A Class Of Nonlinear Systems Output Feedback Control

The problem of output feedback control for a class of single-input-single-output (SISO) nonlinear system is considered.First,an observer is introduced for the state estimates and the observer error are thus derived.Then,the nonlinear output feedback controller and robust adaptive neural network controller is established for the nonlinear system in output-strict-feedback form under only output is available for feedback,by utilizing integral backstepping method,adaptive neural network design and Nussbaumtype gain technique.It is shown that,the controllers designed ensure the closed systems derived the desired performance.The main results in this paper are as follows:1,A robust backstepping design procedure is developed for output-feedback stabilization of a nonlinear system.A controller is constructed to achieve regulation of the output,and keeps all signals bounded.2,The problem of output feedback stabilization for a class of nonlinear system is considered.By constructing the appropriate Lyapunov functionals and using the backstepping design,an output feedback controller making the closed-loop system keeps all the closed-loop signals bounded while achieve regulation of the output is established.3,Robust adaptive neural network control and backstepping design are considered. An adaptive neural network controller is established for a uncertain nonlinear output feedback system.The controller regulates the output to a neighborhood of origin by properly choosing design parameters,and keeps all signals bounded.4,A robust adaptive neural network controller is established for the uncertain nonlinear system in output-strict-feedback form under only output is available for feedback, by utilizing the Nussbaum-type gain technique with adaptive neural network design. Under this controller,the system achieves regulation the output to a neighborhood of origin by appropriately choosing design parameters,and keeps all the closed-loop signals bounded.