Model Predictive Control based Motion Cueing Algorithm for Driving Simulator

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).

A Novel Nonlinear Scaling Method for Optimal Motion Cueing Algorithm in Flight Simulator

Motion cueing algorithm plays a key role in simulator motion reproduction and improves the realism of movement sensation by combining with the human vestibular system.It is well established that scaling&limiting should be used to decrease the amplitude of the acceleration and angular velocity signals for making full use of limited workspace of motion platform.A novel nonlinear scaling method based on a third-order polynomial and back propagation(BP)neural networks for the motion cueing algorithm is proposed in this paper.The third-order polynomial method is applied to the low amplitude segment of the input signal to minimize the trigger delay of the sensation acceleration;in the high amplitude segment,the BP neural network is used to adaptively adjust the scaling factor of the input signal,to avoid washout displacement and angular displacement beyond the boundary of the workspace.The simulation experiment is verified in the longitudinal/pitch direction for flight simulator,and the result implies that the proposed method not only can overcome the problem of constant scaling parameter and improve motion platform workspace utilization,but also reduce the false cues during the motion simulation process.

Optimal motion cueing algorithm for accelerating phase of manned spacecraft in human centrifuge

This paper presents a novel optimal Motion Cueing Algorithm(MCA)to control the rotations of a Human Centrifuge(HC)and achieve the best simulation of a Space Craft(SC)motion.Relations of the specific forces sensed by astronauts of the SC and the HC have been derived and linearized.A Linear Quadratic Regulator(LQR)controller is implemented for the problem which tends to minimize the error between the two sensed specific forces as well as control input in a cost function.It results in control inputs of the HC to generate its sensed specific force as close as possible to the one in the SC.The algorithm is implemented for both linearized and nonlinear portions of a US space shuttle mission trajectory as a verification using MATLAB.In longitudinal direction,the proposed MCA,works well when the acceleration is less than 2 g in which the tracking error does not exceed 12%.In lateral direction the tracking is much better even in nonlinear region since the error remains less than 7%for tilting up to 50°.Finally,the effect of weight matrixes in the LQR cost function on overall weight and power of the HC motion system is discussed.

A Design of Nonlinear Scaling and Nonlinear Optimal Motion Cueing Algorithm for Pilot’s Station

Motion cueing algorithms(MCA)are often applied in the motion simulators.In this paper,a nonlinear optimal MCA,taking into account translational and rotational motions of a simulator within its physical limitation,is designed for the motion platform aiming to minimize human’s perception error in order to provide a high degree of fidelity.Indeed,the movement sensation center of most MCA is placed at the center of the upper platform,which may cause a certain error.Pilot’s station should be paid full attention to in the MCA.Apart from this,the scaling and limiting module plays an important role in optimizing the motion platform workspace and reducing false cues during motion reproduction.It 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 MCA.A nonlinear scaling method is designed to accurately duplicate motions with high realistic behavior and use the platform more efficiently without violating its physical limitations.The simulation experiment is verified in the longitudinal/pitch direction for motion simulator.The result implies that the proposed method can not only overcome the problem of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation,but also reduce the false cues to improve dynamic fidelity during the motion simulation process.