A comparative study of Conventional vs AI-based controllers of Electric Motors

Abstract

In this paper, the authors focus on a comparative analysis of conventional and AI-based controllers in the electric motor system. This is chiefly aimed at watching the performance, flexibility, energy profiles, and fault tolerance of the two control strategies in nonlinear and dynamic settings. The secondary research technique was used, with the key data and information used coming from peer-reviewed journal articles, case studies, and technical reports. It was found that the simple and inexpensive controllers like PID and PI may not justify the expectations because they have the propensity to misbehave whenever subjected to variable loads or uncertainties in a system. In comparison, AI-based control systems such as neural networks and hybrid algorithms proved better adaptive, learn in real-time and optimised energy. They were also able to respond to torque as well as being fault-tolerant. Their complicated architecture and the significant computation requirements are. However, a challenge in the low-power systems. The results of the paper assume that an AI-based industrial control can provide tangible benefits in terms of performance but needs to resolve the system integration and scalability problem to put them into wider use.