Energy consumption optimization in adaptive neural control of complex systems based on Lyapunov stability analysis

Efficient energy management has become a critical challenge, driving technological advancements aimed at reducing costs and minimizing energy waste. While existing approaches focus on energy resource management, this paper introduces a novel indirect adaptive neuro-control strategy designed for complex dynamic systems. Unlike conventional methods, our approach integrates a recurrent neural network-based emulator and a controller with independently adjustable adaptive rates, providing enhanced adaptability and robustness. A key innovation is the development of a Lyapunov stability (LS)-based online training algorithm, ensuring real-time adjustment of network weights while optimizing a multi-objective criterion that balances control energy minimization and closed-loop stability preservation. To demonstrate the superiority of the proposed strategy, we conduct comprehensive comparative studies and experimental validation on a semi-batch reactor used in cleaner biofuel production, showcasing its practical effectiveness over traditional techniques.