Quadcopter formation flight control combining MPC and robust feedback linearization

Zhao, Wansheng; Go, Tiauw Hiong

doi:10.1016/j.jfranklin.2013.10.021

Cited by 107 publications

(63 citation statements)

References 19 publications

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“…Therefore, formation control of multiple vehicles has attracted considerable attention for study [1][2][3]. As a typical unmanned aerial vehicle (UAV), a quadrotor has simple mechanical and flexible maneuverability.…”

Section: Introductionmentioning

confidence: 99%

Event-Based Formation Control of Multiple Quadrotors on SO(3)

Dong

Mingyu

Wang

et al. 2018

Mathematical Problems in Engineering

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This paper is concerned with the formation problem of multiple quadrotors, and an event-based control strategy is proposed. The communication topology and relative positions of formation are first considered, and then the model of multiple quadrotors system is developed on the special orthogonal group SO(3). By designing the trigger function, certain events are generated for each quadrotor. Then, the formation controller is driven to update its parameters according to the events. The attitude controller on SO(3) is designed for tracking of the command and stabilization. By the proposed method continuous communication is not required between quadrotors, and it is proved that the quadrotors could achieve the desired formation. Simulation illustrates that the proposed event-based formation control method is effective.

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Section: Introductionmentioning

confidence: 99%

Event-Based Formation Control of Multiple Quadrotors on SO(3)

Dong

Mingyu

Wang

et al. 2018

Mathematical Problems in Engineering

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“…Linear model. Used for simple maneuvers [3] or for the calculation of the control algorithm by complex methods of high computational cost (LQR, model predictive control) [4], [ 5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…By the geometric center of the construction we understand the point of intersection of center lines of the arm. These models allow implementing the regulator by on-line methods [1], [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Quadrocopters control most commonly uses the following: PD/PID -regulators [3], [6], [7], [11], [12], LQR [4], [5], model predictive control [6], [9], backstepping control [7], [8], sliding mode control [7] and inverse control [5], [7], [13]. In this paper, the methods are chosen to control the synthesis of linearquadratic regulator (LQR method), and the method of inverse dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Control Methods Design for a Model of Asymmetrical Quadrocopter

Beniak¹,

Gudzenko²

2016

JAMRIS

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“…[6] also guarantees a weaker form of recursive feasibility than [4] and what we guarantee in this work. In [7] the authors approximate the non-linear dynamics of a quadrotor by linearizing it around hover and apply the RMPC of [4] to the linearized dynamics. This differs significantly from our approach, where we formulate the RMPC on the feedback linearized dynamics directly, and not on the dynamics obtained via Jacobian linearization of the non-linear system.…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive control for non-linear systems with input and state constraints via feedback linearization

Pant

Abbas

Mangharam

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance. Abstract-Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance.

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Quadcopter formation flight control combining MPC and robust feedback linearization

Cited by 107 publications

References 19 publications

Event-Based Formation Control of Multiple Quadrotors on SO(3)

Event-Based Formation Control of Multiple Quadrotors on SO(3)

Control Methods Design for a Model of Asymmetrical Quadrocopter

Robust model predictive control for non-linear systems with input and state constraints via feedback linearization

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