Discrete-time hypersonic flight control based on extreme learning machine

This paper describes the neural controller design for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). The dynamics are transformed into the strict-feedback form. Considering the uncertainty, the neural controller is constructed based on the single-hidden layer feedforward network(SLFN). The hidden node parameters are modified using extreme learning machine (ELM) by assigning random values. Instead of using online sequential learning algorithm (OSLA), the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of closed-loop system. By estimating the bound of output weight vector, a novel back-stepping design is presented where less online parameters are required to be tuned. The simulation study is presented to show the effectiveness of the proposed control approach.

[1]  Meng Joo Er,et al.  Adaptive Fuzzy Control With Guaranteed Convergence of Optimal Approximation Error , 2011, IEEE Transactions on Fuzzy Systems.

[2]  Zhongke Shi,et al.  Direct neural discrete control of hypersonic flight vehicle , 2012 .

[3]  Guo-Xing Wen,et al.  Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems , 2010, Neurocomputing.

[4]  Kimio Kanai,et al.  A discrete-time multivariable model-following method applied to decoupled flight control , 1986 .

[5]  Erkki Oja,et al.  GPU-accelerated and parallelized ELM ensembles for large-scale regression , 2011, Neurocomputing.

[6]  Shixing Wang,et al.  Adaptive neural control based on HGO for hypersonic flight vehicles , 2011, Science China Information Sciences.

[7]  Robert F. Stengel,et al.  Robust Nonlinear Control of a Hypersonic Aircraft , 1999 .

[8]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[9]  Qinyu. Zhu.  Extreme Learning Machine , 2013 .

[10]  Robert Stengel,et al.  Digital Controllers for VTOL Aircraft , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Danwei W. Wang,et al.  Dynamic Surface Control of Constrained Hypersonic Flight Models with Parameter Estimation and Actuator Compensation , 2014 .

[12]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[13]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[14]  Zhongke Shi,et al.  Brief Paper - Universal Kriging control of hypersonic aircraft model using predictor model without back-stepping , 2013 .

[15]  P.V. Kokotovic,et al.  The joy of feedback: nonlinear and adaptive , 1992, IEEE Control Systems.

[16]  Guang-Bin Huang,et al.  Convex Incremental Extreme Learning Machine , 2007 .

[17]  Sundaram Suresh,et al.  Stable indirect adaptive neural controller for a class of nonlinear system , 2011, Neurocomputing.

[18]  Youdan Kim,et al.  Nonlinear discrete-time reconfigurable flight control law using neural networks , 2006, IEEE Trans. Control. Syst. Technol..

[19]  B. Xu,et al.  Adaptive Kriging controller design for hypersonic flight vehicle via back-stepping , 2012 .

[20]  Meng Joo Er,et al.  Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence , 2013, IEEE Transactions on Fuzzy Systems.

[21]  Yu Zhou,et al.  Composite adaptive fuzzy H∞ tracking control of uncertain nonlinear systems , 2013, Neurocomputing.

[22]  Xu Bin,et al.  Adaptive neural control based on HGO for hypersonic flight vehicles , 2011 .

[23]  Fuchun Sun,et al.  Adaptive discrete-time controller design with neural network for hypersonic flight vehicle via back-stepping , 2011, Int. J. Control.

[24]  A. Serrani,et al.  Nonlinear Robust Adaptive Control of Flexible Air-Breathing Hypersonic Vehicles , 2009 .