Development of neurocontrollers for spacecraft navigation

Neural network and fuzzy logic technologies are being utilized in the development of intelligent control ulternatives for improving the performance, flexibility, und reliability of automated rendezvous and docking system. Methods which rely on a mathematical model of the complex spucecraji system are neither accurate nor computationally efficient. Unmodeled variations in the spacecraft mass can result in target overshooting or undershooting during automated docking operations. Similarly, unmodeled shifts in the location of the center of mass can lead to spacecraft oscillation. Furthermore, unmodeled dynamics such as sloshing propellant and faulty thruster operation can have a major impact on spacecraft controllability. Neural network-based controllers are viable alternatives to model-based controllers for the reason that an accurate model can be determined empirically. Neural network solutions are based on the responses of u real system and not a muthematical model. In this paper, CMAC neural networks are used in the supervised learning mode to approximate control laws for automated docking of an unknown spacecraft system during the terminal phuse of spaceflight. Preliminary computer simulation results show that CMAC controllers can be trained in about a second to learn the control laws for automatic docking. The results show that automated docking can be performed accurately and eficiently, and that "soji" docking can be achieved. More importantly, the risks ussociared with "hard" docking can be significantly reduced. A hardware testbed is being developed for conducting proof-of-concept testing and for evaluating the effectiveness and robustness of various neurocontroller approaches. The hardware restbed is a scaled version of a spacecraft designed to float above the surface of a large granite table using high precision air bearings. Motion in the horizontal plane is nearly friction-free, thus providing a simulated Og environment along the plane. Neural network and fuzzy logic technologies are being utilized in the development of intelligent control alternatives for improving the performance, flexibility, and reliability of automated rendezvous and docking systems. Methods which rely on a mathematical model of the complex spacecraft system are neither accurate nor computationally efficient. It is very difficult to accurately model real systems, particularly when they are highly nonlinear or vary over time. In some situations, the structure of the spacecraft system may be unknown. The performance and stability of a model-based controller can be dramatically affected by events such as the expenditure of propellant, redistribution of internal mass, and reconfiguration due to loading and unloading of cargo payloads. Unmodeled variations in the spacecraft mass can result in target overshooting or undershooting during docking maneuvers. Similarly, unmodeled shifts in the location of the center of mass can lead to spacecraft oscillation. Furthermore, unmodeled dynamics such as sloshing propellant and faulty thruster operation can have a major impact on spacecraft controllability. Adaptive controllers based on analytical methods have several major shortcomings. They require that the form of the system equations be known and that the parameters of the system equations be estimated. Moreover, the parameters are updated slowly due to the iterative nature of the adaptive algorithms. Neural network-based controllers are viable alternatives to model-based controllers for the reason that an accurate model can be determined empirically. Neural networks can learn nonlinear relationships from experience, synthesize its own rules, and adapt to changing conditions. The neural network solutions are based on the responses of a real system and not a mathematical model. Neural networks are useful in control applications because they can approximate any wellbehaved function to an arbitrary degree of accuracy. They can even approximate ill-behaved functions required in some control applications (Werbos). Another beneficial feature of neural networks is that they inherently provide noise filtering, which would be useful for suppressing structural resonance and sensor noise that cause unnecessary conml actions. "Copyright