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Found 6 papers, 1 papers with code
Innovation-triggered Learning for Data-driven Predictive Control: Deterministic and Stochastic Formulations
Two kinds of data selection mechanisms are proposed by online evaluating the innovation contained in the sampled data, wherein the innovation is quantified by its effect of shrinking the set of potential system dynamics that are compatible with the sampled data.
Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation
Disturbance observers have been attracting continuing research efforts and are widely used in many applications.
Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods
We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations.
Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration
This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression.
Event-triggered Observability: A Set-membership Perspective
This work attempts to discuss the observability of linear time-invariant systems with event-triggered measurements.
Bayesian Optimization Assisted Meal Bolus Decision Based on Gaussian Processes Learning and Risk-Sensitive Control
For the case of announced meals, the proposed method achieves satisfactory and similar performance in terms of mean glucose and percentage time in [70, 180] mg/dL without increasing the risk of hypoglycemia.
