Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances

Liu, Yuyi; Rajappa, Sujit; Montenbruck, Jan Maximilian; Stegagno, Paolo; Bülthoff, Heinrich H.; Allgöwer, Frank; Zell, Andreas

doi:10.1016/j.robot.2017.08.011

Cited by 39 publications

(24 citation statements)

References 13 publications

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“…In this way, a non-convex optimization problem needs to be solved at every sampling time instant in a receding horizon fashion. Another approach to obstacle avoidance is described in [11] where a high-level path planner generates collisionfree trajectories which are followed by an MPC controller.…”

Section: A Background and Motivationmentioning

confidence: 99%

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

Small

Sopasakis

Fresk

et al. 2019

2019 18th European Control Conference (ECC)

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We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC problem at a rate of 20 Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant.

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Section: A Background and Motivationmentioning

confidence: 99%

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

Small

Sopasakis

Fresk

et al. 2019

2019 18th European Control Conference (ECC)

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“…Therefore, t reach of FO switching control law can be obtained from (t) = ∫ Next, a more realistic model is built, which includes the wind field. At first, the wind field can fall into two types, one is fixed wind field, such as static wind, and the other is random wind field, such as random wind, and so on [25,26]. The two kinds of wind fields are denoted as…”

Section: ) If the Initial Condition Is Smentioning

confidence: 99%

“…Next, a more realistic model is built, which includes the wind field. At first, the wind field can fall into two types, one is fixed wind field, such as static wind, and the other is random wind field, such as random wind, and so on . The two kinds of wind fields are denoted as

d_{1} = {false[d_{x} d_{y} d_{z} false]}^{T}, d_{2} = {false[d_{ϕ} d_{θ} d_{ψ} false]}^{T}

.…”

Section: Controllers Design Strategy and Stability Analysismentioning

confidence: 99%

Design of Fractional‐Order Backstepping Sliding Mode Control for Quadrotor UAV

Shi

Cheng

Yin

et al. 2018

Asian Journal of Control

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This paper derives a nonlinear translational and rotational dynamic model of the quadrotor UAV. An original fractional-order backstepping sliding mode control method for a typical quadrotor UAV is presented. The new approach can decrease the chattering phenomenon and increase the robustness of control strategy. The strongly coupled characteristics of the under-actuated system are sought by the virtual controllers. The feasibility for different track trajectories are verified using MATLAB. The new control method can not only track the expected trajectory very well but also has a better robustness for different complex trajectory under disturbances, as the simulation results demonstrate.

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“…The multi-agent obstacle avoidance problem has gained a lot of attention in recent years. Single agent obstacle avoidance, motion planning and control is well studied [9], [10]. However, the multi-agent obstacle avoidance problem is more complex due to motion planning dependencies between different agents, and the poor computational scalability associated with the nonlinear nature of these dependencies.…”

Section: State-of-the-artmentioning

confidence: 99%

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju

Rajappa

Black

et al. 2018

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these nonlinear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. Video of simulation studies: https://youtu.be/umkdm82Tt0M arXiv:1805.09633v1 [cs.RO]

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Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances

Cited by 39 publications

References 13 publications

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

Design of Fractional‐Order Backstepping Sliding Mode Control for Quadrotor UAV

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

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