Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-72377-6_8
Title: Designing a recurrent neural network-based controller for gyro-mirror line-of-sight stabilization system using an artificial immune algorithm
Authors: Ang, J.H.
Goh, C.K.
Teoh, E.J.
Tan, K.C. 
Issue Date: 2007
Source: Ang, J.H.,Goh, C.K.,Teoh, E.J.,Tan, K.C. (2007). Designing a recurrent neural network-based controller for gyro-mirror line-of-sight stabilization system using an artificial immune algorithm. Studies in Computational Intelligence 66 : 189-209. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-72377-6_8
Abstract: The gyro-mirror line-of-sight stabilization platform used to maintain the line-of-sight of electro-optical sensors mounted on moving vehicles is a multivariate and highly nonlinear system. The system is also characterized by a peculiar phenomenon in which a movement about one axis will trigger off a coupled movement in the other axis. Furthermore, uncertainties such as noise, practical imperfections and additional dynamics are often omitted from the mathematical model of the system thus resulting in a non-trivial control problem. In order to handle the complex dynamics of the gyro-mirror as well as to optimize the various conflicting control objectives, a multi-objective artificial immune system framework which combines the global search ability of evolutionary algorithms and immune learning of artificial immune systems is proposed in this chapter for the design of the gyroscope recurrent neural network controller. In addition, a new selection strategy based on the concepts of clonal selection principle is used to maintain the balance between exploration and exploitation of the objective space. Simulation results demonstrate the effectiveness of the proposed approach in handling noise, plant uncertainties and the coupling effects of the cross-axis interactions. © 2007 Springer-Verlag Berlin Heidelberg.
Source Title: Studies in Computational Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/55593
ISBN: 3540723765
ISSN: 1860949X
DOI: 10.1007/978-3-540-72377-6_8
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