AI Recommenders for Adaptive Control Strategy Selection in Smart Electrical Drives

Smart electrical drives are increasingly important in modern industrial applications, where operational conditions can change fast. Traditional control strategies often rely on current methods that struggle to maintain optimal performance across several scenarios. This paper proposes a conceptual framework that leverages artificial intelligence-based recommender systems for adaptive control strategy selection in smart electrical drives. The proposed AI recommender would analyze real-time sensor data including speed, temperature, load conditions to select the most appropriate control strategy for optimal performance.