Complexity reduction in motion cueing algorithm for the ULTIMATE driving simulator

Munir, Sarmad; Hovd, Morten; Fang, Zhou; Olaru, Sorin; Kemeny, Andras

doi:10.1016/j.ifacol.2017.08.2256

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…This method significantly reduces online computation time [11]. A 2 DoF MCA was developed which was later extended by incorporating a vestibular model in [12]. Although this technique reduces online computation time, it suffers from memory storage issues along with restrictions in using large prediction horizons N p with fast sampling rates.…”

Section: Introductionmentioning

confidence: 99%

Computationally-efficient Motion Cueing Algorithm via Model Predictive Control

Chadha

Jain

Lazcano

et al. 2023

2023 IEEE International Conference on Mechatronics (ICM)

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Driving simulators have been used in the automotive industry for many years because of their ability to perform tests in a safe, reproducible and controlled immersive virtual environment. The improved performance of the simulator and its ability to recreate in-vehicle experience for the user is established through motion cueing algorithms (MCA). Such algorithms have constantly been developed with model predictive control (MPC) acting as the main control technique. Currently, available MPCbased methods either compute the optimal controller online or derive an explicit control law offline. These approaches limit the applicability of the MCA for real-time applications due to online computational costs and/or offline memory storage issues. This research presents a solution to deal with issues of offline and online solving through a hybrid approach. For this, an explicit MPC is used to generate a look-up table to provide an initial guess as a warm-start for the implicit MPC-based MCA. From the simulations, it is observed that the presented hybrid approach is able to reduce online computation load by shifting it offline using the explicit controller. Further, the algorithm demonstrates a good tracking performance with a significant reduction of computation time in a complex driving scenario using an emulator environment of a driving simulator.

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Section: Introductionmentioning

confidence: 99%

Computationally-efficient Motion Cueing Algorithm via Model Predictive Control

Chadha

Jain

Lazcano

et al. 2023

2023 IEEE International Conference on Mechatronics (ICM)

View full text Add to dashboard Cite

show abstract

“…The real-time control of such platform which comes with important safety constrained leads to computationally involving schemes. Some improvements have been proposed in term of complexity reduction [12] and recently explore alternative schemes such as neural network solver implementation [13]. However, aside the constraint handling and the computational limitations, the inherent delays are particularly difficult to handle in the control design with direct implications in the motion sickness phenomena.…”

Section: Introductionmentioning

confidence: 99%

MPC delay compensation based on maximal controllable sets for real-time driving simulators

Soyer

Olaru

Fang

2021

2021 25th International Conference on System Theory, Control and Computing (ICSTCC)

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“…Moreover, filter properties leads to backlash effect [2] the first time in 2004 [3]. MPC-MCA provides a framework that takes the bounds of the workspace and the limitations of actuators into account in the computation of control actions [4], [5], [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

A novel Motion Cueing Algorithm based on real-time optimization and periodic invariant sets

Soyer

Olaru

Fang

2020

2020 IEEE Conference on Control Technology and Applications (CCTA)

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This paper deals with control design of Motion Cueing Algorithms for driving simulation. The development of driving-assistance systems and the gradual move towards autonomous driving led automobile manufacturers to focus on high performance driving simulation in order to validate novel functionalities and driving confort before production. Driving simulators are currently constrained environments because of the workspace size and the actuators resistance. As part of the software operating the platform, a control block has to manage the position of a cabin by guaranteeing realistic acceleration feelings to a driver. In this purpose, the controller is usually design within Model Predictive Control (MPC) framework. However large prediction horizons and constrained tracking problems implies heavy computational burden. In this paper, a novel MPC-based motion cueing algorithm is proposed considering periodic invariant sets as a key concept to decrease the complexity of the real-time optimization.

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Complexity reduction in motion cueing algorithm for the ULTIMATE driving simulator

Cited by 5 publications

References 8 publications

Computationally-efficient Motion Cueing Algorithm via Model Predictive Control

Computationally-efficient Motion Cueing Algorithm via Model Predictive Control

MPC delay compensation based on maximal controllable sets for real-time driving simulators

A novel Motion Cueing Algorithm based on real-time optimization and periodic invariant sets

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