Details

Autor(en) / Beteiligte

• Yan-Jun Liu
• Shao-Cheng Tong
• Wang, Dan
• Tie-Shan Li
• Chen, C. L. Philip

Titel

Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems

Ist Teil von

• IEEE transaction on neural networks and learning systems, 2011-08, Vol.22 (8), p.1328-1334

Ort / Verlag

New York, NY: IEEE

Erscheinungsjahr

2011

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• An adaptive output feedback control is studied for uncertain nonlinear single-input-single-output systems with partial unmeasured states. In the scheme, a reduced-order observer (ROO) is designed to estimate those unmeasured states. By employing radial basis function neural networks and incorporating the ROO into a new backstepping design, an adaptive output feedback controller is constructively developed. A prominent advantage is its ability to balance the control action between the state feedback and the output feedback. In addition, the scheme can be still implemented when all the states are not available. The stability of the closed-loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to validate the effectiveness of the proposed scheme.