Modeling and Control of a Tiltrotor UAV for Path Tracking

Donadel, Rodrigo; Raffo, Guilherme V.; Becker, Leandro Buss

doi:10.3182/20140824-6-za-1003.01735

Cited by 31 publications

(11 citation statements)

References 10 publications

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“…Finally, Cardoso et al (2016) presented a robust controller for this platform design and showed its pathtracking ability in a realistic simulation under external disturbances and model uncertainties. Donadel et al (2014) proposed a design where the tangential tiltings are fixed and the two radial tiltings are actuated, hence, again, n u = 2 + 2, with propellers' CoM also above the CoM of the platform as shown in Figure 2. They proposed a control approach relying on optimal H ' /H 2 techniques, which they validated in realistic simulations.…”

Section: Radial and Tangential Tilting Designsmentioning

confidence: 99%

Design of multirotor aerial vehicles: A taxonomy based on input allocation

Hamandi

Usai

Sablé

et al. 2021

The International Journal of Robotics Research

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This paper reviews the effect of multirotor aerial vehicle designs on their abilities in terms of tasks and system properties. We propose a general taxonomy to characterize and describe multirotor aerial vehicles and their designs, which we apply exhaustively on the vast literature available. Thanks to the systematic characterization of the designs, we exhibit groups of designs having the same abilities in terms of achievable tasks and system properties. In particular, we organize the literature review based on the number of atomic actuation units and we discuss global properties arising from their choice and spatial distribution in the mechanical designs. Finally, we provide a discussion on the common traits of the designs found in the literature and the main open and future problems.

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Section: Radial and Tangential Tilting Designsmentioning

confidence: 99%

Design of multirotor aerial vehicles: A taxonomy based on input allocation

Hamandi

Usai

Sablé

et al. 2021

The International Journal of Robotics Research

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“…Both algorithms were designed to control the UAV prototype shown in Figure 3 and both were compiled with the floating point operations being solved by software. They differ from each other on the control technique, one uses a common LQR strategy while the other uses a mix of H 2 e H∞ strategies with a feedforward control [11] to control a scenario where the UAV is carrying a load. An important characteristic is that both algorithms must be executed within a 12 millisenconds windows, otherwise the UAV may not stabilise.…”

Section: A Gem5 Simulation Platformmentioning

confidence: 99%

Performance Analysis of Embedded Control Algorithms used in UAVs

Silvestre¹,

Becker²

2020

Anais Estendidos Do X Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2020)

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Performance analysis of embedded systems is critical when dealing with Cyber-Physical Systems that require stability guarantees. They typically operate having to respect deadlines imposed during the design of the related control system. In a recent past performance analysis was typically done only by executing the code, and making measures, on the target embedded platform. Nowadays, code execution/measuring can also be done on simulation software, which offers greater degree of liberty for designers to configure the system for the desired tests. This paper presents results obtained from analyzing the performance of two control algorithms developed for controlling an Unmanned Aerial Vehicle (UAV) running on simulated and real embedded platforms. Such analysis is important for twofold reasons: better understand the timing behavior or the algorithms and evaluate architectural issues related with the target embedded platform. Raspberry Pi 3 Model B+ (with Cortex-A53 processor) is used as reference platform and serves as basis for creating different simulated versions for the analysis. Initial results highlighted the important role played by cache memory in the performance of the control algorithms and were able to detect a major bottleneck in one of the control algorithms that could compromise system stability.

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“…To perform the control design, Backstepping and Integral Backstepping control strategies are used for the stabilization of tilt-rotor UAVs in [7] and [8]. In [9], the authors modelled and controlled a tilt-rotor UAV for path tracking. The linearized error model is obtained from the nonlinear model, which is used to synthesize a H∞ and a multi-objective H2/H∞ controllers by using the LMI approach.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

et al. 2019

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This paper presents the dynamic modelling and control technique for a tilt-rotor aerial vehicle operating in bi-rotor mode. This kind of aircraft combines two flight envelopes, making it ideal for scenarios that require hovering, vertical take-off/landing and fixed-wing capabilities. In this work, a detailed mathematical model is derived using Newton-Euler formalism. Based on the obtained model, a new control scheme that incorporates six Proportional-Derivative (PD) controllers is proposed for the attitudes (roll (φ), pitch (θ), yaw (ψ)) and the positions (x, y, z) of the aircraft. Then, intelligent Particle Swarm Optimization (PSO) and conventional Reference Model (RM) techniques are applied for optimal tuning of the controllers' parameters. The stability analysis is developed using the Lyapunov approach and its application to the tilt-rotor system in the case of intelligent and conventional PD controllers. Numerical results of two scenarios prove the efficiency of the controllers tuned using the PSO method. Indeed, its ability to track the desired trajectories is demonstrated through 3D path tracking simulations, even in the presence of wind disturbances. Finally, experimental tests of stabilization and trajectory tracking are carried out on our prototype. These testing showed that our tilt-rotor was stable and suitably follows the imposed trajectories.

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Modeling and Control of a Tiltrotor UAV for Path Tracking

Cited by 31 publications

References 10 publications

Design of multirotor aerial vehicles: A taxonomy based on input allocation

Design of multirotor aerial vehicles: A taxonomy based on input allocation

Performance Analysis of Embedded Control Algorithms used in UAVs

Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

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