Trajectory planning of quadrotor using sliding mode control with extended state observer

Xiao, Jun

doi:10.1177/0020294020927419

Cited by 17 publications

(10 citation statements)

References 24 publications

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“…A variation in the rotor's speed is used to design the vehicle's control system. The structural design of the Quadrotor is simple but since it is a multivariable system, the system has non-linear dynamics and is underactuated [8].…”

Section: Detailed Study Of Quadrotorsmentioning

confidence: 99%

Quadrotors in the Present Era: A Review

Thusoo¹,

Jain²,

Bangia³

2021

ITII

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The advancement in the field of aerial robotics and control engineering has created many opportunities for the utilization of Unmanned Aerial Vehicles (UAVs). Applications of UAVs from precision agriculture to delivering medicines and products at our doorsteps cannot be ignored. Quadrotors are the widely studied as sub-category of the rotor-type UAVs. Their ability to hover, vertical take-off and landing along with their small size and simple design make them suitable for many real-life applications like medicine delivery in containment zones struck with COVID-19. But under actuation caused due to four rotors to control six inputs creates instability in their flight. In this paper, Quadrotors and various Quadrotor applications are discussed. The various modeling and control techniques are discussed. Controlling techniques like LQR, LQG, PID and robust control is implemented for position, attitude and altitude control. Results for Proportional Integral and Derivative (PID) and Model Reference Adaptive Control (MRAC) of model generated using force-moment mathematical model are analyzed and compared using MATLAB Simulink. These control techniques are implemented for position, attitude and altitude control. In this paper, it has been concluded that MRAC performs better as compared to PID controller for position, attitude and Altitude control of Quadrotor.

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Section: Detailed Study Of Quadrotorsmentioning

confidence: 99%

Quadrotors in the Present Era: A Review

Thusoo¹,

Jain²,

Bangia³

2021

ITII

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“…In [6,7], optimization methods are first employed and then performance is enhanced iteratively through experimentation. In recent years, nonlinear controller designs [8][9][10][11] have also been employed to generate safe maneuvers. For safe path planning [12][13][14][15], on the other hand, cost functions are utilized to minimize the flight envelope.…”

Section: Introductionmentioning

confidence: 99%

Safe Motion Planning and Learning for Unmanned Aerial Systems

Perk

Tsourdos

2022

Aerospace

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To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems. Expert pilots, however, demonstrate maneuvers that are deemed at the edge of plane envelope. Inspired by biological systems, in this paper, we introduce a framework that leverages methods in the field of control theory and reinforcement learning to generate feasible, possibly aggressive, trajectories. For the control policies, Dynamic Movement Primitives (DMPs) imitate pilot-induced primitives, and DMPs are combined in parallel to generate trajectories to reach original or different goal points. The stability properties of DMPs and their overall systems are analyzed using contraction theory. For reinforcement learning, Policy Improvement with Path Integrals (PI2) was used for the maneuvers. The results in this paper show that PI2 updated policies are a feasible and parallel combination of different updated primitives transfer the learning in the contraction regions. Our proposed methodology can be used to imitate, reshape, and improve feasible, possibly aggressive, maneuvers. In addition, we can exploit trajectories generated by optimization methods, such as Model Predictive Control (MPC), and a library of maneuvers can be instantly generated. For application, 3-DOF (degrees of freedom) Helicopter and 2D-UAV (unmanned aerial vehicle) models are utilized to demonstrate the main results.

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“…Trajectory estimation plays a critical role from industrial appliances to research areas. It is widely used in numerous tasks like path planning, 1 navigation, 2 visual tracking 3 and simultaneous localization and mapping (SLAM). [4][5][6] Successful trajectory estimation depends on three key aspects: dynamic model, measurement model, and estimation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive trajectory estimation with power limited steering model under perturbation compensation

Yang

et al. 2022

Measurement and Control

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Trajectory estimation of maneuvering objects is applied in numerous tasks like navigation, path planning and visual tracking. Many previous works get impressive results in the strictly controlled condition with accurate prior statistics and dedicated dynamic model for certain object. But in challenging conditions without dedicated dynamic model and precise prior statistics, the performance of these methods significantly declines. To solve the problem, a stochastic nonlinear model called the power-limited steering model is proposed to describe the motion of non-cooperative object. It is a natural combination of instantaneous power and instantaneous angular velocity, relying on the nonlinearity to achieve the change of states. And the renormalization group is introduced to compensate the nonlinear effect of perturbation in our model. For robust and efficient trajectory estimation, an adaptive trajectory estimation (AdaTE) algorithm is proposed. By updating the statistics and truncation time online, it corrects the estimation error caused by biased prior statistics and observation drift, while reducing the computational complexity lower than O(n). The experiment of trajectory estimation demonstrates the convergence of AdaTE, and the better robust to the biased prior statistics and the observation drift compared with several typical estimation algorithms. Other experiments demonstrate through slight modification, our method can also be applied to local navigation in random obstacle environment, and trajectory optimization in visual tracking.

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Trajectory planning of quadrotor using sliding mode control with extended state observer

Cited by 17 publications

References 24 publications

Quadrotors in the Present Era: A Review

Quadrotors in the Present Era: A Review

Safe Motion Planning and Learning for Unmanned Aerial Systems

Adaptive trajectory estimation with power limited steering model under perturbation compensation

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