A practical parameter determination strategy based on improved hybrid PSO algorithm for higher-order sliding mode control of air-breathing hypersonic vehicles

Abstract A hybrid particle swarm optimization (PSO) algorithm for longitudinal dynamic models of air-breathing hypersonic flight vehicles (HFV) is proposed and applied to determine design parameters for a higher-order sliding model controller (HOSMC) while considering the effects of parameter uncertainty on trajectory tracking control. The input and output linearization of air-breathing HFV longitudinal dynamic models were achieved using the feedback linearization approach. Also, an HOSMC was designed for air-breathing HFV trajectory tracking control, and the design parameters were determined based on stochastic robustness analysis and hybrid PSO algorithm. Simulations revealed that the HOSMC design parameters can be optimized effectively and easily using the parameter determination strategy based on an improved hybrid PSO algorithm. The proposed HOSMC was used to stabilized the trajectory tracking of air-breathing HFV and the controller proposed exhibited great robustness.

[1]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  Arie Levant,et al.  Homogeneity approach to high-order sliding mode design , 2005, Autom..

[4]  Arie Levant,et al.  Adjustment of high‐order sliding‐mode controllers , 2009 .

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

[6]  Christopher I. Marrison,et al.  Design of Robust Control Systems for a Hypersonic Aircraft , 1998 .

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[9]  Jie Wu,et al.  Trajectory tracking for an autonomous airship using fuzzy adaptive sliding mode control , 2012, Journal of Zhejiang University SCIENCE C.

[10]  Reza Firsandaya Malik,et al.  New particle swarm optimizer with sigmoid increasing inertia weight , 2007 .

[11]  Arie Levant,et al.  Quasi-continuous high-order sliding-mode controllers , 2005, IEEE Transactions on Automatic Control.

[12]  Arie Levant,et al.  Higher-order sliding modes, differentiation and output-feedback control , 2003 .

[13]  Wei Zheng,et al.  Positioning control for an unmanned airship using sliding mode control based on fuzzy approximation , 2014 .

[14]  David B. Doman,et al.  Control-Oriented Modeling of an Air-Breathing Hypersonic Vehicle , 2007 .

[15]  Yang Tao,et al.  Quasi-continuous high-order sliding mode controller and observer design for flexible hypersonic vehicle☆ , 2013 .

[16]  Changyin Sun,et al.  Finite time integral sliding mode control of hypersonic vehicles , 2013 .

[17]  Lin Cao,et al.  Aerodynamic configuration optimization for hypersonic gliding vehicle based on improved hybrid multi-objective PSO algorithm , 2015, 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[18]  Arie Levant,et al.  Universal single-input-single-output (SISO) sliding-mode controllers with finite-time convergence , 2001, IEEE Trans. Autom. Control..

[19]  Changyin Sun,et al.  Tracking control of air-breathing hypersonic aircrafts in cruising flight based on sliding mode method , 2011, Proceedings of the 30th Chinese Control Conference.

[20]  Michael A. Bolender,et al.  An Aerothermal Flexible Mode Analysis of a Hypersonic Vehicle (Postprint) , 2006 .

[21]  A. Levant.  Universal SISO sliding-mode controllers with finite-time convergence , 2001 .

[22]  Huijun Gao,et al.  Adaptive sliding mode tracking control for a flexible air-breathing hypersonic vehicle , 2012, J. Frankl. Inst..