Model-based predictive motion cueing strategy for vehicle driving simulators

Dagdelen, Mehmet; Reymond, Gilles; Kemeny, Andras; Marc, Bernard; Mäızi, Nadia

doi:10.1016/j.conengprac.2009.03.002

Cited by 96 publications

(44 citation statements)

References 22 publications

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“…The vehicle dynamics model is a proprietary software MADA, which is used internally for the numerical design of vehicle dynamics, and tuned for the present experiment to model a Renault Laguna2 sedan car. The motion cueing software is based on a model predictive control scheme [22]. The overall driving fidelity was validated by expert Renault drivers [19].…”

Section: B Ultimatementioning

confidence: 99%

Motion Scaling for High-Performance Driving Simulators

Berthoz

Bles²,

Bülthoff

et al. 2013

IEEE Trans. Human-Mach. Syst.

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Advanced driving simulators aim at rendering the motion of a vehicle with maximum fidelity, which requires increased mechanical travel, size, and cost of the system. Motion cueing algorithms reduce the motion envelope by taking advantage of limitations in human motion perception, and the most commonly employed method is just to scale down the physical motion. However, little is known on the effects of motion scaling on motion perception and on actual driving performance. This paper presents the results of a European collaborative project, which explored different motion scale factors in a slalom driving task. Three state-of-the-art simulator systems were used, which were capable of generating displacements of several meters. The results of four comparable driving experiments, which were obtained with a total of 65 participants, indicate a preference for motion scale factors below 1, within a wide range of acceptable values (0.4-0.75). Very reduced or absent motion cues significantly degrade driving performance. Applications of this research are discussed for the design of motion systems and cueing algorithms for driving simulation.

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Section: B Ultimatementioning

confidence: 99%

Motion Scaling for High-Performance Driving Simulators

Berthoz

Bles²,

Bülthoff

et al. 2013

IEEE Trans. Human-Mach. Syst.

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“…The MPC problem is solved by calculating the associated Quadratic Programming (QP) problem and solving using a commercially-available solver. The controller produces much smoother motion commands than that proposed by Dagdelen et al [7]. Some simulation results are presented that indicate that the MPC algorithm makes better use of the workspace than the classical algorithm.…”

Section: Figure 2 -Tilt Coordination Topologymentioning

confidence: 81%

“…The resulting controller matches the perceived accelerations until a platform limit is imminent, at which point the platform returns to centre; if the sign of the vehicle acceleration subsequently changes, perception matching resumes. An issue with MPC is the computational burden of doing an optimization at each time step; Dagdelen et al [7] reduce the online computation time using the invariant set method of Gutman and Cwikel [8]. Their initial tests indicated that the transition between full acceleration rendering and the washout phase was too abrupt, and an artificial smoothing of the transition was added.…”

Section: Figure 2 -Tilt Coordination Topologymentioning

confidence: 99%

“…So far, two publications have proposed MPC motion cueing algorithms. The first, by Dagdelen et al [7], is based on a modified perception tracking approach; over the prediction horizon H p , the optimal control sequence is chosen that minimises the motion perception error for the first two time steps and then for the remaining H p -2 steps returns to centre below the human perception threshold. The resulting controller matches the perceived accelerations until a platform limit is imminent, at which point the platform returns to centre; if the sign of the vehicle acceleration subsequently changes, perception matching resumes.…”

Section: Figure 2 -Tilt Coordination Topologymentioning

confidence: 99%

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Model predictive driving simulator motion cueing algorithm with actuator-based constraints

Garrett

Best

2013

Vehicle System Dynamics

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Citation: GARRETT, N.J.I. and BEST, M.C., 2013. Model predictive driving simulator motion cueing algorithm with actuator-based constraints. Vehicle System Dynamics, 51 (8) AbstractThe simulator motion cueing problem has been considered extensively in the literature; approaches based on linear filtering and optimal control have been presented and shown to perform reasonably well. More recently, Model Predictive Control (MPC) has been considered as a variant on the optimal control approach; MPC is perhaps an obvious candidate for motion cueing due to its ability to deal with constraints, in this case the platform workspace boundary.This paper presents an MPC-based cueing algorithm that, unlike other algorithms, uses the actuator positions and velocities as the constraints. The result is a cueing algorithm that can make better use of the platform workspace whilst ensuring that its bounds are never exceeded. The algorithm is shown to perform well against the classical cueing algorithm and an algorithm previously proposed by the authors in simulation and in tests with human drivers.

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“…The Model Predictive Control (MPC) paradigm appears to be the ideal candidate for the task, given its capabilities of explicitly dealing with constraints, which can include both the physical limitations of the actuators and the human perception system. The use of an appropriate model and cost function makes this approach an efficient, viable solution to cope with the design of MCAs [1], [2].…”

Section: Introductionmentioning

confidence: 99%

A non-linear MPC based Motion Cueing implementation for a 9 DOFs dynamic simulator platform

Bruschetta

Maran

Beghi

2014

53rd IEEE Conference on Decision and Control

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The use of dynamical driving simulators is nowadays common practice in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structure are designed, depending on the particular application and the corresponding targeted market. The effectiveness of such devices is related to their capabilities of well reproducing the driving feelings, hence it is crucial that the motion control strategies generate both realistic and feasible signals to the platform, to assure that it is kept within its limited operation space. Such strategies are called Motion Cueing Algorithms (MCAs), and they are clearly tailored to the particular mechanical structure of the device. In this paper we describe an MCA based on non linear Model Predictive Control (NMPC) techniques for a simulator of new conception, that consists of an hexapod over a flat base moved by a tripod, thus exhibiting highly non linear behaviour. The procedure is based on previous works where a linear, MPCbased MCA has been applied to a simpler device. The algorithm has been evaluated on a simulation environment, and a first implementation on the real device is in progress. Preliminary results show that a full exploitation of the working area is achieved, while managing at best all the limitations given by the particular structure and preserving the ease of tune and intuitiveness of the linear approach.

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Model-based predictive motion cueing strategy for vehicle driving simulators

Cited by 96 publications

References 22 publications

Motion Scaling for High-Performance Driving Simulators

Motion Scaling for High-Performance Driving Simulators

Model predictive driving simulator motion cueing algorithm with actuator-based constraints

A non-linear MPC based Motion Cueing implementation for a 9 DOFs dynamic simulator platform

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