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Summary This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.

NOMENCLATURE AR aspect ratio c coanda surface height (m) H main nozzle height (m) l coanda surface length (m) L main nozzle width (m) m · air mass-flow rate (kg/sec) P a free stream pressure (Pascal) R coanda surface radius (m) t average secondary slot height (m) T thrust (N) α T thrust-vector angle (deg) θ collar cut-off angle (deg) CFD computational fluid dynamics FTV fluidic thrust vectoring ISA international standard atmosphere RCS radar cross section RPM revolution per minute SLS sea level standard a, p, s 1,2 subscripts for: axial component, primary nozzle, upper and lower secondary nozzles ABSTRACTThis paper presents the application of a relatively new technique of fluidic thrust-vectoring (FTV), named Co-flow, for a small gas-turbines. The performance is obtained via experiment and computational fluid dynamics (CFD). The effects of a few selected parameters including the engine throttle setting, the secondary air mass-flow rate and the secondary slot height upon thrust-vectoring performance are provided. Thrust vectoring performance is characterised by the ability of the system to deflect the engine thrust with respect to the delivered secondary air mass-flow rate. The experimental study was conducted under static conditions in an outdoor environment at Cranfield University workshop that was especially designed for this purpose. As part of this investigation, the system was modelled by CFD techniques, using Pointwise's Gridgen software and the three-dimensional flow solver, Fluent. Also, Cranfield's gas-turbine performance code (TurboMatch) was utilised to estimate boundary conditions for the CFD analysis with respect to the integrated nozzle. The presented technique is easy-to-use approach and offers better result for thrust-vectoring problems than previously published works. Experimental results do show the overall viability of the blowing slot mechanism as a means of vectoring the engine thrust, with the current configuration. Computational predictions are shown to be consistent with the experimental observations and make the CFD model a reliable tool for predicting Co-flow fluidic thrust-vectoring performance of similar systems.

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