Actuator- Based Optimization Motion Cueing Algorithm

An Optimal Motion Cueing Algorithm Using the Inverse Kinematic Solution of the Hexapod Simulation Platform

“…Later, Asadi et al [26] designed the new robust optimal MCA based on GA with the deliberation of the SBMP's limitation within the Cartesian coordinate system of the manipulator as well as motion perception related factors such as motion threshold, sensation error fluctuations and sensation shape following. Ellensohn et al [27] considered the displacement, velocity and acceleration limitations of the actuators in calculation of the optimum motion signal for the SBMP using the prediction model and the direct kinematics model. The expensive computational load is the main disadvantage of their algorithm because there is not an analytical solution for direct kinematic solution of the parallel manipulator.…”

“…As the SBMP normally faces large and fast motion signals including AV and LA, the acceleration limitations of the SBMP's actuators are important in order to regenerate the accurate motion signal for the SBMP's driver. Also, Pradipta and Sawodny [25] and Ellensohn et al [27] considered the actuator limitations of the SBMP, but the computational burden of their proposed algorithm is high for real-time applications.…”

A New Fuzzy Logic Based Adaptive Motion Cueing Algorithm Using Parallel Simulation-Based Motion Platform

“…The movement signals of the real vehicle cannot be directly implemented into the SBMP because of the limited workspace area. The approach to reproduce the real vehicle motion signals (including linear acceleration and angular velocity), generating the same real vehicle movement feeling for the SBMP user, is called motion cueing algorithm (MCA) including classical [11,12], adaptive [13][14][15][16][17][18], optimal [19][20][21][22][23] and model predictive control [24][25][26][27][28]. The classical MCAs are very popular in industries and research centres because of its simplicity, easy tunning, low computational load and safety.…”

“…Since their method uses the online optimisation algorithm, there is a high computational load for real-time applications. Ellensohn et al [14] used the direct kinematics model of the SBMP and the prediction model of the motion signal to extract the best endeffector configuration based on the position, velocity and acceleration limitations of the actuators. Their model suffered from high computational loads since the direct inverse kinematics problem cannot be solved analytically, and the prediction model increases the size of the matrices that should be solved in real-time.…”

“…All the previous studies on adaptive MCAs [13][14][15][16][17][18] considered the limitations of the SBMP in the Cartesian system instead of the joints' limitations which lead to the conservative use of the workspace area. Considering the active and passive joints' positions in adaptive MCA leads to the extensive use of the mechanisms workspace and improves the fidelity for the driver of the SBMP.…”