Grid and Polytopic LPV Modeling of Aeroelastic Aircraft for Co-design

Mocsányi, Réka Dóra; Takarics, Béla; Vanek, Bálint

doi:10.1016/j.ifacol.2020.12.1600

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…83 Then, polytopic LPV model representations can reduce the computational cost of grid-based LPV controller at the expense of more conservative results. 84 Thanks to this approach, the behavior of the differential equations ( 54), in its whole working range, can efficiently be approximated by a polytopic LPV model represented as a convex combination of a set of linear systems, representing the dynamics around specific working points, interpolated by measurable scheduling parameters. 32 With respect to the design control problem, for comparison purposes, we have implemented and contrasted grid and polytopic LPV based methods in Sect.…”

Section: Lpv Model Of the Longitudinal And Lateral Vehicle Dynamicsmentioning

confidence: 99%

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Combined longitudinal and lateral dynamics regulation of autonomous vehicles via induced ℒ2$$ {\mathcal{L}}_2 $$‐gain linear parameter varying control strategies based on parameter‐dependent Lyapunov functions

Gagliardi,

D'Angelo,

Casavola

2024

Intl J Robust & Nonlinear

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This article considers a path tracking control problem for autonomous vehicles constrained to maintain a safe distance from the next vehicle in the lane. The control design problem is solved by considering a mathematical model of the vehicle that jointly describes the lateral and longitudinal dynamics. Specifically, the optimal controller is designed aimed at minimizing the orientation and position tracking errors with respect to a given reference path. Moreover, the controller is able to ensure that the vehicle can both follow a speed profile and automatically adjusts the vehicle speed to maintain a proper distance from the next vehicle. The lateral dynamic is regulated by a 2‐DOF feedforward/feedback controller, where the feedforward law is implemented by exploiting an inverse kinematic model of the vehicle. On the other side, the feedback action, along with the action of the controller in charge to regulate the longitudinal dynamics, is the optimal gain‐scheduling state‐feedback controller. In particular, a parameter‐dependent quadratic Lyapunov function approach is used to reduce the conservativeness of the solution and a suitable convex LMI optimization problem is formulated for the synthesis. The vehicle is described by a seven‐degrees‐of‐freedom (7DOF) full‐car model, with the tire‐generated forces described by the classical Pacejka's Magic Formula. In order to assess the performance of the proposed control strategy, simulations have been undertaken in Matlab/Simulink by considering an autonomous vehicle driving into an urban scenario.

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Section: Lpv Model Of the Longitudinal And Lateral Vehicle Dynamicsmentioning

confidence: 99%

“…On the other hand, solving LMIs for too dense grids can lead to numerical issues and computational cost 83 . Then, polytopic LPV model representations can reduce the computational cost of grid‐based LPV controller at the expense of more conservative results 84 …”

Section: Longitudinal and Lateral Control Designmentioning

confidence: 99%