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Model Predictive Control based motion cueing algorithm for Driving Simulator

Journal of Systems Science and Systems Engineering 2024年第5期33卷 607-626页

作者： Ayesha Hameed Ali Soltani Sharif Abadi Andrzej Ordys Institute of Automatic Control and Robotics Warsaw University of TechnologyWarsawPoland Institute of Electronic Systems Warsaw University of TechnologyWarsawPoland

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver’s behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. motion cueing algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver’s perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive Control (MPC).

关键词： Driving simulator motion cueing algorithm control theory model predictive control PID

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A Neural Network-Based motion cueing algorithm Using the Classical Washout Filter for Comprehensive Driving Scenarios

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 5112-5121页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Zakarya, Muhammad Lim, Chee Peng Liew, Alan Wee-Chung Karkoub, Mansour Nahavandi, Saeid Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia Griffith Univ Inst Integrated & Intelligent Syst Brisbane Qld 4222 Australia Lamar Univ Mech Engn Dept Beaumont TX 77705 USA Swinburne Univ Technol Def Innovat Hawthorn Vic 3122 Australia Harvard Univ Harvard Paulson Sch Engn & Appl Sci Boston MA 02134 USA

The motion cueing algorithm (MCA) enables lifelike motion in simulators resembling real driving. Regenerated motions must adhere to workspace constraints. Vehicle motion signals (linear acceleration, angular velocity) are generated in a simulated vehicle environment utilised in MCA for motion cues. These signals are categorised into levels (slow, medium, fast) based on frequency and amplitude. The commonly used MCA, the classical washout filter, is typically fine-tuned using worst-case (fast-driving) scenarios to meet the simulator's requirements across various situations. However, this approach reduces the MCA's effectiveness in handling slower driving scenarios, resulting in conservatism in platform workspace usage for slow and medium driving. Consequently, a noticeable motion sensation error arises between real vehicle drivers and motion simulator users. To rectify this issue, a novel neural network-based MCA is developed in this study. Three distinct classical washout filters are meticulously tuned to cater to slow, medium, and fast driving scenarios. These filters generate precise motion cues for simulator users at corresponding levels of driving scenarios. The neural network-based MCA is constructed using the synthesised signals from these classical washout filters. This proposed method is thoroughly validated through the utilisation of MATLAB software. In direct comparison with the standard classical washout filter, the proposed MCA significantly reduces the motion sensation error, enriches motion fidelity, and optimises the utilisation of the simulator's workspace.

关键词： motion cueing algorithm classical washout filter neural network multilayer perceptron optimisation methods parallel processing

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A Safety-Oriented motion cueing algorithm for a Serial Robotic Flight Simulator Using a Predictive Neural Network Reference Governor

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15718-15731页

作者： Matheus, Aline da C. de Oliveira, Wesley R. Villani, Emilia Embraer BR-12227901 Sao Jose Dos Campos Brazil Aeronaut Inst Technol ITA Competence Ctr Mfg CCM Div Mech Engn IEM BR-12228900 Sao Jose Dos Campos Brazil

High fidelity flight simulators use motion platforms to reproduce the feeling of motion from a real flight. While most of the published works for both aircraft and vehicle simulators are related to parallel motion platforms, this work approaches the problem of designing the motion cueing algorithm of a flight simulator based on a serial manipulator. The simulator presents a large cockpit with an embedded visual system and dimensions that resemble those of an aircraft flight deck. motion cueing in this context should be able to minimize false cues while ensuring safe operation, coping not only with the dynamic and kinematic constraints of the robot but also avoiding crash events that might happen between the cockpit and the serial arm. While there have been several contributions regarding classical filtering, tuning optimization, and model-based predictive control approaches to cope with constraints of parallel platforms, they result in the inefficient utilization of the robot workspace or even the inability to handle collisions of the cockpit with the robot. This work presents a novel motion cueing algorithm for a serial robotic flight simulator, which focuses on ensuring safety regarding the physical boundaries of the cockpit while enhancing motion fidelity. The approach is based on a hybrid model-based predictor that uses a neural network to infer workspace collisions in real-time (including crash events of the cockpit with the robotic arm), releasing a non-linear deterministic control action that acts as a feedforward reference governor. Simulation and experimental results evince improved workspace usage while ensuring safe operation.

关键词： Robots Collision avoidance Prediction algorithms Manipulators Robot sensing systems Filtering algorithms Arms motion cueing algorithm serial robotic flight simulator neural networks collision-avoidance

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A Deep Reinforcement Learning Based motion cueing algorithm for Vehicle Driving Simulation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第7期73卷 9696-9705页

作者： Scheidel, Hendrik Asadi, Houshyar Bellmann, Tobias Seefried, Andreas Mohamed, Shady Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia German Aerosp Ctr Inst Syst Dynam & Control D-82234 Wessling Germany

motion cueing algorithms (MCA) are used to control the movement of motion simulation platforms (MSP) to reproduce the motion perception of a real vehicle driver as accurately as possible without exceeding the limits of the workspace of the MSP. Existing approaches either produce non-optimal results due to filtering, linearization, or simplifications, or the computational time required exceeds the real-time requirements of a closed-loop application. This work presents a new solution to the motion cueing problem, where instead of a human designer specifying the principles of the MCA, an artificial intelligence (AI) learns the optimal motion by trial and error in interaction with the MSP. To achieve this, a well-established deep reinforcement learning (RL) algorithm is applied, where an agent interacts with an environment, allowing him to directly control a simulated MSP to obtain feedback on its performance. The RL algorithm used is proximal policy optimization (PPO), where the value function and the policy corresponding to the control strategy are both learned and mapped in artificial neural networks (ANN). This approach is implemented in Python and the functionality is demonstrated by the practical example of pre-recorded lateral maneuvers. The subsequent validation shows that the RL algorithm is able to learn the control strategy and improve the quality of the immersion compared to an established method. Thereby, the perceived motion signals determined by a model of the vestibular system are more accurately reproduced, and the resources of the MSP are used more economically.

关键词： Trajectory Training Prediction algorithms Artificial neural networks Real-time systems Training data Filtering motion cueing algorithm deep reinforcement learning proximal policy optimization machine learning artificial neural network motion simulator

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An Analytic Policy Gradient-Based Deep Reinforcement Learning motion cueing algorithm for Driving Simulators

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IEEE ACCESS 2025年 13卷 81507-81523页

作者： Huang, Xiaowei Shi, Xuhua Wang, Peiyao Xu, Hongzan Tang, Xiaojun Zhang, Gaoran Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zeekr Automobile Ningbo Hangzhou Bay New Zone Co L Ningbo 315800 Peoples R China

The proposed motion cueing algorithm (MCA), based on a reinforcement learning algorithm using gradient information to directly update the control policy, introduces three significant enhancements. First, transform the complex simulator environment into a differentiable simulator environment that provides gradient information at each time step and use this gradient information to directly update the control policy. Second, the network architecture is reconfigured into a concurrent controller format, similar to Model Predictive Control (MPC). This controller processes a sequence of vehicle motion reference signals over a future period, utilizing a multi-layer perceptron to generate the simulator's motion reference control signal sequences for the same duration. Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. As the prediction horizon increases, the algorithm demonstrates superior computational efficiency, which helps reduce the incidence of motion sickness during the use of the driving simulator. Third, a loss function specifically designed for the motion simulator is proposed. This function incorporates constraints derived from the MPC framework to address workspace limitations and applies them to workspace management. These constraints restrict the platform's acceleration and speed near the workspace boundaries, allowing for better utilization of the available space. The algorithm is validated using Carla's autonomous driving simulation software as the dataset generator. During the training process, the proposed algorithm in this paper achieves an order-of-magnitude improvement in convergence speed compared to conventional training methods of PPO and DDPG. Simulations with a 10-step prediction horizon indicate that the Root Mean Square Error (RMSE) produced by this algorithm is comparable to that of the MCA based on MPC (MPC-MC

关键词： Prediction algorithms Software algorithms Cost function Training Trajectory Predictive models motion sickness Mathematical models Filters Deep reinforcement learning motion cueing algorithm driving simulator analytic policy gradient (APG) differentiable simulator deep reinforcement learning

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An Optimal Nonlinear Model Predictive Control-Based motion cueing algorithm Using Cascade Optimization and Human Interaction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9191-9202页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Chen, Yutao Abdar, Moloud Karkoub, Mansour Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Fuzhou Univ Coll Elect Engn & Automat Qishan Campus Fuzhou 350108 Peoples R China Lamar Univ Mech Engn Dept Beaumont TX 77705 USA Swinburne Univ Technol Hawthorn Vic 3122 Australia Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.

关键词： Solid modeling Tuning Optimization Actuators Mathematical models Kinematics Predictive models Cascade optimization human interaction motion cueing algorithm nonlinear model predictive control weight tuning

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A Novel Decoupled Model Predictive Control-Based motion cueing algorithm for Driving Simulators

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第6期72卷 7024-7034页

作者： Asadi, Houshyar Bellmann, Tobias Qazani, Mohammadreza Chalak Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia German Aerosp Ctr Inst Syst Dynam & Control Deutsch Zentrum Luft & Raumfahrt Space Syst Dynam D-82234 Oberpfaffenhofen Germany

The motion cueing algorithm (MCA) is used to reproduce the driving/flying motion sensation of the real land or air vehicles for the users of motion simulator. Highly accurate MCAs are required for the motion simulators to create realistic sensation for the simulator users, otherwise, they might cause motion sickness and user discomfort. Model predictive control (MPC) is a popular technique in development of the high-fidelity MCAs as it is able to consider the motion sensation as well as the workspace constraints of the simulator platform. Tilt coordination in MCA is in charge of creating sustained linear acceleration feeling through tilting the platform cabin and exploiting gravitational acceleration under human motion threshold, to not allow the user to perceive this as a rotational motion. However, the existing MPC-based MCAs have not considered the rate limit to constrain the tilt motion under human threshold which causes violations of the rotational sensation threshold during generating sustained acceleration feeling. Instead, some researchers increase the penalty weights of rotational motions in order to slow down rotational motions. Using this strategy slows down all the simulator rotational motions not only the ones coming from tilt coordination, to generate sustained acceleration feeling, but also from rotational channel (due to real vehicle rotation) which causes motion sensation error and consequently motion sickness. Therefore, as the existing MPC-based MCAs cannot differentiate the rotations of tilt coordination channel (due to sustained acceleration feeling generation), from rotational channel (due to vehicle rotational motions), they are not able to produce accurate rotational motion sensations because of insufficient rotational motions. In this research, a novel decoupled MPC-based MCA is developed to systematically address the issues related to the existing MPC-based MCAs, in terms of inaccurate motion generation, by redesigning the MPC-based MCA usi

关键词： motion simulator platform motion cueing algorithm human vestibular model model predictive control

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Adaptive motion cueing algorithm Using Optimized Fuzzy Control System for motion Simulators

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第1期8卷 390-403页

作者： Asadi, Houshyar Bellmann, Tobias Mohamed, Shady Lim, Chee Peng Khosravi, Abbas Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Campus Waurn Ponds Vic 3216 Australia Deutsehes Zentrum Luft & Raumfahrt DLR Inst Syst Dynam & Control Space Syst Dynam D-82234 Cologne Germany

The motion cueing algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car;the platform physical constraints;the motion threshold limiter effects;and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.

关键词： Filtering algorithms Low-pass filters Fuzzy logic Irrigation Tuning Mathematical models Genetic algorithms motion cueing algorithm fuzzy logic washout filter genetic algorithm motion simulator

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A Time-Varying Weight MPC-Based motion cueing algorithm for motion Simulation Platform

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11767-11778页

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Mohamed, Shady Lim, Chee Peng Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia

The motion cueing algorithm (MCA) is playing the most critical role in motion simulation platform (MSP) to reproduce the realistic motion sensation of the real car for the MSP's users while taking into cogitation of the physical boundaries of the platform. Recently, the model predictive control (MPC) is employed for designing MCAs which led to the formation of MPC-based MCAs. The purpose of the MPC-based MCA is to recalculate the optimal values of the input signals with consideration of the MSP's physical restrictions. All the current MPC-based MCAs have fix weights that can cause the conservative and inefficient utilization of the MSP's workspace limitations as they have been tuned based on the worst-case scenarios to keep the platforms within their physical limitations. Then, the error of motion sensation between the real car and MSP users increases due to the conservative utilization of the MSP's workspace boundaries. The main objective of this study is to provide more efficient workspace utilization to minimise the error of motion feeling between the real car and MSP users while respecting the workspace boundaries. A procedure according to the optimised fuzzy logic-based units is employed to calculate the appropriate MPC weights online while considering the sensed specific force error, sensed angular velocity error and the current motion status of the MSP including linear position, linear velocity and angular position of the cockpit. The proposed MPC-based MCA is designed and developed using MATLAB. The outcomes show a better motion feeling compared with the current MPC-based MCAs.

关键词： motion cueing algorithm time-varying weight model predictive control fuzzy logic multi-objective genetic algorithm hexapod platform

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Computationally-efficient motion cueing algorithm via Model Predictive Control

Computationally-efficient Motion Cueing Algorithm via Model ...

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IEEE International Conference on Mechatronics (ICM)

作者： Chadha, Akhil Jain, Vishrut Lazcano, Andrea Michelle Rios Shyrokau, Barys Delft Univ Technol Dept Cognit Robot Delft Netherlands Toyota Motor Europe Zaventem Belgium

ISBN: (纸本)9781665466615

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.

关键词： motion cueing algorithm driving simulator model predictive control

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