Close formation flight of swarm unmanned aerial vehicles via metric-distance brain storm optimization

Close formation flight of swarm unmanned aerial vehicles (UAVs) has drawn much attention from scholars due to its significant importance in many aspects. In this paper, we focus on an advanced controller design for swarm UAV close formation based on a novel bio-inspired algorithm, i.e., metric-distance brain storm optimization (MDBSO). The proposed method utilizes the brain storm optimization (BSO) which has been extensively adopted in complicated systems with great performances and modifies its basic operators to formulate the formation flight controller design. The original clustering operator in BSO is replaced by a fresh clustering method based on metric distances, while the individual updating operator utilizes Lévy distribution to extend search steps to fit into the metric searching regions. Then the proposed algorithm is applied to optimize the benchmark controller in swarm UAV close formation to enhance the tracking performances under complicated circumstances. Simulation results demonstrate that our approach is more superior in stable configuration of swarm UAV close formations by comparing with several generic methods.

[1]  Iddo Eliazar,et al.  A geometric theory for Lévy distributions , 2014 .

[2]  Yukio-Pegio Gunji,et al.  Emergence of the scale-invariant proportion in a flock from the metric-topological interaction , 2014, Biosyst..

[3]  Hai Lin,et al.  A bumpless hybrid supervisory control algorithm for the formation of unmanned helicopters , 2013 .

[4]  Ferrante Neri,et al.  A memetic Differential Evolution approach in noisy optimization , 2010, Memetic Comput..

[5]  Yuhui Shi,et al.  Optimal Satellite Formation Reconfiguration Based on Closed-Loop Brain Storm Optimization , 2013, IEEE Computational Intelligence Magazine.

[6]  Jun Zhang,et al.  Parameter investigation in brain storm optimization , 2013, 2013 IEEE Symposium on Swarm Intelligence (SIS).

[7]  JiaZheng Pei,et al.  Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm , 2017 .

[8]  Tatsuji Takahashi,et al.  Metric and Topological Neighborhoods in Flocking Models , 2014, BICT.

[9]  T. Niizato,et al.  Metric-topological interaction model of collective behavior , 2011 .

[10]  Yuhui Shi,et al.  Convergence analysis of brain storm optimization algorithm , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[11]  John D'Azzo,et al.  Close formation flight control , 1999 .

[12]  Haibin Duan,et al.  Receding horizon control for multiple UAV formation flight based on modified brain storm optimization , 2014, Nonlinear Dynamics.

[13]  H. Weimerskirch,et al.  Energy saving in flight formation , 2001, Nature.

[14]  Y. Johnson,et al.  Robust controller design and performance of forward-velocity dynamics of UAVs in close formation flight , 2014, 2014 International Conference on Advances in Green Energy (ICAGE).

[15]  G. Parisi,et al.  Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study , 2007, Proceedings of the National Academy of Sciences.

[16]  Yilun Shang,et al.  Consensus reaching in swarms ruled by a hybrid metric-topological distance , 2014, The European Physical Journal B.

[17]  Ronald J. Ray,et al.  Flight Test Techniques Used to Evaluate Performance Benefits During Formation Flight , 2002 .

[18]  M. Pachter,et al.  Flight Test Results of Close Formation Flight for Fuel Savings , 2002 .

[19]  Yuhui Shi,et al.  Brain Storm Optimization Algorithm , 2011, ICSI.

[20]  Ning Xian,et al.  Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV , 2017 .

[21]  Gianmarco Radice,et al.  Close proximity formation flying via linear quadratic tracking controller and artificial potential function , 2015 .

[22]  Yuhui Shi,et al.  Solution clustering analysis in brain storm optimization algorithm , 2013, 2013 IEEE Symposium on Swarm Intelligence (SIS).

[23]  Yuhui Shi,et al.  Maintaining population diversity in brain storm optimization algorithm , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[24]  Hugh H. T. Liu,et al.  Robust Design of Close Formation Flight Control via Uncertainty and Disturbance Estimator , 2016 .

[25]  Rajesh Kumar,et al.  A novel two-level particle swarm optimization approach for efficient multiple sequence alignment , 2015, Memetic Comput..

[26]  Junfeng Chen,et al.  Brain storm optimization algorithm: a review , 2016, Artificial Intelligence Review.

[27]  Haibin Duan,et al.  Lévy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system , 2017 .