Adaptive neuro-control for spacecraft attitude control

Abstract 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.

[1]  Alexander G. Parlos,et al.  Adaptive attitude control and momentum management for large-angle spacecraft maneuvers , 1992 .

[2]  Alexander G. Parlos,et al.  Parameter Estimation in Space Systems using Recurrent Neural Networks , 1991 .

[3]  Hans Seywald,et al.  Artificial neural networks in Space Station optimal attitude control , 1995 .

[4]  Richard S. Sutton,et al.  Neural networks for control , 1990 .

[5]  Richard S. Sutton,et al.  A Menu of Designs for Reinforcement Learning Over Time , 1995 .

[6]  Kumpati S. Narendra,et al.  Adaptive control using neural networks , 1990 .

[7]  S. Vadali,et al.  Space Station attitude control and momentum management - A nonlinearlook , 1992 .

[8]  Bong Wie,et al.  New approach to attitude/momentum control for the Space Station , 1989 .

[9]  Theodore Bossart,et al.  Invertibility of map, zero dynamics and nonlinear control of Space Station , 1991 .

[10]  Kalmanje KrishnaKumar.  Optimization of the neural net connectivity pattern using a backpropagation algorithm , 1993, Neurocomputing.

[11]  Tarunraj Singh,et al.  Attitude control of spacecraft using neural networks , 1993 .

[12]  S Z Qin,et al.  Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.

[13]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  B. Wie,et al.  Robust H(infinity) control design for the Space Station with structured parameter uncertainty , 1990 .