Hybrid learning control in flexible space structures with reconfiguration capability

The design of control algorithms for flexible space structures, possessing nonlinear dynamics which are often time-varying and likely ill-modeled, presents great challenges for all conventional methodologies. In the present paper, we propose the use of a hybrid connectionist system as a learning controller with reconfiguration capability. The ability of connectionist systems to approximate arbitrary continuous functions provides an efficient means of vibration suppression and trajectory maneuvering for precision pointing of flexible structures. Embedded with adjustable time-delays and interconnection weights, adaptive time-delay radial basis function network offers an effective modeling technique to capture all of the spatiotemporal interactions among the structure members. A fuzzy-based fault diagnosis system is applied for health monitoring to provide the neural controller with various failure scenarios. Associative memory is incorporated into an adaptive architecture to compensate catastrophic changes of structural parameters by providing a continuous solution space of acceptable controller configurations. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via specific examples