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Time Dependent Inverse Optimal Control using Trigonometric Basis Functions
The choice of objective is critical for the performance of an optimal controller.
Stochastic MPC with robustness to bounded parametric uncertainty
assumption on the noise distribution, we also provide an average asymptotic performance bound for the l2-norm of the closed-loop state.
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus control performance.
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Second, we propose a linearization-based technique that directly provides approximations of the trajectory distribution, taking correlations explicitly into account.
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