Sliding Mode Control for Hexacopter Autopilot design

Abrougui, Helmi; Nejim, Samir; Charrada, Anis; Dallagi, Habib

doi:10.1109/ic_aset53395.2022.9765926

Cited by 4 publications

(8 citation statements)

References 17 publications

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“…We assumed that the sailboat depicted in Fig. 1 has a rigid sail 1 and moves at a fixed heading φ and speed v.…”

Section: Sails Controllermentioning

confidence: 99%

“…We based our approach on the work of Melin et al [3]. Equation (18) shows the force F s acting on the sail, 1 Rigid sails maintain their shape regardless of the wind.…”

Section: Sails Controllermentioning

confidence: 99%

“…Sailboats have shown great potential for long-term navigation and marine monitoring applications where they cannot touch land for extended periods, making the energy efficiency of their different systems essential. However, designing an ANS for sailboats is challenging due to complex sailboat dynamics and the variability of wind and waves [1,2]. Several authors have suggested controllers for ANS that require deep knowledge of sailboat dynamics.…”

mentioning

confidence: 99%

“…Several authors have suggested controllers for ANS that require deep knowledge of sailboat dynamics. Abrougui et al [1] designed an automatic control system B Nelson Santiago Giraldo…”

mentioning

confidence: 99%

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Sailboat navigation control system based on spiking neural networks

Giraldo,

Isaza,

Velásquez

2023

Control Theory Technol.

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In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally, we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.

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“…We assumed that the sailboat depicted in Fig. 1 has a rigid sail 1 and moves at a fixed heading φ and speed v.…”

Section: Sails Controllermentioning

confidence: 99%

“…We based our approach on the work of Melin et al [3]. Equation (18) shows the force F s acting on the sail, 1 Rigid sails maintain their shape regardless of the wind.…”

Section: Sails Controllermentioning

confidence: 99%

mentioning

confidence: 99%

“…Several authors have suggested controllers for ANS that require deep knowledge of sailboat dynamics. Abrougui et al [1] designed an automatic control system B Nelson Santiago Giraldo…”

mentioning

confidence: 99%

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Sailboat navigation control system based on spiking neural networks

Giraldo,

Isaza,

Velásquez

2023

Control Theory Technol.

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“…Comparisons were made with other control methods to indicate the superiority of the proposed control scheme and control strategy. Abrougui et al (2022) discussed an autopilot design for behavior control and modeling of the hexarotor UAV. The designed autopilot consists of low-level control for attitude stabilization control and high-level control for trajectory tracking control.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

Köse

Oktay²

2023

AEAT

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Purpose The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing). Design/methodology/approach In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data. Findings With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set. Research limitations/implications It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network. Practical implications Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies. Social implications Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability. Originality/value The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure.

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