Huang, B.-R.; Sands, T. Novel Learning for Control of Nonlinear Spacecraft Dynamics. Journal of AppliedMath 2023, 1, doi:10.59400/jam.v1i1.42.
Huang, B.-R.; Sands, T. Novel Learning for Control of Nonlinear Spacecraft Dynamics. Journal of AppliedMath 2023, 1, doi:10.59400/jam.v1i1.42.
Huang, B.-R.; Sands, T. Novel Learning for Control of Nonlinear Spacecraft Dynamics. Journal of AppliedMath 2023, 1, doi:10.59400/jam.v1i1.42.
Huang, B.-R.; Sands, T. Novel Learning for Control of Nonlinear Spacecraft Dynamics. Journal of AppliedMath 2023, 1, doi:10.59400/jam.v1i1.42.
Abstract
With correct dynamic system parameters (embodied in self-awareness statements), a controller can provide precise signals for tracking desired state trajectories. If dynamic system parameters are initially guessed incorrectly, a learning method may be used to find the correct parameters. In the deterministic artificial intelligence method, self-awareness statements are formed as mathematical expressions of the governing physics. When the nonlinear, coupled expressions are precisely parameterized as the product of known matrix components and unknown victrix (i.e., a regression form) tracking errors may be projected onto the known matrix to update the unknown victrix in an optimal form (in a two-norm sense). In this work, a modified learning method is proposed and proved to have global convergence of both state error and parameter estimation error. The modified learning method is compared with those in the prequels using simulation experiments of three-dimensional rigid body dynamic rotation motion. The modified approach is two magnitudes better than the methods in the prequels in terms of state error convergence.
Copyright:
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