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Creators/Authors contains: "Lim, Chuan Yuan"

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  1. ; ; (, IEEE)
    This paper presents two machine learning-based constraint management approaches based on Reference Governors (RGs). The first approach, termed NN-DTC, uses regression neural networks to approximate the distance to constraints. The second, termed NN-NL-RG, uses regression neural networks to approximate the input-output map of a nonlinear RG. Both approaches are shown to enforce constraints for a nonlinear second order system. NN-NL-RG requires a smaller dataset size as compared to NN-DTC for well-trained neural networks. For systems with multiple constraints, NN-NL-RG is also more computationally efficient than NN-DTC. Finally, promising results are reported by having both approaches implemented on a more complex spacecraft proximity maneuvering and docking application, through simulations. 
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