Data learning based hypersonic flight control using ELM

This paper is towards the controller design using extreme learning machine (ELM) for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). The basic idea is to train the data learning from previous controller and then obtain the optimal weight. In the first step, the existed the back-stepping controller with high order neural networks (HONNs) is borrowed to collect the required data. The “adaptive behavior” of existed the back-stepping controller is trained and tested by batch learning of ELM. Then the optimal parameters obtained from ELM are used as initialization to construct the feedback design for controller. In this way, the prior information of nominal design is not needed and there is no need of online learning for the neural networks (NNs). The simulation study is presented to show the effectiveness of the proposed control approach.

[1]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

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

[4]  Zhongke Shi,et al.  Direct neural control of hypersonic flight vehicles with prediction model in discrete time , 2013, Neurocomputing.

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

[6]  Zhongke Shi,et al.  Command Filter Based Robust Nonlinear Control of Hypersonic Aircraft with Magnitude Constraints on States and Actuators , 2014, J. Intell. Robotic Syst..

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

[8]  DaoXiang Gao,et al.  Dynamic Surface Control for Hypersonic Aircraft Using Fuzzy Logic System , 2007, 2007 IEEE International Conference on Automation and Logistics.

[9]  Danwei Wang,et al.  Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time , 2014, J. Intell. Robotic Syst..

[10]  Zhongke Shi,et al.  Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form , 2014, IEEE Transactions on Cybernetics.

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

[12]  Zhongke Shi,et al.  Neural control of hypersonic flight vehicle model via time-scale decomposition with throttle setting constraint , 2013, Nonlinear Dynamics.

[13]  Shixing Wang,et al.  A Singularly Perturbed System Approach to Adaptive Neural Back-stepping Control Design of Hypersonic Vehicles , 2014, J. Intell. Robotic Syst..

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

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

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

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

[18]  Bin Jiang,et al.  Sliding Mode Control for a Class of Uncertain MIMO Nonlinear Systems with Application to Near-Space Vehicles , 2013 .

[19]  Licheng Jiao,et al.  Adaptive Backstepping Fuzzy Control for Nonlinearly Parameterized Systems With Periodic Disturbances , 2010, IEEE Transactions on Fuzzy Systems.

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

[21]  Petros A. Ioannou,et al.  Adaptive Sliding Mode Control Design fo ra Hypersonic Flight Vehicle , 2004 .

[22]  Zhongke Shi,et al.  Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Mou Chen,et al.  Disturbance-Observer-Based Robust Flight Control for Hypersonic Vehicles Using Neural Networks , 2011 .

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