B-Spline Artificial Neural Networks in Robust Induction Motor Control

• Published in: IEEE access 2024, Vol.12, p.101679-101700
• Main Authors: Beltran-Carbajal, Francisco, Yanez-Badillo, Hugo, Galvan-Perez, Daniel, Rivas-Cambero, Ivan, Sotelo, David, Sotelo, Carlos
• Format: Article
• Language: English
• Subjects: Adaptive control; Adaptive systems; Artificial intelligence; Artificial neural networks; Asynchronous motors; Biological neural networks; Control methods; Control systems design; Electric motors; Horsepower; Induction motors; Intelligent control; machine control systems; Network control; Neural networks; Nonlinear control; Nonlinear systems; Performance evaluation; Real-time systems; Robust control; Sliding mode control; Splines (mathematics); Tracking control; Trajectory control; Trajectory optimization; Uncertainty; Vibrations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3430323

Artificial intelligence stands for an excellent alternative to be considered in development of new adaptive high-efficiency control design methodologies for uncertain modern complex engineering systems driven by electric motors. In this sense, artificial neural networks can be embedded within innovative nonlinear control design strategies to add capabilities to neutralize various kinds of dynamic perturbations or uncertainties that can significantly deteriorate the operating efficiency of nonlinear electric motor systems. This paper introduces a novel adaptive sliding mode nonlinear control method based on B-spline artificial neural networks for efficient tracking of optimal smooth operating reference trajectories in induction or asynchronous motors under substantially disturbed operational scenarios. Robustness regarding nonlinear theoretical mathematical modelling errors, parametric uncertainty and unknown external multiple-frequency oscillating disturbing influences is considered. Numerical experiments involving multiple disturbed operating case studies are presented to demonstrate the effective performance of the proposed robust adaptive artificial neural-network control scheme on large horsepower three-phase induction motors. Finally, a comparative evaluation is conducted, emphasizing system performance through the use of index performance criteria, and comparing the proposed adaptive robust B-spline approach with a nonlinear passivity-based controller. New insights to extend the introduced adaptive neural network robust control design strategy to other considerably perturbed practical nonlinear engineering systems are thus provided.