ISSN:
1573-0409
Keywords:
Adaptive control
;
learning and adaptation
;
neural networks
;
inverse-dynamics
;
and parameter uncertainty
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract As is known, many of the attributes of intelligent control in a biological process are due to the interactions of billions of neurons. Changing the weights of neurons alter the behavior of the entire neural network. Learning in a neutral network is accomplished by adjusting the weights, typically to minimize some objective function, and storing these weights as the actual strengths of the interconnections. The authors believe, therefore, that a control technique designed on the principles of neural networks will exhibit a ‘learn-while-performing’ capability. In this paper such a neuro-controller, called the ‘Inverse-Dynamics Adaptive Control (IDAC)’, for a class of unknown linear plants with structural perturbations is presented. Algorithms necessary to implement the IDAC technique are derived in detail. Simulation results show that the IDAC scheme exhibits dynamic learning and adaptation capabilities in the control of unknown complex systems. Noa-priori knowledge of the process to be controlled is necessary for the implementation of this scheme. Furthermore, the plant parameter variations due to the structural or environmental perturbations may be investigated by studying the IDAC parameter trajectories.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01257817
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