Finite-dimensional RKHS approximation via n-widths enables scenario optimization to deliver violation guarantees on nonlinear one-step predictors without a priori bounds on the true RKHS norm or Lipschitz constant.
Gaussian processes for dynamics learning in model predictive control
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A robust adaptive MPC framework for nonlinear systems with bounded disturbances uses Gaussian process models and contraction metrics to guarantee recursive feasibility, robust constraint satisfaction, and convergence with high probability.
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Robust Nonlinear System Identification in Reproducing Kernel Hilbert Spaces via Scenario Optimization
Finite-dimensional RKHS approximation via n-widths enables scenario optimization to deliver violation guarantees on nonlinear one-step predictors without a priori bounds on the true RKHS norm or Lipschitz constant.
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A robust and adaptive MPC formulation for Gaussian process models
A robust adaptive MPC framework for nonlinear systems with bounded disturbances uses Gaussian process models and contraction metrics to guarantee recursive feasibility, robust constraint satisfaction, and convergence with high probability.