ANN-Based Controllers for Improved Performance of BLDC Motor Drives

Shanmugasundaram, R.; Ganesh, C.; Singaravelan, A.

doi:10.1007/978-981-15-5262-5_6

Cited by 12 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Saini et al (2022) suggested an enhanced PID controller tuning for controlling the celerity of the DC motor through a hybrid stochastic fractal search algorithm. Shanmugasundaram et al (2012, 2020, 2021) have employed a fuzzy, neural network and adaptive neuro-fuzzy inference system for speed control of a second-order BLDC drive. The findings of these research works had drawbacks such as the interference of human knowledge and skill, hardware dependence, complexity, processing time, approximate results and data dependency.…”

Section: Introductionmentioning

confidence: 99%

An effective controller design strategy for higher order systems

Chandramouleeswaran,

Deivanayagam Pillai,

Ramasamy

et al. 2023

Journal of Vibration and Control

View full text Add to dashboard Cite

The recent methods that guarantee the stability of the reduced-order models are Mihailov stability, improved Pade approximation and truncation, improved generalized pole clustering, moment matching and salp swarm optimization. Further, these methods could overcome the limitations such as non-uniqueness, pole clustering, gain adjustment and difficulty to maintain the dominant roots in the lower order system for non-minimum higher order plants. This research emphasizes the design of the proposed compensating algorithm by using an additional open loop zero for the stable reduced-order models of large-scale single-input single-output linear time-invariant continuous time systems. The results of the proposed algorithm are compared with the existing compensating methods and the design is validated and illustrated numerically.

show abstract