On data-driven stabilization of systems with nonlinearities satisfying quadratic constraints

In this paper, we directly design a state feedback controller that stabilizes class of uncertain nonlinear systems solely based on input-state data collected from finite-length experiment. Necessary and sufficient conditions are derived to guarantee the system is absolutely stabilizable designed. Results under some relaxed prior information about system, strengthened assumptions perturbed also discussed. All results semi-definite programs depend only, which – once solved return controllers. As such they represent end-to-end solutions problem learning control for an important systems. Numerical examples illustrate method with different levels information.