Enhancing Control Systems with Neural Network-Based Intelligent Controllers

Kevin Puentes; Luis Morales; David F. Pozo-Espin; Viviana Moya

doi:10.28991/ESJ-2024-08-04-01

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Kevin Puentes Departamento de Automatización Y Control Industrial, Escuela Politécnica Nacional, Quito,, Ecuador
Luis Morales Departamento de Automatización Y Control Industrial, Escuela Politécnica Nacional, Quito,, Ecuador
David F. Pozo-Espin
david.pozo@udla.edu.ec
Facultad de Ingeniería y Ciencias Aplicadas, Ingeniería en Electrónica y Automatización, Universidad de Las Américas, Quito,, Ecuador https://orcid.org/0000-0002-7436-3838
Viviana Moya Facultad de Ciencias Técnicas, Universidad Internacional Del Ecuador UIDE, Quito 170411,, Ecuador

Vol. 8 No. 4 (2024): August

Research Articles

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The primary challenge faced by a neural controller in the dynamic model of a mobile robot lies in its ability to address the inherent complexity of the system dynamics. Given that mobile robots exhibit nonlinear movements and are subject to diverse environmental conditions, they contend with a challenging dynamic environment. The neural controllers must demonstrate the capability to continuously adapt and effectively learn to manage the variability present in the dynamic of the robot. This paper presents two intelligent controllers utilizing neural networks, showcasing their relevance in the field of robotics. The first controller, referred to as the neural PID (PIDN), integrates the traditional PID controller with a neural component. The second controller leverages the dynamic model of a differential robot to improve trajectory tracking, employing a parallel architecture that combines PID with neural networks (PID+NN). Our proposals adhere to a cascading structure, where the outer loop takes the lead in reducing position errors through a kinematic controller, while concurrently, the inner loop is employed to regulate linear and angular velocities through the proposed controllers. The controllers are validated in the CoppeliaSIM simulator, offering a realistic setting for evaluating the behavior of the chosen Pioneer 3-DX robot. To comprehensively assess controller performance, three strategies are examined: PIDN, PID+NN, and the conventional PID. Through a blend of qualitative and quantitative analyses, employing diverse performance metrics, the advantages of our proposed controllers become apparent.

Doi: 10.28991/ESJ-2024-08-04-01

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Puentes, K., Morales, L., Pozo-Espin, D. F., & Moya, V. (2024). Enhancing Control Systems with Neural Network-Based Intelligent Controllers. Emerging Science Journal, 8(4), 1243–1261. https://doi.org/10.28991/ESJ-2024-08-04-01

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Enhancing Control Systems with Neural Network-Based Intelligent Controllers

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