Wavelet Sliding-Mode Control with Varying Boundary Layer via Time-Variant Sliding Function for Pitch Autopilot

Authors

1 Assoc. Prof., Faculty of Mathematics, Lorestan University, Lorestan, Iran

2 Lect., Faculty of Mathematics, Lorestan University, Lorestan, Iran

Abstract

Abstract
In this paper, the nonlinear method, “wavelet sliding-mode control with varying boundary layer via time-variant sliding function” for the pitch autopilot controlling EMRAAT missle, based on steering logic BTT is employed. Time-variant sliding surface filters all un-modeled frequencies. The adjustable boundary layer width, break-frequency bandwidth and the consequent parameters in neural wavelet approximation are tuned, to reduce the effects of the system uncertainties, un-modeled frequencies,. The chattering phenomenon do not occur and control cost is lower than other methods. The tracking operation is time optimal operation . Three theorems and one lemma, which facilitate design of the employed controller, are presented. To investigation the operation of the employed method, the Mexican Hat wavelet function as wavelet basis function is selected. This method is implemented for a couple of examples, pitch autopilot control systemand invert pendulum, to illustrate adventages of the proposed method.
Keywords:Break frequency bandwidth, Rejection regulator, Sliding Mode Control, Missle, Autopilot, Wavelet Varying Boundary Layer, Wavelet networks

Keywords

Main Subjects

References

[1] Astrom KJ, Wittenmark B (1995) Adaptive control. Addison Wesley.
[2] Gang F (2006) A survey on analysis  and design of model-based fuzzy control systems. IEEE Transactions on Fuzzy Systems 14(5): 676–697.
[3] Sastry SS, Isidori A (1989) Adaptive control of linearizable systems. IEEE Trans Automat Contr 34: 1123–1131.
[4] Chang KY, Lin HJ, Cheng TL (2008) Sliding mode controller design with  norm and variance constraints for bilinear stochastic systems. IEICE Trans Fundamentals 91-A(2): 686–691.
[5] Turner MC, Bates DG (2007) Mathematical methods for robust and  non-linear control. Springer Berlin Heidelberg New York.
[6] Kung CC, Chen TH, Kung LH (2005) Modified adoptive fuzzy sliding mode controller for uncertain nonlinear systems. IEJCE Trans Fundamentals 88-A(5): 1328–1334.
[7] Tzafestas SG, Rigatos GG (1999) Design and stability analysis of a new sliding-mode fuzzy logic controller of reduced complexity. Machine Intelligence and Robotic Control 1(1): 27–41.
[8] Babuska R, Verbruggen H (2003) Neuro-fuzzymethod for non-linear system identification. Annual Review in Control 27: 73–85.
[9] Ho HF, Wong YK, Rad AB (2009) Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISI systems. Simulation Modeling Practice and Theory 17: 1199–1210.
[10] Hsu CF, Lee TT, Lin CM, Chen LY (2004) Robust neuro-fuzzy controller design via sliding- mode approach. IEEE Proceeding of the Budapest, Hungary Conference. July: 917–922.
[11] Lee H, Kim F, Kang HJ (2001) A new sliding-mode control with fuzzy boundary layer. Fuzzy Sets and Systems 120: 135–143.
[12] Wang CH, Lin TC, Lee TT, Liu HL (2002) Adaptive hybrid intelligent control for uncertain non-linear dynamical systems. IEEE Transaction on System, Man, and Cybernetics 32(5): 583–597.
[13] Chen FC, Liu CC (1994) Adaptively controlling nonlinear continuous-time systems using multilayer neural networks. IEEE Trans Automat Contr 39: 1306–1310.
[14] Yesidirek A, Lewis FL (1995) Feedback linearization using neural networks. Automatica 31: 1659–1664.
[15] Polycarpou MM, Ioannou PA (1992) Modeling, identification and stable adaptive control of continuous-time nonlinear dynamical system using neural networks. in Proc Am Contr Conf, Chicago, IL: 36–40.
[16] Wang LX (1994) Adaptive fuzzy systems and control: design and analysis. Englewood Cliffs, NJ: Prentice-Hall.
[17] Spooner JT, Passino KM (1996) Stable adaptive control using fuzzy systems and neural networks. IEEE Trans Fuzzy Syst 4: 339–359.
[18] Ge SS, Lee TH, Harris CJ (1998) Adaptive neural network control of robotic manipulators. London, U.K.: World Scientific.
[19] Wang LX (1994) Adaptive fuzzy systems and control: design and stability analysis. Englewood Cliffs NJ: Prentice-Hall.
[20] Srivasiava S, Singh M, Hanmandlu M, Jha AN (2005) New fuzzy wavelet neural networks for system identification and control. Applied Soft Computing 6: 1–17.
[21] Phan PA, Gale TJ (2008) Direct adaptive fuzzy control with a self-structuring algorithm. Fuzzy Sets and Systems 159: 871–899.
[22] Yarahmadi M, Karbasi SM, Mirzaii A (2010) Robust  wavelet sliding-mode control via time-variant sliding function. IEICE Trans Fundamentals 93-A(6): page 1181.
[23] Yarahmadi M, Karbasi SM, Mirzaii A (2014) Indirect fuzzy sliding-mode with varying boundary layer via time-variant sliding function. IJIFS Jurnal, In print.
[24] Schumache DA (1994) Tactical missle autoplot design using nonlinear control. Thesis & Dissertation, The university of Michigan.
[25] SlotineJJ, Li V (1991) Applied non-linear control. Englewood Cliffs. NJ.
[26] Chui CK (1992) An introduction to wavelets, Academic press.
[27] Delyon B, Juditsky A, Benveniste A (1995) Accuracy analysis for wavelet approximations. IEEE Trans Neural Netw 6(2): 332–348.
Journal of Solid and Fluid Mechanics
Volume 4, Issue 1 - Serial Number 1
March and April 2014
Pages 93-106

Files

Share

How to cite

Statistics