Performance Analysis and Dexterity Monitoring of Hexapod-Based Simulator Mohammad Reza Chalak Qazani 1 , Houshyar Asadi 2 , Siamak Pedrammehr 3 , Saeid Nahavandi 4 1,2,3,4 Institute for Intelligent Systems Research and Innovation, IISRI, Deakin University, VIC, Australia e-mail: m.r.chalakqazani@gmail.com Abstract—Washout filter is in demand to generate the vehicle acceleration and angular velocities with high loyalty to make the best realistic motion. The limitation of the simulator’s moving platform is an integral part of every motion simulator. Adaptive and model predictive control washout filters contribute with this limitation online. It is necessary to have a real vision to the workspace limitation which is needed for unpredicted next step of the simulator. In this paper dexterity analysis is addressed as a novel method to monitor the performance accuracy of the simulator. The dexterity of both the classical and optimal washout filters are compared. Finally, it is shown that the dexterity analysis in optimal washout filter is necessary to avoid the workspace limitations. Keywords-parallel-based simulator; dexterity analysis; classical washout filter; optimal washout filter I. INTRODUCTION The regeneration of motion signals from the real vehicle cannot be modified for the driving simulator not merely because the real vehicle produces a large acceleration than the motion platform, but the reachable workspace of the manipulator is restricted as well. The rotation limitations of the universal and spherical joints, displacement of prismatic joints, and the collisions between pods have a direct effect on the workspace of the platform and reduces the platform rotation. To overcome this problem, Motion Cueing Algorithm (MCA) has been announced to produce the motion signal for the simulator as close as the real vehicle motion. The emulation of the visual and vestibular information to the real motion signal is called motion cueing [1]. MCA is used to prohibit motion sickness and platform limitation. Colombet et al. [2] showed that the driving simulator decreases the motion sickness 12 times smaller in comparison with the static simulator. The standard form of MCA is known as washout filter and it is sorted into four categories such as classical, adaptive, optimal and Model Predictive Control (MPC) washout filters. Classical washout filter is employed to generate certain rotational velocities and linear forces for the perception of the simulator driver that can be sensed in the real car. This is the common method used in motion cueing algorithms [3]. The first MCA have been proposed by Schmidt and Conrad as a classical washout filter [4, 5]. Their work has been improved by Reid and Nahon [6-8]. The human body perception model is capable of sensing the linear force and the angular velocity. The outputs of the classical washout algorithm are sway, surge and heave as linear positions, and roll, pitch, and yaw as angular positions of the simulator platform [9]. The tilt coordinate takes advantage of the somatogravic illusion using the gravity to simulate the low- frequency acceleration [10-12]. The classical washout filter consists of the fixed structure without considering of human perception model such as vestibular system. Kurosaki [13] used the control techniques to minimize the error. He found an optimized structure for the washout filter instead of optimizing the signal. Sivan et al. [14] proposed a new method considering a vestibular model that was missed in [13]. Nahon and Reid [6, 15] assumed a center of rotation using a frame attached to the head of the driver or pilot and minimized the error based on the cost function. This method has further been improved by Genetic Algorithm [16, 17] and considering human sensation model [18-20]. The significant difference of the optimal washout filter and the classical one is its higher order transfer function. Since the generation of the mechanisms, researchers had a struggle with performance analysis from the design of the mechanism to the configuration evaluation in a specific workspace area. Dexterity is of importance as it benefits the performance analysis of the mechanism [21-25]. Considering the determinants, the first measurement of dexterity has been performed by Paul and Stevenson [26]. Yoshikawa [27] defined the square root of the JJ T determinant where J is the Jacobian matrix of the manipulator. In this paper, the dexterity analysis is employed to evaluate the platform configuration. This, moreover, helps to find the unpredicted next step of the simulator, which would be useful in the new generations of washout filters. In section II of the paper, the classical and optimal washout filters are addressed. The SimMechanic model of the simulator is generated in section III considering washout filtering, inverse kinematics, PID controller under MATLAB. The results are obtained and evaluated through classical and optimal scenarios in section V. In section VI, the concluding remarks are presented. II. CLASSICAL AND OPTIMAL WASHOUT FILTERS The purpose of classic motion cueing algorithm is the production of certain linear forces and angular velocity to be sensed by human perception in the simulator compared to the personal senses in the real vehicle. Classical washout filter is composed of four basic strategies. A linear or non-linear scale factor and limitation which are used to match the signal with the physical parameters of the simulator and eliminate the data out of limitation, respectively. The second strategy is the high-pass filter to remove the low-frequency part of the linear acceleration signal and transfer the high-frequency as a 226 2018 4th International Conference on Control, Automation and Robotics 978-1-5386-6338-7/18/$31.00 ©2018 IEEE Authorized licensed use limited to: University of Tasmania. Downloaded on July 10,2024 at 10:12:08 UTC from IEEE Xplore. Restrictions apply.