Planar Inequality Constraints for Stable, Collision-free Model Predictive Control of a Quadcopter

Jardine, Peter T.; Givigi, Sidney N.; Yousefi, Shahram

doi:10.1016/j.ifacol.2017.08.1702

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Similarly in [13], the collision cost function requires the position of all agents. The MPC approach [12] utilizes a planar inequality constraint tangent to an obstacle's surface.…”

Section: Introductionmentioning

confidence: 99%

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

Dubay

Pan

2023

Asian Journal of Control

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A system of fast moving quadcopters has a high risk of collisions with neighboring quadcopters or obstacles. The objective of this work is to develop a control strategy for collision and obstacle avoidance of multiple quadcopters. In this paper, the problem of distributed dynamic matrix control (DMC) for collision avoidance among a team of multiple quadcopters attempting to reach consensus in the horizontal plane and yaw direction ( , and ) is investigated. Violations of a predetermined safety radius generates output constraints on the DMC optimization function, which has not been dealt with in the literature. Different from past works, the proposed strategy can perform collision avoidance in the , , and ‐directions. In addition, logarithmic barrier functions are implemented as input rate constraints on the control actions. Extensive simulation studies for a team of quadcopters illustrate promising results of the proposed control strategy and case variations. In addition, DMC parameter effects on the system performance are studied, and a successful study for obstacle avoidance is presented.

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“…Similarly in [13], the collision cost function requires the position of all agents. The MPC approach [12] utilizes a planar inequality constraint tangent to an obstacle's surface.…”

Section: Introductionmentioning

confidence: 99%

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

Dubay

Pan

2023

Asian Journal of Control

View full text Add to dashboard Cite

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Model predictive control of a collaborative manipulator considering dynamic obstacles

Krämer

Rösmann

Hoffmann

et al. 2020

Optim Control Appl Methods

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SummaryCollaborative robots have to adapt its motion plan to a dynamic environment and variation of task constraints. Currently, they detect collisions and interrupt or postpone their motion plan to prevent harm to humans or objects. The more advanced strategy proposed in this article uses online trajectory optimization to anticipate potential collisions, task variations, and to adapt the motion plan accordingly. The online trajectory planner pursues a model predictive control approach to account for dynamic motion objectives and constraints during task execution. The prediction model relates reference joint velocities to actual joint positions as an approximation of built‐in robot tracking controllers. The optimal control problem is solved with direct collocation based on a hypergraph structure, which represents the nonlinear program and allows to efficiently adapt to structural changes in the optimization problem caused by moving obstacles. To demonstrate the effectiveness of the approach, the robot imitates pick‐and‐place tasks while avoiding self‐collisions, semistatic, and dynamic obstacles, including a person. The analysis of the approach concerns computation time, constraint violations, and smoothness. It shows that after model identification, order reduction, and validation on the real robot, parallel integrators with compensation for input delays exhibit the best compromise between accuracy and computational complexity. The model predictive controller can successfully approach a moving target configuration without prior knowledge of the reference motion. The results show that pure hard constraints are not sufficient and lead to nonsmooth controls. In combination with soft constraints, which evaluate the proximity of obstacles, smooth and safe trajectories are planned.

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Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design

Jardine

Givigi

Yousefi

2021

IEEE Systems Journal

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Planar Inequality Constraints for Stable, Collision-free Model Predictive Control of a Quadcopter

Cited by 3 publications

References 20 publications

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

Model predictive control of a collaborative manipulator considering dynamic obstacles

Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design

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