Parameter Identification of BLDC Motor Model Via Metaheuristic Optimization Techniques

AbstractThe brushless dc (BLDC) motor has been increasingly used in industrial automation, automotive, aerospace, instrumentation and appliances. Analysis and design of the BLDC motor efficiently require its accurate model and parameters. In this paper, the parameter identification of the BLDC motor model via well-known metaheuristic optimization search techniques is proposed. Two trajectory-based methods, i.e. adaptive tabu search (ATS) and intensified current search (ICS) are employed to estimate the BLDC motor parameters. As simulation results of model identification and validation, both ATS and ICS can provide optimal BLDC model parameters. The BLDC models obtained show a very good agreement to actual system dynamics. However, the ICS can pro-vide optimal model parameters faster than the ATS

Multipath Adaptive Tabu Search for a Vehicle Control Problem

Tabu search has become acceptable worldwide as one of the most efficient intelligent searches applied to various real-world problems. There have been different modifications made to the generic tabu search in recent years to achieve better performances. Among those reviewed in the introduction of this paper, the adaptive tabu search (ATS) has incorporated the backtracking and the adaptive search radius mechanisms that help accelerate the search and release it from a local solution lock. The paper explains an enhancement made to the ATS to accomplish multipath ATS (MATS) algorithms. Performances of the ATS and the MATS are evaluated using surface optimization problems, and results are presented in the paper. Finally, the MATS is applied to solve a real-world vehicle control problem