Characteristic model–based generalized predictive control and its application to the parafoil and payload system

Tan, Panlong; Sun, Qinglin; Chen, Zengqiang; Jiang, Yuxin

doi:10.1002/oca.2506

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Eq. (2) and (7) give the basic motion equations of the PPF, and it is sufficient to establish a six DOF dynamic model of the PPF without considering the relative motion between the payload and parafoil. However in eight DOF dynamic model, the constraints, including velocity constraint, angular rate constraint and force constraint should be considered and dealt with.…”

Section: Constraint Analysis and Dynamic Modeling Of The Ppfmentioning

confidence: 99%

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Dynamic Modeling and Experimental Verification of Powered Parafoil With Two Suspending Points

Tan

Sun

et al. 2020

IEEE Access

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This paper presents the dynamic modeling process of a powered parafoil (PPF). The PPF is composed of parafoil and payload equipped with a propeller. The payload is suspended to the parafoil via two suspending points, therefore the motion of the payload relative to the parafoil must be considered in the dynamic and control study. The proposed model of the PPF is derived from the Lagrangian equations and dynamic constraints with six degree of freedom (DOF) of the parafoil and two DOF of the payload. In the modeling process, the velocity, angular rate and force constraints are introduced and the detailed modeling process is provided. Time-domain response of the PPF under different conditions are calculated and the dynamic characteristics of the PPF are analyzed. A series of simulations are implemented to illustrate the manipulation characteristics. Furthermore, the dynamic model is validated by comparing the simulation results with the experimental data. INDEX TERMS Powered parafoil, constraint analysis, flexible-wing vehicle, dynamic modeling.

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Section: Constraint Analysis and Dynamic Modeling Of The Ppfmentioning

confidence: 99%

“…The use of parafoil has substantially enhanced airdrop capabilities during the last several decades. Many novel results about the application of the parafoil have been reported in literature [1]- [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Experimental Verification of Powered Parafoil With Two Suspending Points

Tan

Sun

et al. 2020

IEEE Access

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“…To correct directional errors, Prakash and Ananthkrishnan [19] designed a controller using the nonlinear dynamic inversion method and kept longitudinal and lateral loops open given the natural pendulum stability of the parafoil. Other traditional control methods were used for the parafoil [20], [21], [22], including proportional-integralderivative (PID) control with parameter optimization, model predictive control, etc. Gockel [23] discovered that the deviation from the planned path is mostly caused by the wind prediction error rather than the observation error and delay of the flight state within a certain range.…”

Section: Introductionmentioning

confidence: 99%

Dynamics Modeling and Autonomous Landing for Flexible Parafoil-Vehicle Multibody System

et al. 2023

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In this paper, the autonomous landing of the parafoil used for aerial vehicles is investigated through the numerical approach. The coupled parafoil-vehicle dynamics model is developed based on the multibody method. The transition process from aircraft flight to parafoil glide is designed in a global system framework. To deal with large flexible deformations during the deployment process, the parafoil canopy is approximated as interacting symmetric subparts using the geometric division method. The pseudo-spectral method is employed to generate a reference trajectory for the landing target. As the system dynamics are introduced into constraint conditions, the generated trajectory is beneficial to improve landing precision. In addition, other optimization objectives, including minimal energy consumption and obstacle avoidance, are considered in trajectory planning. The enhanced proportional integral control is designed to follow the reference trajectory. The neural network inversion model is used to reduce the coupling effect between the longitudinal and the directional control channels. There is a coupling effect that the two controls both are implemented through left and right trailing-edge deflections. An adaptive output allocation method is proposed to ensure that the directional control always is effective in particular when one side reaches saturation. Finally, simulations for autonomous landing under different conditions are carried out to validate the integrated system.INDEX TERMS Flexible parafoil model, multi-body system, trajectory optimization, flight control.

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“…Ward et al [11] designed a parafoil trajectory tracking controller on the basis of model prediction. Tan et al [12] proposed the GPC with characteristic model (CM) as a predictive model called the CM-based GPC and used it in the trajectory tracking control for parafoil systems. Sun et al [13] proposed a hybrid control approach for powered parafoil based on ADRC.…”

Section: Introductionmentioning

confidence: 99%

6-DOF Modeling and 3D Trajectory Tracking Control of a Powered Parafoil System

Zhao

Yao

et al. 2020

IEEE Access

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The powered parafoil system is obtained by adding the propeller thrust to the unpowered parafoil system, and has coupling and nonlinear characteristics, which make its precise control more difficult than that of the unpowered parafoil system. To achieve the trajectory tracking control of the powered parafoil system in the field of precision airdrop, a mathematical model of the 6-DOF of the powered parafoil system is established first. Then, a new trajectory tracking strategy is proposed, which can overcome the limitations of the traditional guidance-based trajectory tracking strategy. We design lateral, longitudinal, and velocity controllers on the basis of the motion characteristics of the powered parafoil system. Then, we adopt the widely used PID control strategy in engineering. In response to the difficult of the PID controller parameter tuning of the powered parafoil system, we achieve the PID controller parameter tuning with the ecosystem particle swarm optimization (ESPSO) of the swarm intelligence optimization algorithm. The effectiveness of the algorithm is verified by simulation experiments. Results show that the proposed algorithm can obtain high trajectory tracking accuracy even when a deviation in the initial state and a random gust wind disturbance in the outside world occur regardless of the method of the multiphase homing or the optimal control homing adopted. The proposed trajectory tracking strategy also has strong robustness and adaptability.

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Characteristic model–based generalized predictive control and its application to the parafoil and payload system

Cited by 5 publications

References 20 publications

Dynamic Modeling and Experimental Verification of Powered Parafoil With Two Suspending Points

Dynamic Modeling and Experimental Verification of Powered Parafoil With Two Suspending Points

Dynamics Modeling and Autonomous Landing for Flexible Parafoil-Vehicle Multibody System

6-DOF Modeling and 3D Trajectory Tracking Control of a Powered Parafoil System

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