<![CDATA[The growing global demand for sustainable energy has elevated the importance of energy-efficient electromechanical systems in applications such as electric vehicles (EVs), renewable energy conversion, and industrial automation. These systems reduce carbon emissions and operational costs by optimizing power use, aligning with UN Sustainable Development Goals and 2050 net-zero targets. However, traditional control methods like field-oriented control (FOC) and PID controllers struggle with nonlinear dynamics, parameter variations, and variable loads, resulting in suboptimal performance, higher energy losses, and reduced robustness. This paper addresses this gap by proposing an AI-assisted framework for designing and controlling smart electromechanical devices, using permanent magnet synchronous motor (PMSM) drives as a prototype. The approach integrates adaptive neural networks with reinforcement learning to enable real-time optimization of dynamic response, robustness, and energy efficiency. The system was rigorously simulated in MATLAB/Simulink using a d-q reference frame model under nominal, disturbed, and variable-load conditions. Results show significant improvements: transient settling time reduced by 42–58%, overshoot by 60–67%, and energy consumption by 12–18%, achieved through minimized torque ripple and losses. The framework also demonstrated superior disturbance rejection and parameter-variation stability. These advances position the proposed solution as a transformative approach for sustainable applications, enhancing efficiency in EV propulsion, renewable energy integration, and industrial automation, while paving the way for future hardware implementation and scalable AI-driven systems.]]>
