Manipulation strategy of tilt quad rotor based on active disturbance rejection control

Wang, Zhigang; Li, Jianbo; Duan, Dengyan

doi:10.1177/0954410019875534

Cited by 27 publications

(17 citation statements)

References 13 publications

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“…The flight dynamics characteristics of the quad tiltrotor UAV change greatly during the transition process. 31 In order to ensure that the quad tilt-rotor UAV can fly safely during the transition process, we design the manipulation strategy and controller 32 based on active disturbance rejection control (ADRC). 33 ADRC technology does not depend on the model, which can handle various internal uncertainties, and has strong robustness.…”

Section: Design Of the Flight Controllermentioning

confidence: 99%

A novel system identification algorithm for quad tilt-rotor based on neural network with foraging strategy

Wang¹,

Wu²,

Duan

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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Quad tilt-rotor(QTR) UAV is a nonlinear time-varying system in full flight mode. It is difficult and inaccurate to model the nonlinear time-varying system, which cannot fully reflect the problem of controlling input and system response output in the full flight mode. In order to solve the above problems, a novel neural network model was adopt to identify the nonlinear time-varying system of quad tilt-rotor in full flight mode. An adaptive learning rate algorithm based on foraging strategy is proposed based on the global error BP neural network. Corresponding to the nonlinear time-varying system, BP neural network is set as the time-invariant system structure with constant network structure and continuously changing weights at multiple times, and the nonlinear input-output relationship under the time-varying system is jointly described by fitting the network at all times. The extended Kalman filtering algorithm is used to track the network connection weights by modifying the network weights at the current moment with the input and output data at the next moment. The final identification result shows that the smaller mean square error of both only transition process and full flight mode, shows that using this optimization algorithm can well describe the input and output characteristics of the nonlinear time-varying systems. When the same network structure is adopted, no matter for transition mode or full mode, the BP optimization algorithm based on foraging strategy is better than the global BP algorithm for system identification of the full mode quad tilt-rotor. Therefore, when the BP neural network based on foraging strategy is adopted, the same network structure can be adopted to systematically identify the full mode of quad tilt-rotor by changing the weight.

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Section: Design Of the Flight Controllermentioning

confidence: 99%

A novel system identification algorithm for quad tilt-rotor based on neural network with foraging strategy

Wang¹,

Wu²,

Duan

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

Self Cite

View full text Add to dashboard Cite

show abstract

“…Convert the force on the left side of the front wing under the wind axis into the force and moment under the body axis, as shown in (17). Where c = cos and s = sin.…”

Section: Figurementioning

confidence: 99%

“…Due to ADRC technology does not depend on the model, which can handle various internal uncertainties and has strong robustness. So the ADRC can be used for the design of the control system of the QTR in transition process [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Tuning of ADRC for QTR in Transition Process Based on NBPO Hybrid Algorithm

Wang

Duan

et al. 2019

IEEE Access

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The quad tilt rotor (QTR) has complex dynamics characteristics, and the environmental factors have a great influence on it. This increases the difficulty of its control especially in transition mode. To solve this problem, the design of the controller based on active disturbance rejection control (ADRC) with a novel tuning algorithm is proposed in this paper. The aerodynamic models of propeller, wing, vertical tail and fuselage are build respectively by using the idea of component modeling. The pitch channel of the linearized flight dynamic model is provided for the control system. A simple tuning method of active disturbance rejection control for the quad tilt rotor in transition process that achieves high performance and good robustness is presented. The proposed method makes ADRC become easy to tune and more practical. The problem of parameter tuning is turned into a multi-objective optimization problem, and using radial basis function(RBF) neural network with particle swarm optimization algorithm(PSO) and bacterial foraging optimization algorithm(BFO) hybrid algorithm tuning method(NBPO) to fit the tuning rules. We introduce the control input, the overshoot and rise time of the system into the fitness function. The local and global optimal solutions are introduced into the chemotaxis and migration to improve the information exchange ability of BFO. The correctness and effectiveness of the NBPO tuning method were verified by numerical simulation results, compared with the one parameter tuning method(OPM) and PID, the results showed that the NBPO tuning method can greatly improve the control performance of the system.

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“…All of the researches are focus on the general tilt rotor [26], [27] . The tilt rotor aircraft has the problem of rotor and wing aerodynamic interference.…”

Section: Introductionmentioning

confidence: 99%

Research on Manipulation Strategy and Flight Test of the Quad Tilt Rotor in Conversion Process

Chen¹,

Wang²,

Wu³

et al. 2021

IEEE Access

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The quad tilt rotor (QTR) has the problem of manipulation redundancy due to the helicopter and fixed-wing manipulation mechanisms. Solving the problem of manipulation redundancy and develop an effective manipulation strategy is critical for QTR during conversion flight. A novel manipulation strategy based on states of QTR is proposed in this paper to solve the problem of manipulation redundancy in conversion mode. The longitude is controlled by the tilt angle of nacelle in helicopter mode and the course is controlled by the speed error of propellers in fixed-wing mode. The Active Disturbance Rejection Control (ADRC) is adopt into the flight control law, which can observe and compensate for internal and external disturbance. The simulation and flight test are carried out to verify the proposed manipulation strategy and the results show that the proposed manipulation strategy is effective and reasonable.

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Manipulation strategy of tilt quad rotor based on active disturbance rejection control

Cited by 27 publications

References 13 publications

A novel system identification algorithm for quad tilt-rotor based on neural network with foraging strategy

A novel system identification algorithm for quad tilt-rotor based on neural network with foraging strategy

Tuning of ADRC for QTR in Transition Process Based on NBPO Hybrid Algorithm

Research on Manipulation Strategy and Flight Test of the Quad Tilt Rotor in Conversion Process

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