Neural network designs with genetic learning for control of a single link flexible manipulator

Sandeep Jain, Pei Yuan Peng, Anthony Tzes, Farshad Khorrami

Research output: Contribution to journalConference articlepeer-review


The application of neural networks for active control of lightly damped systems is considered in this paper. The training process of the neural-network controller is based on the genetic learning algorithm. The scheme imitates nature's cleansing phenomena of natural selection and survival of the fittest to generate individual controllers with the best fitness values. It essentially incorporates an exhaustive search in the weight-space governed by the rituals of crossover and mutation to seek the optimum neural-network weights to satisfy certain performance criteria. Several appropriate modifications of the classical genetic algorithm for neural-network control purposes are discussed. The genetic-trained neural-network controller is applied for tip position tracking and vibration suppression of a single-link flexible arm. Simulation studies are presented to validate the effectiveness of the advocated algorithms.

Original languageEnglish (US)
Pages (from-to)2570-2574
Number of pages5
JournalProceedings of the American Control Conference
StatePublished - 1994
EventProceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA
Duration: Jun 29 1994Jul 1 1994

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'Neural network designs with genetic learning for control of a single link flexible manipulator'. Together they form a unique fingerprint.

Cite this