Kernel-based identification with frequency domain side-information

This paper discusses the problem of system identification when frequency domain side-information is available. We mainly consider the case where the side-information is provided as the H_∞-norm of the system being bounded by a given scalar. This framework allows considering different forms of frequency domain side-information, such...

The existence and uniqueness of solutions for kernel-based system identification

The notion of reproducing kernel Hilbert space (RKHS) has emerged in system identification during the past decade. In the resulting framework, the impulse response estimation problem is formulated as a regularized optimization defined on an infinite-dimensional RKHS consisting of stable impulse responses. The consequent estimation problem is...

Kernel-based Identification of Periodically Parameter-Varying Models of Power Kites

Electricity in the Air: Insights From Two Decades of Advanced Control Research and Experimental Flight Testing of Airborne Wind Energy Systems

Airborne wind energy systems convert wind energy into electricity using tethered flying devices, typically flexible kites or aircraft. Replacing the tower and foundation of conventional wind turbines can substantially reduce the material use and, consequently, the cost of energy, while providing access to wind at higher altitudes. Because the...

Experimental Validation of Path-Tracking Model Predictive Control for Fixed-Wing Power Kites

A projected gradient and constraint linearization method for nonlinear model predictive control

Projected gradient descent denotes a class of iterative methods for solving optimization programs. In convex optimization, its computational complexity is relatively low whenever the projection onto the feasible set is relatively easy to compute. On the other hand, when the problem is nonconvex, e.g., because of nonlinear equality constraints...

Stability Certificates for a Model-Based Controller for Autonomous Power Kites