Reconfigurable learning control in large space structures

The design of control algorithms for large space structures, possessing nonlinear dynamics which are often time-varying and likely ill-modeled, presents great challenges for all current methodologies. These limitations have led to the pursuit of autonomous control systems. In the present paper, the author proposes 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 flexible structures. A fault-diagnosis network is applied for health monitoring to provide the neural controller with various failure scenarios. Associative memory is incorporated into an adaptive architecture to compensate slowly varying as well as catastrophic changes of structural parameters by providing a continuous solution space of acceptable controller configurations, which is created a priori. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via specific examples