Innovative Control Systems for Enhancing the Efficiency and Resilience of Smart Cities

Abstract

The fast-growing rate of expansion of the urban landscape has posed challenges for modern-day smart cities' infrastructures, which require not only efficiency but also resilience against any unexpected disruptions. Conventional control system models simply do not cope well with the complex dynamics of ever-evolving cities, leading to problems like traffic jams, energy waste, and improper distribution of resources. To tackle this issue, the present paper offers a new, preemptive solution based on a two-tiered Hybrid Control System that combines Model Predictive Control (MPC) and Reinforcement Learning (RL). This approach leverages data analytics to optimize the performance of coupled urban systems. The efficiency of the proposed system was thoroughly tested using Matlab/Simulink simulations of traffic networks and energy microgrids under extreme boundary conditions for the infrastructure. Empirical evidence has shown that the hybrid control strategy significantly outperforms conventional baseline systems. Certainly, the design has yielded an average energy efficiency improvement of 22% (which translates into a net gain of 14%) while cutting average driving time by 33.3%. In addition, runtime robustness was boosted through acceleration of recovery kinetics by 60%, translating into a system downtime decrease from 300 seconds to 120 seconds. These statistics clearly demonstrate the effectiveness of this control mechanism in addressing the performance trade-offs across domains under pressure conditions. All in all, this research project offers a scalable and generalizable model of urban management control that serves as a blueprint for achieving sustainability in terms of carbon reduction during traffic jams and protection of critical systems during electricity supply failures, marking a vital milestone toward developing the truly smart and resilient urban centers of the future.