ADAPTIVE NEURO-CONTROL FOR SPACECRAFT ATTITUDE-CONTROL

Spacecraft attitude control is approached as a nonlinear adaptive control problem and neuro-control, which combines concepts from artificial neural networks and adaptive control, is investigated as an alternative to linear control approaches. Three capabilities of neuro-controllers are demonstrated using a nonlinear model of the Space Station Freedom. These capabilities are: (a) synthesis of robust nonlinear controllers using neural networks; (b) copying an existing control law using neural networks; and (c) adaptively modifying neuro-controller characteristics for varying inertia characteristics. The main components of the adaptive neuro-controllers are an identification network and a controller network. Both these networks are trained using the back-propagation of error learning paradigm, To ensure robustness of the neuro-controller, optimally connected neural networks are synthesized for the identification and the controller networks. For the on-line adaptive control problem, a backpropagation of error technique using a linear adaptive critic is introduced in place of the backpropagation through time technique. Performances of the nonlinear neuro-controllers for the three cases listed above are verified using a nonlinear simulation of the Space Station. Results presented substantiate the feasibility of using neural networks in robust nonlinear adaptive control of spacecraft.