Application of Salp Swarm Algorithm for DC Motor Parameter Estimation in an Industry 4.0 Control Systems IoT Framework

Common industrial automation applications include food, packaging, logistics systems, tool machines and robots, among others. To achieve higher demands in terms of dynamic behavior and precision, industrial automation heavily relies on industrial AC motor drives, servo motor drives and DC motor drives. A microcontroller used at the center of a topcaliber motor control system, can quickly compute cascaded control tasks, and measure current, position, and speed with ultimate precision. The heavy demand load occurs in real-time, which requires a highly capable system. On the contrary, in such an environment, many factors can affect the corresponding motor control system performance, as the controlled plant (e.g. DC motors) may exhibit operational parameter variations over time. This paper deals with the application of a recently introduced meta-heuristic optimization technique in order to estimate the main motor parameters with accuracy, so as the corresponding control system to present sustainability -in real time- in terms of strict industry 4.0 demands. The results obtained, reveal that the potential use of the algorithm utilized in a IoT framework can enhance the reliability of a modern industrial control system.