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Parameter Auto-tuning for Improving Scale Factor and Washout Effect of Classical motion cueing algorithm with Cylindrical Coordinates

Parameter Auto-tuning for Improving Scale Factor and Washout...

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AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering, RCTEMME 2021

作者： Pham, Duc An Huu, Quang Nguyen School of Mechanical Engineering Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9789811919671

Driving simulators are developing for a more realistic driving experience for drivers in a virtual environment. A motion cueing algorithm (MCA) plays a vital role in recreating drivers’ motion cues on the real car and preserves the driving motion platform within its physical boundaries. The comfortable motion perception for simulation cases with a specific input signal, however, depends on the parameters of the MCA, often tuned with experts in the loop by trial and error. This manual tuning technique is so time-consuming that the paper improves a novel generic optimization method, called mean-variance mapping optimization, to look for the suitable parameters general MCAs with novel penalty function of scale factor and washout effect. In the work, the novel auto-tuning method was first used to find the parameters of classical MCA with cylindrical coordinates. Then, the comparison of the simulated quantities of three test cases with different parameter sets achieved from trial and error, auto-tuning methods with old and novel penalty function was implemented to investigate the advantage of the new auto-tuned parameter set. As a result, the classical MCAs responses with new auto-tuned parameters eliminate the false cues of angular velocity while improving the washout effect and product higher scale factor. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Auto-tuning parameter Driving simulator motion cueing algorithm MVMO

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Novel model predictive control-based motion cueing algorithm for compensating centrifugal acceleration in KUKA robocoaster-based driving simulators

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SCIENCE PROGRESS 2023年第4期106卷 368504231204759页

作者： Pham, Duc-An Pham, Trung Nghia Nguyen, Duc-Toan Hanoi Univ Sci & Technol Sch Mech Engn Hanoi City Vietnam

The washout motion cueing algorithm (MCA) is a critical element in driving simulators, designed to faithfully reproduce precise motion cues while minimizing false cues during simulation processes, particularly deceptive translational and rotational cues. To enhance motion sensation accuracy and optimize the use of available workspace, model predictive control (MPC) has been employed to develop innovative motion cueing algorithms. While most MCAs have been tailored for the Steward motion platform, there has been a recent adoption of the motion platform based on KUKA Robocoaster as an economical option for driving simulators. However, leveraging the full potential of the KUKA Robocoaster requires trajectory conversion of the motion base. Thus, this research proposes a novel MCA specifically designed for the KUKA Robocoaster-based motion platform, utilizing large planar circular motion to simulate lateral movement for drivers. Nonetheless, circular motion introduces disruptive centrifugal forces, which can be mitigated through proper pitch-tilted angles. The novel MPC generates simulated motion that accurately follows the lateral specific force target and effectively maintains the roll angular velocity below its threshold value. Additionally, it compensates for disturbing centrifugal acceleration by implementing pitch rotational motion, ensuring the pitch angular velocity remains below its threshold. Simulation tasks conducted on the motion platform, focusing solely on lateral acceleration, demonstrate the successful elimination of false motion cues in both the roll/sway and pitch/surge channels. The proposed innovative MPC solution offers an original approach to motion cueing algorithms in KUKA Robocoaster-based driving simulators. It enables the exploitation of the KUKA Robocoaster platform's capabilities while delivering accurate and immersive motion cues to drivers during simulation experiences.

关键词： motion cueing algorithm model predictive control KUKA robocoaster CARLA driving simulator

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A novel motion cueing algorithm integrated multi-sensory system-Vestibular and proprioceptive system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS 2020年第2期234卷 256-271页

作者： Pham Duc-An Nguyen Duc-Toan Hanoi Univ Sci & Technol Sch Mech Engn 1A Dai Co Viet St Hanoi Vietnam

motion cueing algorithms are used to produce a motion which feels as realistic as possible while remaining in the limited workspace of driving simulators. Several optimal motion cueing algorithms were developed to improve both the exploitation of the workspace of a driving simulator and the realistic of the simulated motion. In the dynamics model of the optimal motion cueing algorithms, several kinds of motion-sensory systems are integrated to optimize the simulated motion sensation. However, most previous works have just focused on the visual and vestibular system. The mathematical model of the proprioceptive system, that also senses the non-visual motion, has rarely been concerned. In this paper, a novel optimal motion cueing algorithm, which integrates model of the proprioceptive system, is developed to reduce the false cues from muscle spindle of head/neck system sensing lateral tilted angle. The optimal motion cueing algorithm has a significant effect on the pilot's perception when the tilted angle is rather large. An example of the simulation of a roller coaster running along a planar S-curve trajectory with only lateral acceleration is investigated with current motion cueing algorithms and optimal motion cueing algorithm. Several objective criteria were introduced to evaluate the simulated perception of all investigated motion cueing algorithms. The results demonstrate that optimal motion cueing algorithm is better than current motion cueing algorithms in most criteria and also sub-criteria.

关键词： motion cueing algorithm KUKA Robocoaster proprioceptive system multi-sensory system optimal algorithm

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A New Fuzzy Logic Based Adaptive motion cueing algorithm Using Parallel Simulation-Based motion Platform

A New Fuzzy Logic Based Adaptive Motion Cueing Algorithm Usi...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Bellmann, Tobias Perdrammehr, Siamak Mohamed, Shady Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia German Aerosp Ctr Inst Syst Dynam & Control Space Syst Dynam Deutsch Zentrum Luft & Raumfahrt DLR D-82234 Wessling Germany

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169323

Parallel manipulators are recently used in most motion simulation laboratories as they can easily generate six degrees of freedom motion. Recently, fuzzy logic-based adaptive motion cueing algorithms (MCAs) have been employed to reproduce the motion signals. The usage of fuzzy logic-based adaptive MCA reduces the movement sensation error between the real vehicle and the simulation-based motion platform (SBMP) user considering the end-effector limitations in Cartesian space. . In this paper, a new fuzzy logic-based adaptive MCA is introduced to generate motion signals based on the joints' limitations and the movement sensation error between the real vehicle and the SBMP user. Considering the parallel SBMP, joint limits enhance the ability of the introduced adaptive motion cueing algorithm to generate more accurate movement feelings with high fidelity. The simulation results prove that the proposed adaptive motion cueing algorithm can effectively use the large workspace of the parallel SBMP whilst reducing the motion sensation error.

关键词： motion cueing algorithm adaptive controller fuzzy logic motion platform path planning

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Optimising Control and Prediction Horizons of a Model Predictive Control-Based motion cueing algorithm Using Butterfly Optimization algorithm

Optimising Control and Prediction Horizons of a Model Predic...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Qazani, Mohammad Reza Chalak Jalali, Seyed Mohammad Jafar Asadi, Houshyar Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia

ISBN: (纸本)9781728169293

The motion cueing algorithm (MCA) oversees regenerating the motion feeling of the real vehicle for the simulation-based motion platform (SBMP) within the physical limitations. Model Predictive Control (MPC) is recently employed as an MCA, which is called MPC-based MCA due to the consideration of the plant's boundaries in finding the optimal input signal. The computational load of the MPC directly relates to the control horizon and prediction horizon of the MPC. In this paper, a new optimisation method using butterfly optimisation algorithm is developed to find the optimal control horizon and prediction horizon of MPC-based MCA. The proposed method reduces the time of the tuning process of the MPC-based MCA, which is usually carried out via trial-and-error and genetic algorithm methods. Also, the trial-and-error method increases the motion sensation error and insufficient usage of the SBMP. The model is validated using MATLAB simulation environment, and the outcomes show that the developed butterfly optimisation algorithm will lead better motion sensation with less wrong motion signals and low computational burden compared with the trial-and-error and genetic algorithm method.

关键词： motion cueing algorithm model predictive control MPC horizons meta-heuristic algorithm butterfly optimisation algorithm genetic algorithm

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A Model Predictive Control-based motion cueing algorithm using an Optimized Nonlinear Scaling for Driving Simulators

A Model Predictive Control-based Motion Cueing Algorithm usi...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Asadi, Houshyar Mohammadi, Arash Mohamed, Shady Qazani, Mohammad Reza Chalak Lim, Chee Peng Khosravi, Abbas Nahavandi, Saeid Deakin Univ IISRI Geelong Vic Australia

ISBN: (纸本)9781728145693

Driving motion simulators are widely used for their reliable, safe and cost-effective abilities to replicate real vehicle driving experience for simulator drivers in virtual environment. As all motion simulators have physical limitations, motion cueing algorithm (MCA) is the most necessary algorithm for transformation of the real vehicle's linear and rotational motions to motion platform aiming to regenerate realistic driving sensation. Model Predictive Control (MPC)-based MCA has recently become one of the most popular MCAs. Scaling and limiting is an important unit of MPC-based MCA to reduce the amplitude of motion signal uniformly aiming to improve the realism of produced motion within the physical limitations of workspace. The current implementations of MPC use a basic form of scaling. In this paper, a novel MPC-based MCA is developed using an optimised nonlinear scaling unit and Genetic algorithm (GA). The goal is to reproduce accurate motion sensation for the motion simulator drivers as close as possible to real vehicle within the platform's physical constraints. This is achieved via a polynomial scaling unit which is optimized by GA. The aim is to overcome the disadvantages associated with the tuning based on trial-and-error for MPC-based MCA scaling unit which is the main cause of inefficient platform workspace usage and motion sensation error between real vehicle driver and motion simulator driver. The proposed optimization-based method enhances the function of the nonlinear scaling units by considering some important factors such as the motion simulator's physical constraints and motion sensation error between the drivers in a real vehicle and a motion simulator platform. The proposed method is verified via simulation results which show the superiority of the optimised nonlinear scaling compared with the current trial-and-error based scaling method for MPC-based MCA as it is able to reduce the sensation error between the motion simulator and real vehicle d

关键词： motion cueing algorithm Model Predictive Control motion Sensation motion simulator Genetic algorithm

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Optimizing Model Predictive Control horizons using Genetic algorithm for motion cueing algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 92卷 73-81页

作者： Mohammadi, Arash Asadi, Houshyar Mohamed, Shady Nelson, Kyle Nahavandi, Saeid Deakin Univ IISRI Geelong Vic Australia

Driving simulators are effective tools for producing the feeling of driving a real car through generation of a similar environment and motion cues. The main problem of motion simulators is their limited workspace which does not allow them to produce the exact motions of a real vehicle, hence they need a motion cueing algorithm (MCA). A high-fidelity motion simulator can be used for vehicle prototyping and testing as well as driver/pilot training to enhance transportation safety. Using motion simulators with the capability of replacing realistic motions for these purposes is less risky for drivers and more time and cost-effective. Due to workspace limitations, washout filters have been designed to bring motion simulators back to a neutral position;however, the problem of violation of platform constraints is still an issue. Recently Model Predictive Control (MPC) has become popular in driving simulators. The primary advantage of this control method is respecting constraints and consideration of future dynamics. The horizon windows of future control and prediction affect the computational burden and the output performance. As these horizons are chosen manually by the designer, they are sub-optimal and in some cases too wide or narrow. In this paper, a novel method based on Genetic algorithm (GA) is employed to achieve the best control and prediction horizons considering minimization of several terms such as sensation error, displacement and the computational burden. This new method is proposed to eliminate the MPC-MCA drawbacks such as time-consuming empirical guessing by iterative trial-and-error for the initial control and prediction horizons as selecting the initial control and prediction horizons based on trial-and-error can lead to large sensation error, low motion fidelity, inefficient platform usage as well as the computational burden. Therefore, this method provides a new framework for tuning not only the MPC-MCA optimally but also all the MPC-based application

关键词： motion cueing algorithm Model Predictive Control Genetic algorithm Optimization

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A genetic algorithm-based nonlinear scaling method for optimal motion cueing algorithm in driving simulator

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2018年第8期232卷 1025-1038页

作者： Asadi, Houshyar Lim, Chee Peng Mohammadi, Arash Mohamed, Shady Nahavandi, Saeid Shanmugam, Lakshmanan Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia

A motion cueing algorithm plays an important role in generating motion cues in driving simulators. The motion cueing algorithm is used to transform the linear acceleration and angular velocity of a vehicle into the translational and rotational motions of a simulator within its physical limitation through washout filters. Indeed, scaling and limiting should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the motion cueing algorithm. This is to decrease the effects of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation. A nonlinear scaling method based on the genetic algorithm for the motion cueing algorithm is developed in this study. The aim is to accurately produce motions with a high degree of fidelity and use the platform more efficiently without violating its physical limitations. To successfully achieve this aim, a third-order polynomial scaling method based on the genetic algorithm is formulated, tuned, and implemented for the linear quadratic regulator-based optimal motion cueing algorithm. A number of factors, which include the sensation error between the real and simulator drivers, the simulator's physical limitations, and the sensation signal shape-following criteria, are considered in optimizing the proposed nonlinear scaling method. The results show that the proposed method not only is able to overcome problems pertaining to selecting nonlinear scaling parameters based on trial-and-error and inefficient usage of the platform workspace, but also to reduce the sensation error between the simulator and real drivers, while satisfying the constraints imposed by the platform boundaries.

关键词： motion cueing algorithm nonlinear scaling human sensation washout filter genetic algorithm

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motion cueing algorithm for high performance driving simulator using yaw table

Motion cueing algorithm for high performance driving simulat...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Fang, Z. Tsushima, M. Kitahara, E. Machida, N. Wautier, D. Kemeny, A. Nissan Motor Co Ltd CAE & Testing Div 1 NTC 560-2 Atsugi Kanagawa 2430192 Japan Renault Virtual Real & Immers Simulat Ctr TCR AVA 0 131 Ave Golf F-78288 Guyancourt France

In the last decade, MPC based motion cueing algorithm (MCA) has been successfully applied in driving simulator. Generally, in the conventional MPC based MCA, the multi-axes motion cueing problem is dealt with separately using 2DOF (linear + tilt motions) optimization technique. In the current study, a multi-axes coupled 4DOF optimization algorithm is proposed which completes the lack of efficient motion cueing algorithm for X-Y based driving simulator using yaw table. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： motion control motion cueing algorithm driving simulator real-time control

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Complexity reduction in motion cueing algorithm for the ULTIMATE driving simulator

Complexity reduction in motion cueing algorithm for the ULTI...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Munir, Sarmad Hovd, Morten Fang, Zhou Olaru, Sorin Kemeny, Andras Norwegian Univ Sci & Technol Engn Cybernet Dept N-7491 Trondheim Norway Virtual Real & Immers Simulat Ctr RENAULT Paris France Supelec Automat Control Dept 3 Rue Joliot Curie F-91192 Gif Sur Yvette France

The performance of a driving simulator depends on the efficiency of the embedded motion cueing algorithm. An explicit model predictive control was established recently as a pertinent design framework for the motion cueing algorithm. The complexity of the explicit solution increases manifold when the human vestibular model is considered. The present paper focuses on the complexity reduction of explicit solution using low complexity contractive sets for the motion cueing algorithm. The low-complexity explicit controller is formulated for the efficient control of the motion cueing system for the ULTIMATE driving simulator available in Renault. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： motion cueing algorithm Explicit model predictive control Complexity reduction Driving simulator Contractive set low complexity contractive sets

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