Adaptive State Observer for Nonlinear MIMO Systems with Uncertain Dynamics
This paper proposes an adaptive observer design for a class of nonlinear MIMO systems with unknown nonlinearities. Neural networks (NN) with online updating weights are utilized to estimate unknown dynamics, such that the precise system model, the Lipschitz or norm-bounded assumptions on the unknown nonlinearities are not required. By developing a novel gain design methods, some constraints used in the neural-based observer and sliding-mode observer designs, i.e., Strictly Positive Real (SPR) or matching conditions, are removed. Applicability of the presented method is verified by simulations.
Adaptive observer Nonlinear system Neural network
Jiping Xu Jing Na Zaiwen Liu Hongbing Xiao
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, School of Automation, Beijing Institute of Technology, Beijing, 100081, P. R. China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
1017-1022
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）