Introduces CLBC strategy with high-order tuners and composite learning for exponential stability and parameter convergence under IE or partial IE, using extra prediction error for transients.
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Standard INDI plus incremental control allocation is invalid for nonaffine input systems due to untenable linear approximations, and a learning-based nonlinear allocation method provides a fast, effective alternative.
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Composite learning control with modular backstepping and high-order tuners
Introduces CLBC strategy with high-order tuners and composite learning for exponential stability and parameter convergence under IE or partial IE, using extra prediction error for transients.
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Improving INDI for Input Nonaffine Systems via Learning-Based Nonlinear Control Allocation
Standard INDI plus incremental control allocation is invalid for nonaffine input systems due to untenable linear approximations, and a learning-based nonlinear allocation method provides a fast, effective alternative.