Approximation‐based adaptive control of uncertain non‐linear pure‐feedback systems with full state constraints

IET Control Theory and Applications - Tập 8 Số 17 - Trang 2070-2081 - 2014
Bong Su Kim1, Sung Jin Yoo1
1School of Electrical and Electronics Engineering, Chung-Ang University84 Heukseok-Ro, Dongjak-GuSeoul156-756South Korea

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Tài liệu tham khảo

10.1007/978-3-0348-8407-5

10.1016/S0005-1098(02)00135-8

10.1007/978-1-4612-0205-9

NgoK.B.MahonyR. andJiangZ.P.: ‘Integrator backstepping using barrier functions for systems with multiple state constraints’.Proc. 44th IEEE Conf on decision and control 2005 pp.8306–8312

10.1016/j.automatica.2008.11.017

10.1016/j.automatica.2011.08.044

10.1007/s12555-012-0403-8

10.1109/TNN.2010.2047115

TeeK.P. andGeS.S.: ‘Control of nonlinear systems with full state constraint using a barrier lyapunov function’.Proc. 48th IEEE Conf on Decision and Control 2009 pp.8618–8623

10.1080/00207179.2011.631192

TeeK.P. andGeS.S.: ‘Control of state‐constrained nonlinear systems using integral barrier Lyapunov functionals’.Proc. 51st IEEE Conf on Decision and Control 2012 pp.3239–3244

Krstic M., 1995, Nonlinear and adaptive control design

10.1109/TAC.2000.880994

10.1016/S0005-1098(01)00254-0

10.1016/S0005-1098(02)00034-1

10.1016/j.automatica.2007.11.025

10.1016/j.automatica.2006.01.004

10.1016/j.amc.2012.12.034

Jeffreys H., 1988, Methods of mathematical physics

10.1080/002071798222280

10.1109/TIE.2003.809394

Ge S.S., 2001, Stable adaptive neural network control

10.1002/0471781819

Khalil H.K., 1996, Nonlinear systems

10.1109/9.256328

Yoo S.J., 2007, Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates, Inf. Sci., 177, 3074, 10.1016/j.ins.2007.02.009

Elzebda J.M., 1989, Development of a analytical model of wing rock for slender delta wings, J. Aircraft, 26, 737, 10.2514/3.45833

10.1002/stc.143

KarimiB.MenhajM.B. andSabooriI.: ‘Robust adaptive control of nonaffine nonlinear systems using radial basis function neural networks’.Proc. 32nd IEEE Conf on Industrial Electronics 2006 pp.495–500