Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Cohen, Mitchell; Abdulrahim, Khairi; Forbes, James Richard

doi:10.1109/lra.2020.3010214

Cited by 49 publications

(27 citation statements)

References 24 publications

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“…The trajectory-tracking controller implemented is based on the finite-horizon LQR controller presented in [24]. The trajectory to be tracked is the teach pass estimated pose trajectory πt , represented at any time-step k as an element of a matrix Lie group Xt…”

Section: Lqr Controllermentioning

confidence: 99%

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Ultra-Wideband Teach and Repeat

Ayman¹,

Cossette²,

Ny³

et al. 2022

Preprint

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Autonomously retracing a manually-taught path is desirable for many applications, and Teach and Repeat (T&R) algorithms present an approach that is suitable for long-range autonomy. In this paper, ultra-wideband (UWB) ranging-based T&R is proposed for vehicles with limited resources. By fixing single UWB transceivers at far-apart unknown locations in an indoor environment, a robot with 3 UWB transceivers builds a locally consistent map during the teach pass by fusing the range measurements under a custom ranging protocol with an on-board IMU and height measurements. The robot then uses information from the teach pass to retrace the same trajectory autonomously. The proposed ranging protocol and T&R algorithm are validated in simulation, where it is shown that the robot can successfully retrace the taught trajectory with sub-metre tracking error.

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Section: Lqr Controllermentioning

confidence: 99%

“…and the command inputs are computed similarly to [24]. Even though the estimated teach trajectory drifts from the true trajectory, the anchors are localized based on the drifted trajectory.…”

Section: Lqr Controllermentioning

confidence: 99%

Ultra-Wideband Teach and Repeat

Ayman¹,

Cossette²,

Ny³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The LQR is a highly effective method to design an optimal full-state-feedback controller for linear, or linearized systems [78]. The correction and design of the LQR needs to find the appropriate state variables and control quantity weighting matrix according to the response curve, without determining the closed-loop pole position according to the required performance [79].…”

Section: Optimal Control (1)mentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

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“…There are several control techniques for the attitude and altitude of the quadcopter such as proportional-integralderivative (PID) control [1,2], adaptive control [3][4][5], neural network [6,7], LQR control [8,9], model predictive control [10,11], have been investigated in many studies. Comparison with other approaches, sliding mode control (SMC) [12,13] is exploited as a special and robust control algorithm against parametric uncertainties, external disturbances through its sliding surface.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

et al. 2021

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In this paper, a sliding mode control based on neural networks is proposed for attitude and altitude system of quadcopter under external disturbances. First, the dynamic model of the quadcopter is considered under external disturbances. Sliding mode controllers are then integrated with neural network algorithm to achieve the time-varying sliding surface; their coefficients in sliding surface are adjusted through backpropagation law. The disturbance observer is also combined with sliding mode controllers to estimate and handle the external disturbances. Finally, the Lyapunov theory is applied to validate the stability of suggested control method. The performance of proposed sliding mode control has been evaluated using a numerical simulation. The results show that the attitude and altitude controller based on suggested algorithm has a better tracking performance and disturbance rejection.

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Finite-Horizon LQR Control of Quadrotors on $SE_2(3)$

Cited by 49 publications

References 24 publications

Ultra-Wideband Teach and Repeat

Ultra-Wideband Teach and Repeat

AUV Trajectory Tracking Models and Control Strategies: A Review

Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

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