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

Wang, Zhigang; Zu, Rui; Duan, Dengyan; Li, Jianbo

doi:10.1109/access.2019.2957318

Cited by 23 publications

(10 citation statements)

References 33 publications

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“…The dynamic and kinematics equation [29] are shown in equation 4and equation (5). Where, m is the weight of aircraft, I is the inertia matrix, u, v, w is the velocity, φ, θ, ψ is the euler Angle, p, q, r is the angular velocity.…”

Section: B Flight Dynamics Model Of Quad Tilt Rotormentioning

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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Section: B Flight Dynamics Model Of Quad Tilt Rotormentioning

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

Self Cite

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“…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. ADRC consists of three parts: tracking differentiator (TD), extended state observer (ESO) and nonlinear state error feedback law (NLSEF).…”

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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“…The disturbance can be eliminated by designing a control law. ADRC has been widely used in many industrial fields, such as aircraft control [13], motor control [14], ship control [15], and vehicle control [16]. ADRC has also been applied for LFC.…”

Section: Introductionmentioning

confidence: 99%

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

et al. 2021

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Load frequency control (LFC) is necessary to guarantee the safe operation of power systems. Aiming at the frequency and power stability problems caused by load disturbances in interconnected power systems, active disturbance rejection control (ADRC) was designed. There are eight parameters that need to be adjusted for an ADRC, which are challenging to adjust manually, thus limiting the development of this approach in industrial applications. Regardless of the theory or application, there is still no unified and efficient parameter optimization method. The traditional particle swarm optimization (PSO) algorithm suffers from premature convergence and a high computational cost. Therefore, in this paper, we utilize an improved PSO algorithm, a reinforcement-learning-based memetic particle swarm optimization (RLMPSO), for the parameter tuning of ADRC to obtain better control performance for the controlled system. Finally, to highlight the advantages of the proposed RLMPSO-ADRC method and to prove its superiority, the results were compared with other control algorithms in both a traditional non-reheat two-area thermal power system and a non-linear power system with a governor dead band (GDB) and a generation rate constraint (GRC). Moreover, the robustness of the proposed method was tested by simulations with parameter perturbations and different working conditions. The simulation results showed that the proposed method can meet the demand for the frequency deviation to stabilize to 0 in LFC with higher performance, and it is worthy of popularization and application.

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

Cited by 23 publications

References 33 publications

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

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

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

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

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