Design of Fuzzy Controller for Hexacopter Position Control

Bačík, Ján; Fedor, Pavol

doi:10.1007/978-3-319-18476-0_20

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…It is compatible with the majority of the MAVs hardware, where its implementation is made accessible publicly in [21] to assure reproducible research. Model-free control approaches for MAVs have commenced with FLS [24], NN [25], or NF-based controllers [26]. However, the requirement of offline training, static structure, dependency on experts' knowledge confine their applicability in complex systems with the uncertain environment.…”

Section: Main Featuresmentioning

confidence: 99%

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PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Ferdaus

Pratama

Anavatti

et al. 2020

Information Sciences

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There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.

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Section: Main Featuresmentioning

confidence: 99%

“…Model-free control approaches for MAVs have commenced with FLS [24], NN [25], or NF-based controllers [26]. However, the requirement of offline training, static structure, dependency on experts' knowledge confine their applicability in complex systems with the uncertain environment.…”

Section: Related Workmentioning

confidence: 99%

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Ferdaus

Pratama

Anavatti

et al. 2020

Information Sciences

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“…Therefore, it is widely used in to various UAVs [19]- [21], except for the case of so-called chattering phenomenon. Most of the existing research work has been focused on system modeling and attitude control algorithms for multi-rotor UAVs [22]- [28], The influence of misoperations and disturbances on the flight safety of UAVs has not been well investigated. Automatic attitude controllers with capabilities of misoperation prevention and disturbance rejection are much in demand to ensure flight safety for UAVs at the near-ground flight.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

et al. 2019

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Unmanned aerial vehicles (UAVs) at low altitude flight may significantly degrade their performance and the safety under wind disturbances and incorrect operations. This paper presents a robust control strategy for UAVs to achieve good performance of low altitude flight and disturbance rejection. First, a novel second-order hexacopter dynamics is established and the position tracking is translated to the altitude and the rotational angle tracking problem. An integrated control scheme is created to deal with the challenges faced by hexacopter at low altitude flight, in which the influence of near-ground threshold distance and the desired roll, pitch, and yaw are analyzed. Moreover, an improved flying altitude planner and an attitude planner for low altitude conditions are designed respectively to avoid the overturning risk due to the big reaction torque and external disturbances. Second, a sliding-mode-based altitude tracking controller and an attitude tracking controller are designed to reduce the tracking errors and improve the robustness of the system. Finally, the proposed control scheme is tested on simulation and experiment platforms of multi-rotor UAV to show the feasibility and accurate trajectory tracking at low altitude flight. INDEX TERMS Modeling, tracking control, low altitude, hexacopter, robustness. YUQING CHEN received the master's and Ph.D. degrees in control theory and control engineering from the

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“…The fuzzy logic control is suitable for systems, where some control system parameters are unknown [10]. This control technique is used nowadays in many applications [11] including electrical drives and power converters [12], [13]- [16] automation technique [17], mobile robotics [18], etc. The downside of the fuzzy logic controller is high demand on computational power, which is not a problem with modern DSPs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Control of Soft-Switching DC-DC Converter

Leso¹,

Zilkova²,

Pastor³

et al. 2016

ElAEE

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The paper presents a novel control technique for a phase-shifted PWM (PS-PWM) soft-switching dc-dc converter. The converter is a full bridge dc-dc converter with controlled output rectifier and an active snubber on the secondary side. To solve the problems with nonlinear nature of the dc-dc converter a fuzzy logic control is used. The fuzzy logic controller design and simulation is presented. The performance of the designed fuzzy logic controller is compared to classical PI controller.

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Design of Fuzzy Controller for Hexacopter Position Control

Cited by 9 publications

References 4 publications

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Fuzzy Logic Control of Soft-Switching DC-DC Converter

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