Aircraft longitudinal motion control based on Takagi-Sugeno fuzzy model

Graphical abstractDisplay Omitted HighlightsWe propose a fuzzy controller design of longitudinal motion of an aircraft.The procedure guarantees stability in presence of model uncertainties.Simulations prove better control performance than conventional approach. Fuzzy logic control of longitudinal motion of an aircraft based on TakagiSugeno modelling approach is presented in this paper. TakagiSugeno model of the motion along the desired trajectory is used for state-space parallel decomposition controller (PDC) design. The proposed control scheme guarantees stability of closed loop and asymptotical step pitch angle reference signal tracking. Simulation results on twin-engine short-range transport aircraft LET L410 indicate that the proposed control law can represent a feasible aircraft motion control technique and especially in presence of model inaccuracies and disturbances it overcomes the most commonly used approaches.

[1]  Narasimhan Sundararajan,et al.  Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks , 2001, Autom..

[2]  Christopher J. Miller,et al.  Nonlinear Dynamic Inversion Baseline Control Law: Flight-Test Results for the Full-scale Advanced Systems Testbed F/A-18 Airplane , 2011 .

[3]  Aarti Panday,et al.  A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling , 2013, Int. J. Appl. Math. Comput. Sci..

[4]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[5]  Kazuo Tanaka,et al.  An approach to fuzzy control of nonlinear systems: stability and design issues , 1996, IEEE Trans. Fuzzy Syst..

[6]  Robert F. Stengel,et al.  Flight Control Design using Nonlinear Inverse Dynamics , 1986, 1986 American Control Conference.

[7]  David B. Doman,et al.  Dynamic Inversion-Based Adaptive/Reconfigurable Control of the X-33 on Ascent , 2002 .

[8]  Guang-Bin Huang,et al.  Adaptive fuzzy fault-tolerant controller for aircraft autolanding under failures , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Kazuo Tanaka,et al.  A Sum-of-Squares Approach to Modeling and Control of Nonlinear Dynamical Systems With Polynomial Fuzzy Systems , 2009, IEEE Transactions on Fuzzy Systems.

[10]  Thierry-Marie Guerra,et al.  LMI-based relaxed nonquadratic stabilization conditions for nonlinear systems in the Takagi-Sugeno's form , 2004, Autom..

[11]  Karl-Erik Årzén,et al.  Piecewise quadratic stability of fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[12]  John J. Burken,et al.  Takagi-Sugeno Fuzzy Model-Based Flight Control and Failure Stabilization , 2011 .

[13]  Abdulrahman H. Bajodah,et al.  Generalised dynamic inversion spacecraft control design methodologies , 2009 .

[14]  Suresh M. Joshi,et al.  Engineering Notes Direct Model Reference Adaptive Control with Actuator Failures and Sensor Bias , 2014 .

[15]  N. Sadati,et al.  Intelligent autolanding controller design using neural networks and fuzzy logic , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[16]  N. Hassan,et al.  Self-Tuning Fuzzy PID Controller Design for Aircraft Pitch Control , 2012, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.

[17]  Oscar Castillo,et al.  Design of a Fuzzy System for the Fly the de Havilland Beaver , 2014, IEEE Conf. on Intelligent Systems.

[18]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  Behrooz Safarinejadian,et al.  Parallel distributed compensator design of tank level control based on fuzzy Takagi-Sugeno model , 2014, Appl. Soft Comput..

[20]  Lawrence Larkin,et al.  A fuzzy logic controller for aircraft flight control , 1984, The 23rd IEEE Conference on Decision and Control.

[21]  Kazuo Tanaka,et al.  Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs , 1998, IEEE Trans. Fuzzy Syst..

[22]  Kuru Ratnavelu,et al.  Optimal control for stochastic linear quadratic singular neuro Takagi-Sugeno fuzzy system with singular cost using genetic programming , 2014, Appl. Soft Comput..

[23]  Kazuo Tanaka,et al.  A multiple Lyapunov function approach to stabilization of fuzzy control systems , 2003, IEEE Trans. Fuzzy Syst..

[24]  Matthew C. Turner,et al.  Experimental Implementation of a Nonlinear Dynamic Inversion Controller with Antiwindup , 2013 .

[25]  Jih-Gau Juang,et al.  Intelligent Fuzzy Systems for Aircraft Landing Control , 2005, FSKD.

[26]  Lucian Teodor Grigorie,et al.  Automatic Control of Aircraft in Longitudinal Plane During Landing , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[27]  Anthony J. Calise,et al.  Nonlinear adaptive flight control using neural networks , 1998 .

[28]  Radu-Emil Precup,et al.  Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems , 2015, Appl. Soft Comput..

[29]  Q. P. Chu,et al.  A Hybrid Sensor Based Backstepping Control Approach with its Application to Fault-Tolerant Flight Control , 2014 .

[30]  Oscar Castillo,et al.  Design of a Fuzzy System for the Longitudinal Control of an F-14 Airplane , 2011, Soft Computing for Intelligent Control and Mobile Robotics.

[31]  Ramón Galán,et al.  Fuzzy optimal control using generalized Takagi-Sugeno model for multivariable nonlinear systems , 2015, Appl. Soft Comput..

[32]  Robert F. Stengel,et al.  Robust nonlinear flight control of a high-performance aircraft , 2005, IEEE Transactions on Control Systems Technology.

[33]  Ramesh K. Agarwal,et al.  Automatic Landing System Design Using Fuzzy Logic , 1998 .

[34]  Kazuo Tanaka,et al.  Parallel distributed compensation of nonlinear systems by Takagi-Sugeno fuzzy model , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[35]  Eric Ting,et al.  Optimized Fuzzy-Proportional/Integral/Derivative Controller for Aircraft Pitch Control , 2013, J. Aerosp. Inf. Syst..

[36]  Oscar Castillo,et al.  Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory , 2003, Appl. Soft Comput..

[37]  Warren E. Dixon,et al.  Asymptotic Tracking for Aircraft via Robust and Adaptive Dynamic Inversion Methods , 2010, IEEE Transactions on Control Systems Technology.

[38]  Chien-Chun Kung.  Nonlinear H∞ robust control applied to F-16 aircraft with mass uncertainty using control surface inverse algorithm , 2008, J. Frankl. Inst..

[39]  Narasimhan Sundararajan,et al.  Direct Adaptive Neural Flight Controller for F-8 Fighter Aircraft , 2006 .

[40]  Kazuo Tanaka,et al.  Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach , 2008 .

[41]  Felipe Lara-Rosano,et al.  Model-reference adaptive control based on neurofuzzy networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[42]  Manuela Battipede,et al.  Adaptive Neurocontroller for a Nonlinear Combat Aircraft model , 1998 .

[43]  J. Valasek,et al.  Adaptive dynamic inversion control of a linear scalar plant with constrained control inputs , 2005, Proceedings of the 2005, American Control Conference, 2005..

[44]  Hai-Jun Rong,et al.  Adaptive fuzzy control of aircraft wing-rock motion , 2014, Appl. Soft Comput..

[45]  Sukumar Kamalasadan,et al.  A Neural Network Parallel Adaptive Controller for Fighter Aircraft Pitch-Rate Tracking , 2011, IEEE Transactions on Instrumentation and Measurement.

[46]  David B. Doman,et al.  Dynamic inversion-based adaptive/reconfigurable control of the X-33 on ascent , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[47]  Claudia-Adina Dragos,et al.  Online identification of evolving Takagi-Sugeno-Kang fuzzy models for crane systems , 2014, Appl. Soft Comput..