Data-driven Lyapunov-based synthesis of feedback controllers for nonlinear systems

Two optimization problems to design stabilizing controllers for nonlinear systems are proposed based on the well-known Lyapunov stability theory. These are coupled with system identification and nonlinear observer design tools in order to deal with unknown dynamics and unavailability of full-state measurements. Moreover, attempts of introducing desired robustness and performance properties to enforce to the control system are discussed. Numerical simulations are given in order to validate the overall approach.