AUTONOMOUS NEURAL CONTROL IN SPACE STRUCTURAL PLATFORMS

The design of control algorithms for space structural platforms, possessing nonlinear dynamics which are often time-varying and ill-modeled, presents great challenges for all current methodologies. 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 control. A fault diagnosis network is applied for health monitoring to provide the neural controller with various failure scenarios. Associative memory is incorporated to compensate catastrophic changes of structural parameters by providing a continuous solution space of acceptable controller configurations.