Identification and control of large structures using neural networks

Abstract Control system design for large space structures, possessing nonlinear dynamics which are often time-varying and likely ill-modeled, presents great challenges for all currently advocated methodologies. The pursuits of an autonomous control system for such nonlinear structures have led to the use of artificial neural networks. In the present paper, we propose the use of radial basis function networks as a learning controller to achieve vibration suppression and trajectory maneuvering. The ability of connectionist systems to approximate arbitrary continuous functions provides an efficient means of modeling, identification and control of complex systems. Based on the model reference adaptive control architecture, a neural controller learns to function as a closed-loop compensator and to force the dynamics of the nonlinear plant to match a given reference model. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via several examples.

[1]  Abhijit S. Pandya,et al.  On-line learning control of autonomous underwater vehicles using feedforward neural networks , 1992 .

[2]  Stuart E. Dreyfus,et al.  Learning input feature selection for sensor fusion in tool wear monitoring , 1992 .

[3]  J. Ramakrishnan,et al.  Modeling, system identification, and control of ASTREX , 1993 .

[4]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[5]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[6]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[7]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[8]  Marco Saerens,et al.  A neural controller , 1989 .

[9]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[10]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[11]  Walter C. Merrill,et al.  Neural network application to aircraft control system design , 1991 .

[12]  Robert E. Uhrig,et al.  Using modular neural networks to monitor accident conditions in nuclear power plants , 1992, Defense, Security, and Sensing.

[13]  R. K. Elsley,et al.  A learning architecture for control based on back-propagation neural networks , 1988, IEEE 1988 International Conference on Neural Networks.

[14]  A. Sideris,et al.  A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[15]  Masahiro Abe,et al.  Incipient fault diagnosis of chemical processes via artificial neural networks , 1989 .

[16]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.