ADAPTIVE CONTROL ALGORITHMS FOR LOAD TORQUE CHANGES IN ELECTRIC DRIVES

Abstract

This thesis is devoted to the study of adaptive control algorithms for load torque changes in electric drives. The work shows that load torque changes affect rotor speed, energy consumption, and wear of mechanical parts, and traditional control systems cannot effectively respond to these changes. The thesis analyzes adaptive control, model-based predictive control, fuzzy logic, and Model Predictive Control (MPC) algorithms. The results show that adaptive control systems reduce rotor speed deviation by 20–40%, accelerate torque generation time by 2–5 times, and optimize energy consumption. This approach increases the efficiency of electric drives, reduces wear of mechanical parts, and ensures reliability in industrial systems.