The rapid urbanization of modern society presents unprecedented challenges, including escalating resource consumption, traffic congestion, and environmental degradation. The sustainable smart city paradigm, powered by the Internet of Things (IoT) and big data, offers a promising solution, but its dynamic and interconnected nature demands advanced control systems. Traditional control methodologies, designed for singular and static systems, are inadequate for managing the complex, multi-domain interactions inherent in urban environments. This paper proposes a novel Hybrid Intelligent Control Framework that addresses this limitation by synergistically integrating multiple AI techniques. Our framework combines the robust, rule-based reasoning of fuzzy logic with the adaptive, predictive capabilities of artificial neural networks. The proposed system is designed to operate across multiple urban sectors, including intelligent transportation, smart energy grids, and dynamic waste management, facilitating holistic and optimized urban governance. Through a simulated case study, we demonstrate that this hybrid approach significantly outperforms traditional single-technique control systems in key sustainability metrics, such as energy efficiency, traffic flow optimization, and carbon emission reduction. The results validate the framework's ability to handle the complexity and uncertainty of urban systems, paving the way for more resilient, efficient, and sustainable cities.
Subrahmanyam et al. (Mon,) studied this question.