Effective Speed Control of DC Compound Motor Using Artificial Neural Network-Based Virtual Inertia Injection

Vardhan, D. Sree Vishnu; Phanindra, D. Raghava Sai; Kumar, Y. V. Pavan

doi:10.1007/978-981-16-2709-5_40

Cited by 2 publications

(1 citation statement)

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“…However, its performance can be hindered by the need for manual adjustment of parameters for each specific application. Alternatively, artificial intelligence techniques, particularly reinforcement learning algorithms, provide the option to automatically and continuously adjust controllers based on real-time feedback obtained from the system [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor

Alejandro-Sanjines,

Maisincho-Jivaja,

Asanza

et al. 2023

Biomimetics

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Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.

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Section: Introductionmentioning

confidence: 99%