Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC

Manzoor, Tayyab; Xia, Yuanqing; Zhai, Di‐Hua; Ma, Dailiang

doi:10.1016/j.cja.2020.03.003

Cited by 46 publications

(13 citation statements)

References 40 publications

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“…Since MPC can handle the constraints of the landing mission, it is widely used in the trajectory of unmanned aerial vehicles (UAVs) [39,40], rockets [41,42] and robots [43,44]. Among them, [44] designed an MPC to solve the problem of point-to-point trajectory planning, thereby controlling the manipulator to catch a thrown tennis ball.…”

Section: Mpc Design For Autonomous Landing Of a Quadrotormentioning

confidence: 99%

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

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Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

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Section: Mpc Design For Autonomous Landing Of a Quadrotormentioning

confidence: 99%

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Guo

Tang²,

Wang

et al. 2022

Aerospace

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“…The tube-based MPC has better steady-state performance but a worse transient performance than the DOBMPC, and they have almost the same computational complexities. It is possible to achieve offset-free tracking in the case of constant or known dynamic disturbance [18][19][20][21][22][23][24]. In [24] an economic robust tube-based MPC for offset-free tracking of discrete-time linear systems was presented.…”

Section: Introductionmentioning

confidence: 99%

Robust MPC for tracking changing setpoints in discrete‐time non‐linear systems with non‐additive unknown disturbance

Zamani

Bolandi

2022

IET Control Theory & Appl

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This paper develops a novel observer-based robust tracking predictive controller for discrete-time nonlinear affine systems capable of dealing with changing setpoints and nonadditive non-slowly varying unknown disturbance with bounded variations. The existence of disturbance and/or sudden changes in a setpoint may lead to feasibility and stability issues in the stabilizing terminal constraint-based MPC. Since robust tracking MPCs usually consider additive disturbance, the recursive feasibility of these methods may be lost in the presence of non-additive non-slowly varying disturbance. The robust tracking MPC presented here extends the artificial reference-based MPC to deal with both changing setpoints and non-additive non-slowly varying disturbance. The key idea is the addition of tightened input and state constraints as new system constraints. The authors also guarantee the boundedness of disturbance observation error and closed-loop tracking error. In this method, the optimal tracking error converges asymptotically to the terminal region, and the perturbed system tracking error remains in a variable size tube around the optimal tracking error. It is shown that the proposed controller can achieve offset-free tracking in the presence of constant disturbance. The simulation results of the satellite attitude control system are provided to demonstrate the efficiency of the proposed predictive controller.

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“…To deal with model uncertainties, an MPC strategy based on an adaptive law was proposed for an unmanned helicopter with two degrees of freedom in [15]. In addition to the unmanned helicopter, there were also other kinds of UAV using MPC-based control method to achieve autonomous flight, such as VTOL UAV [16] and ducted fan aircraft [17].…”

Section: Introductionmentioning

confidence: 99%

Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

et al. 2021

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A helicopter is a highly nonlinear system. Its mathematical model is difficult to establish accurately, especially the complicated flapping dynamics. In addition, the forces and moments exerted on the fuselage are very vulnerable to external disturbances like wind gust when flying in the outdoor environment. This paper proposes a composite control scheme which consists of a nonlinear backstepping controller and an extended state observer (ESO) to handle the above problems. The stability of the closed-loop system can be guaranteed based on Lyapunov theory. The external disturbances and model nonlinearities are treated as a lumped disturbance. Meanwhile, the ESO is employed to compensate the influence by estimating the lumped disturbance in real-time. Numerical simulation results are presented to demonstrate that the algorithm can achieve accurate and agile attitude tracking under the external wind gust disturbances even with model uncertainties. When coming to the flight test, a block dropping device was designed to generate a quantifiable and replicable disturbance, and the experimental results indicate that the algorithm introduced above can reject the external disturbance rapidly and track the given attitude command precisely.

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Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC

Cited by 46 publications

References 40 publications

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Robust MPC for tracking changing setpoints in discrete‐time non‐linear systems with non‐additive unknown disturbance

Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

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