Automatic Carrier Landing System multilayer parameter design based on Cauchy Mutation Pigeon-Inspired Optimization

Abstract The parameter adjusting in Automatic Carrier Landing System (ACLS) is a time-consuming and tedious task. In order to improve the efficiency of the adjusting task and overcome the difficulties in the manual parameter adjustment, a multilayer optimization strategy, in which ACLS is clearly divided into four layers including inner loop, autopilot, guidance control and guidance compensation, is proposed in this study and adopted for the parameter design. Besides, a novel algorithm, named Cauchy Mutation Pigeon-Inspired Optimization (CMPIO) which is inspired by Cauchy distribution, is proposed to optimize ACLS parameters in each layer. Comparative simulations are conducted to verify the feasibility of the multilayer design strategy and the superiority of CMPIO. To enhance the authenticity of the simulations in the guidance compensation layer, some stochastic conditions are considered with different deck motion, air wake and radar noise turbulences alleviated by several rejection methods. The simulation results prove that the designed ACLS based on the multilayer design strategy satisfies the acknowledged criteria including the time and the frequency domain. Furthermore, the stability of the inner loop and the autopilot integrated with Approach Power Compensation System (APCS) are confirmed.

[1]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Honglun Wang,et al.  Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer , 2017 .

[3]  Qidan Zhu,et al.  Longitudinal automatic carrier landing system guidance law using model predictive control with an additional landing risk term , 2019 .

[4]  Haibin Duan,et al.  Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning , 2014, Int. J. Intell. Comput. Cybern..

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

[6]  Xin-Ping Guan,et al.  A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques , 2015, Appl. Soft Comput..

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Rui Zhou,et al.  Pigeon-inspired optimization applied to constrained gliding trajectories , 2015 .

[9]  Paulo Moura Oliveira,et al.  Particle swarm optimization with fractional-order velocity , 2010 .

[10]  Xiangtao Li,et al.  Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm , 2014 .

[11]  Peter J Seiler,et al.  Susceptibility of F/A-18 Flight Controllers to the Falling-Leaf Mode: Linear Analysis , 2011 .

[12]  A. WHITEN Operant Study of Sun Altitude and Pigeon Navigation , 1972, Nature.

[13]  Hans-Dieter Joos,et al.  Design of Autoland Controller Functions with Multiobjective Optimization , 2002 .

[14]  Gang Liu,et al.  A method of F-18/A carrier landing position prediction based on back propagation neural network , 2016, 2016 7th International Conference on Mechanical and Aerospace Engineering (ICMAE).

[15]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[16]  W. Keeton,et al.  Magnets interfere with pigeon homing. , 1971, Proceedings of the National Academy of Sciences of the United States of America.

[17]  R. W. Huff,et al.  H-Dot Automatic Carrier Landing System for Approach Control in Turbulence , 1981 .

[18]  Gang Liu,et al.  Design and Simulation of F/A-18A Automatic Carrier Landing Guidance Controller , 2016 .

[19]  Carey S. Buttrill,et al.  Simulation model of a twin-tail, high performance airplane , 1992 .

[20]  Marcello R. Napolitano,et al.  Estimation of the lateral-directional aerodynamic parameters from flight data for the NASA F/A-18 HARV , 1996 .

[21]  Haibin Duan,et al.  Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system , 2015 .

[22]  Harish Garg,et al.  A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units , 2018 .

[23]  A. L. Prickett,et al.  Flight testing of the F/A-18E/F automatic carrier landing system , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[24]  Hyuk Lim,et al.  Model-based disturbance attenuation for CNC machining centers in cutting process , 1999 .

[25]  Ming Zhu,et al.  Adaptive Sliding Mode Relative Motion Control for Autonomous Carrier Landing of Fixed-Wing Unmanned Aerial Vehicles , 2017, IEEE Access.

[26]  Nan Zhang,et al.  Select N agents with better fitness values from X all to replace the current population X Evaluate and sort the fitness of X all End of iteration ? Return best solution End Mass weighting Cauchy mutation , 2016 .

[27]  R. A. Hess,et al.  Improved automatic carrier landing using deck motion prediction , 1976 .

[28]  M. B. Subrahmanyam,et al.  H-infinity design of F/A-18A automatic Carrier Landing System , 1994 .

[29]  David E. Goldberg,et al.  Control system optimization using genetic algorithms , 1992 .

[30]  Xin Wang,et al.  Adaptive Disturbance Rejection Control for Automatic Carrier Landing System , 2016 .

[31]  M. Davison,et al.  Magnetoreception and its trigeminal mediation in the homing pigeon , 2004, Nature.

[32]  Haibin Duan,et al.  Control parameter design for automatic carrier landing system via pigeon-inspired optimization , 2016 .

[33]  J. Urnes,et al.  Development of the F/A-18A automatic carrier landing system , 1985 .

[34]  Marc L. Steinberg,et al.  A Comparison of Neural, Fuzzy, Evolutionary, and Adaptive Approaches for Carrier Landing , 2001 .

[35]  Nan Zhang,et al.  Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation , 2017, Inf. Sci..

[36]  D. J. Mook,et al.  Improved noise rejection in automatic carrier landing systems , 1992 .