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Found 11 papers, 0 papers with code
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin
This article presents the guided Bayesian optimization algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using an event-triggered digital twin of the system based on available closed-loop data.
Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks
The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article.
Diagonally Square Root Integrable Kernels in System Identification
For the stability of a Gaussian process centered at a stable impulse response, we show that the necessary and sufficient condition is the diagonally square root integrability of the corresponding kernel.
Representer Theorem for Learning Koopman Operators
The learning problem is formulated as a constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators.
The Existence and Uniqueness of Solutions for Kernel-Based System Identification
The consequent estimation problem is well-defined under the central assumption that the convolution operators restricted to the RKHS are continuous linear functionals.
Kernel-based Impulse Response Identification with Side-Information on Steady-State Gain
The objective function of this optimization problem is the empirical loss regularized with the norm of RKHS, and the constraint is considered for enforcing the integration of the steady-state gain side-information.
Regularized Identification with Internal Positivity Side-Information
Utilizing these conditions, the impulse response estimation problem is formulated as a constrained optimization in a reproducing kernel Hilbert space equipped with a stable kernel, and suitable constraints are imposed to encode the internal positivity side-information.
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization
Accurate positioning and fast traversal times determine the productivity in machining applications.
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach
This is beneficial since measurements of the cable net form on the construction site are very expensive.
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.
Structured exploration in the finite horizon linear quadratic dual control problem
This paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty.
