Showing papers by "Yang Gao published in 2002"
TL;DR: A robust Adaptive Fuzzy Neural Controller suitable for identification and control of a class of uncertain MIMO nonlinear systems using the Lyapunov approach is presented.
81 citations
TL;DR: In this paper, a search for the Higgsstrahlung process e+e−→HZ is carried out, covering decays of a Higgs boson into any quark pair, a gluon pair or a tau pair.
13 citations
01 Jan 2002
TL;DR: Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed RAFNC is superior over many existing schemes.
Abstract: This paper presents a robust adaptive fuzzy neural controller (RAFNC) suitable for identification and control of uncertain MIMO nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online adaptive learning ability of uncertain nonlinear systems; (3) Fast adaptation and learning speed; (4) Ease of incorporating expert knowledge; (5) Adaptive control, where structure and parameters of the RAFNC can be self-adaptive in the presence of disturbances to maintain high control performance; (6) Robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed RAFNC is superior over many existing schemes.
5 citations
07 Aug 2002
TL;DR: A robust adaptive fuzzy neural controller suitable for control of uncertain multi-input-multi-output (MIMO) nonlinear systems and has self-organizing fuzzy neural structure, fast online learning ability of uncertain MIMO non linear systems, and adaptive robust control.
Abstract: This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for control of uncertain multi-input-multi-output (MIMO) nonlinear systems. The proposed controller has the following salient features: (1) self-organizing fuzzy neural structure, (2) fast online learning ability of uncertain MIMO nonlinear systems; and (3) adaptive robust control. Two simulation examples are used to demonstrate excellent performance of the proposed controller.
4 citations