Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Abstract

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.

Vehicle motion simulators, a key step towards road vehicle dynamics improvement

“…[7,29] To address these deficiencies and regenerate more realistic motion cues, researchers have modified or altered the classical motion cueing method. For example, adaptive strategy with tunable filter parameters that minimise the cost function associated with vehicle and simulator states, [79,80] optimal method with the model of a human vestibular system that minimises human perception error between real and simulator driver, [81,82] and a Model Predictive Control algorithm with predictions of the future trajectory that make better use of the available workspace [22,83] are some modifications of the classical motion cueing algorithm in this respect.…”

A Motion Cueing Algorithm Based on Model Predictive Control Using Terminal Conditions in Urban Driving Scenario

“…Recently, different types of adaptive MCAs using the fuzzy logic control [13][14][15] are developed while considering the human vestibular system aiming to deliver the realistic motion feeling [16][17][18]. The linear quadratic regulator technique by considering of human vestibular to decrease the human feeling error is employed in forming of optimal MCA [19][20][21]. Asadi et al [22] proposed a hybrid MCA with combination of classical and optimal MCAs to regenerate the compensation motion signal based the human motion sensation between real vehicle and SBMP user.…”

Incorporating Human Perception With the Motion Washout Filter Using Fuzzy Logic Control

“…This causes inflexibility of the structure and makes the resulting simulator insufficient to suit all circumstances. The classical washout filter is tuned by worst case situation tuning methodology, which is the main cause of the poor usage of the available motion platform [21]. In addition, this algorithm does not take human perception into account and it reproduces and replicates the movement, but not the sensation of driving in the overall procedure.…”