Adaptive time-delay neural control in space structural platforms

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 a robust and fault tolerant structural controller. In the present paper, we propose the use of adaptive time-delay radial basis function (ATDRBF) networks as a learning controller in system identification and dynamic control of flexible structures. The ATDRBF network, which incorporates adaptive time-delays and interconnection weights, provides a feasible and flexible modeling technique to effectively capture all of the spatiotemporal interactions among the structure members. In the spirit of model reference adaptive control, we utilize the ATDRBF network as a building block to allow the neural network to function as an indirect closed-loop controller. The horizon-of-one controller regulates the dynamics of the nonlinear plant to follow a prespecified reference model asymptotically. This paper addresses the theoretical foundation of the architecture and demonstrates its applicability via specific examples