Guidance and control of a scaled-down quad tilt prop PAV

Song, Jun-Beom; Byun, Young-Seop; Kim, Jeong; Kang, Beom-Soo

doi:10.1007/s12206-015-0143-9

Cited by 2 publications

(1 citation statement)

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“…In practical flight scenarios, the uncertainties in the system parameters along the attitude varying may be unavoidable due to complex couplings between the attitude and position, the unknown external disturbances, or the modeling uncertainties and attitude constraints that affect the control system dynamics [11,12]. Consequently, the precise attitude control is of great importance for the control system design of QTRA.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

2017

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A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

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Section: Introductionmentioning

confidence: 99%