Chaos-Genetic Algorithm for the System Identification of a Small Unmanned Helicopter

Wang, Tianmiao; Yang, Chen; Liang, Jianhong; Wu, Yongliang; Wang, Chaolei; Zhang, Yicheng

doi:10.1007/s10846-012-9656-y

Cited by 12 publications

(12 citation statements)

References 17 publications

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“…During the experiment, all vehicle state variables were sampled at 50 Hz and recorded in a 4 GB flash memory on the flight control system. 27 To realize the accurate path following of UAV, even in the presence of wind disturbance, we assume that the altitude and the airspeed are maintained well. Figure 2 shows the flowchart of the proposed robust path following controller.…”

Section: System Descriptionmentioning

confidence: 99%

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

Yang

Liang

Wang³

et al. 2017

International Journal of Advanced Robotic Systems

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This article proposes a composite path following controller that allows the small fixed-wing unmanned aerial vehicle to follow a predefined path. Assuming that the vehicle is equipped with an autopilot for altitude and airspeed maintained well, the controller design adopts the hierarchical control structure. With the inner-loop controller design based on the notion of active disturbance rejection control which will respond to the desired roll angle command, the core part of the outerloop controller is designed based on Lyapunov stability theorem to generate the desired course rate for the straight-line paths. The bank to turn maneuver is used to transform the desired course rate to the desired roll angle command. Both the hardware-in-the-loop simulation in the X-Plane simulator and actual experimental flight tests have been successfully achieved, which verified the effectiveness of the proposed method.

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Section: System Descriptionmentioning

confidence: 99%

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

Yang

Liang

Wang³

et al. 2017

International Journal of Advanced Robotic Systems

Self Cite

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“…Hence, the helicopter dynamics can be separated into two interconnected subsystems, that is, the horizontal and vertical motions. In particular, the subsystems are given by (7), which were proposed in [23] and used successfully in [24] hor = hor hor + hor hor , ver = ver ver + ver ver ,…”

Section: Helicopter Dynamical Modelmentioning

confidence: 99%

Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

Ding

Yao

2015

International Journal of Aerospace Engineering

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The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

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“…6 Wang et al 15 proposed a chaos genetic algorithm (GA) to identify the linear helicopter model. 6 Wang et al 15 proposed a chaos genetic algorithm (GA) to identify the linear helicopter model.…”

Section: Introductionmentioning

confidence: 99%

“…At present, the least squares and maximum likelihood methods play an important role in the development of the whole identification theory, many scholars have studied these two methods, and many improved algorithms have appeared. 6 Wang et al 15 proposed a chaos genetic algorithm (GA) to identify the linear helicopter model. Alfi 16 applied a novel particle swarm optimization (PSO), to cope with the online system parameter identification problem.…”

Section: Introductionmentioning

confidence: 99%

Improved differential evolutionary algorithm for nonlinear identification of a novel vibration‐assisted swing cutting system

Wang

Zhao

et al. 2019

Adaptive Control & Signal

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Vibration-assisted swing cutting (VASC) is a new precision machining technology. VASC not only inherits the characteristics of EVC intermittent cutting but also alleviates the problem of EVC residual height. However, system identification is key if you want to achieve precise control. In order to solve this problem, a new improved differential evolutionary (IDE) algorithm is proposed to identify and optimize the Hammerstein-Wiener model parameter in VASC system. IDE algorithm is applied to transform the identification problem of the model into the optimization problem in the parameter space, and the optimal solution of the parameter of the model in the parameter space is obtained. Meanwhile, the IDE algorithm and the conventional five differential evolutionary algorithms perform performance comparison tests on six different test functions. The test results show that the IDE algorithm is strengthening the global search capability, accelerate the convergence rate to the global optimal solution, and indicate that the IDE algorithm can be effectively applied to the parameter optimization of Hammerstein-winner model. Based on the input and output data collected from the experiment, the accuracy of the identification model can be up to 98%, which prove the superiority of the proposed IDE algorithm for system identification. KEYWORDSHammerstein-Wiener model, improved differential evolutionary algorithm, system identification, test functions, vibration-assisted swing cutting 1066

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Chaos-Genetic Algorithm for the System Identification of a Small Unmanned Helicopter

Cited by 12 publications

References 17 publications

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

Combined of Lyapunov-stable and active disturbance rejection control for the path following of a small unmanned aerial vehicle

Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

Improved differential evolutionary algorithm for nonlinear identification of a novel vibration‐assisted swing cutting system

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