: (A) (B)mentioning

confidence: 99%

“…The code used to generate data and figures in this manuscript is adapted from the Appendix of Rohan Shah [8], Koo Mo Koo, and Henry Travis's article [9] Conflicts of Interest: The authors declare no conflict of interest.…”

Section: Acknowledgmentsmentioning

confidence: 99%

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Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

Sands¹

2023

Preprint

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In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence asserts physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory generation algorithms: sinusoidal function and Pontryagin's based generation algorithm. This thesis aims to simulate model-following and deterministic artificial intelligence methods using sinusoidal and Pontryagin's methods and compare their performance difference when following challenging step function slew maneuver.

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Section: Discrete Deterministic Artificial Intelligence With Differen...supporting

confidence: 81%

Section: (A) (B)mentioning

confidence: 99%

Section: (A) (B)mentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

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Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

Sands¹

2023

Preprint

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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%

An ANN-Based Temperature Controller for a Plastic Injection Moulding System

et al. 2019

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This paper proposes an approach to an ANN-based temperature controller design for a plastic injection moulding system. This design approach is applied to the development of a controller based on a combination of a classical ANN and integrator. The controller provides a fast temperature response and zero steady-state error for three typical heaters (bar, nozzle, and cartridge) for a plastic moulding system. The simulation results in Matlab Simulink software and in comparison to an industrial PID regulator have shown the advantages of the controller, such as significantly less overshoot and faster transient (compared to PID with autotuning) for all examined heaters. In order to verify the proposed approach, the designed ANN controller was implemented and tested using an experimental setup based on an STM32 board.

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