A real time implementation of MPC based Motion Cueing strategy for driving simulators

Beghi, Alessandro; Bruschetta, Mattia; Maran, Fabio

doi:10.1109/cdc.2012.6426119

Cited by 36 publications

(21 citation statements)

References 8 publications

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“…The basis of the proposed MPC-based MCA can be found in [4], [5]. The main features of the MPC paradigm are well known: A constrained, optimal control problem over a prediction window is solved at each time step, the first element of the computed solution is applied and then the procedure is iterated, so that the effect of model uncertainties and disturbances can be counteracted.…”

Section: Non-linear Mpc For Motion Cueingmentioning

confidence: 99%

“…This constraint has to be taken care of in the problem formulation. Following the approach in [4], [5], this is achieved by augmenting the state of the otolithic system to include the effect of the platform inclinations, obtaining the "augmented" systems ΣŌ. Σ S and ΣŌ are then combined to get the the complete vestibular model, having the actual vehicle accelerations and rotational velocities as inputs, and the corresponding perceived quantities as outputs.…”

Section: Non-linear Mpc For Motion Cueingmentioning

confidence: 99%

“…Model 1) Linear vestibular model: One of the distinctive elements of the adopted approach is the modelling of the human vestibular system, i.e. the dynamics of the set of organs that are responsible for the perception of linear acceleration and angular velocities [3], [4], [5]. Each perceptive degree of freedom is represented by a linear, continuous-time, second order system.…”

Section: Non-linear Mpc For Motion Cueingmentioning

confidence: 99%

“…In [3], [4], [5] a MCA based on a linear MPC technique has been proposed. In that particular setup the system can be split into four sub-systems (due to partial decoupling of DOFs) to "parallelize" the computation of the optimal solution, thus improving real-time performance.…”

Section: Introductionmentioning

confidence: 99%

“…It is composed by an hexapodal structure installed upon a tripod-actuated plane, able to perform longitudinal, lateral, and yaw displacements, hence improving the platform capabilities along these DOFs, that are particularly relevant for racing applications. A Nonlinear MPC (NMPC) based MCA is proposed that can be considered a substantial evolution of the algorithm described in [4], [5] to manage the coupling between different DOFs, that are mostly due to:…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Non-linear Mpc For Motion Cueingmentioning

confidence: 99%

Section: Non-linear Mpc For Motion Cueingmentioning

confidence: 99%

Section: Non-linear Mpc For Motion Cueingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Assisted and autonomous driving on driving simulators

Bruschetta

Minen

2018

Proceedings

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Perception-Based Motion Cueing: A Cybernetics Approach to Motion Simulation

Pretto

Venrooij

Nesti

et al. 2015

Trends in Augmentation of Human Performance

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The goal of vehicle motion simulation is the realistic reproduction of the perception a human observer would have inside the moving vehicle by providing realistic motion cues inside a motion simulator. Motion cueing algorithms play a central role in this process by converting the desired vehicle motion into simulator input commands with maximal perceptual fidelity, while remaining within the limited workspace of the motion simulator. By understanding how the one’s own body motion through the environment is transduced into neural information by the visual, vestibular and somatosensory systems and how this information is processed in order to create a whole percept of self-motion we can qualify the perceptual fidelity of the simulation. In this chapter, we address how a deep understanding of the functional principles underlying self-motion perception can be exploited to develop new motion cueing algorithms and, in turn, how motion simulation can increase our understanding of the brain’s perceptual processes. We propose a perception-based motion cueing algorithm that relies on knowledge about human self-motion perception and uses it to calculate the vehicle motion percept, i.e. how the motion of a vehicle is perceived by a human observer. The calculation is possible through the use of a self-motion perception model, which simulate the brain’s motion perception processes. The goal of the perception-based algorithm is then to reproduce the simulator motion that minimizes the difference between the vehicle’s desired percept and the actual simulator percept, i.e. the “perceptual error”. Finally, we describe the first experimental validation of the new motion cueing algorithm and shown that an improvement in the current standards of motion cueing is possible

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A real time implementation of MPC based Motion Cueing strategy for driving simulators

Cited by 36 publications

References 8 publications

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

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

Assisted and autonomous driving on driving simulators

Perception-Based Motion Cueing: A Cybernetics Approach to Motion Simulation

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