SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
Adaptive estimation and control with online data memory: A historical perspective
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Adaptive Control with Sparse Identification of Nonlinear Dynamics
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
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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.