Actuator Constrained Motion Cueing Algorithm for a Redundantly Actuated Stewart Platform

Abstract

An improved method to provide a motion trajectory for full flight simulator to simulate the acceleration during a flight simulation is presented. The motion cueing trajectory is based on a constrained optimization problem, with the generated optimal acceleration cues subjected to the actuators travel constraints of the motion platform. The motion platform researched in this contribution is a redundantly actuated parallel manipulator, therefore the available workspace is more limited and the actuator constraints become more complex. The differential kinematic analysis is utilized in the optimization problem to define the relationship of the acceleration in the platform coordinate and in the actuator coordinates. An acceleration profile is defined in function of the actuator travel to create a strict acceleration constraint in the actuator coordinate, thus a strict travel constraint. The algorithm is tested in a simulation and implemented in a full size redundantly actuated motion platform. Measurement results show that the proposed new motion cueing algorithm (MCA) is able to keep the actuators within their travel limit and at the same time provide the correct motion cues for the simulator pilots. The need to tune the MCA for the worst case scenario which is necessary to avoid damage to the platform, while at the same time can be disadvantageous for the normal case use, is relieved by the utilization of the online optimization process.

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

“…Asadi et al [22][23][24] have developed a new optimal MCA using an GA with adding the three compensatory units inside the LQR to be tuned based on GA and minimise the fitness function and maximise the motion fidelity. Pradipta and Sawodny [25] considered the active joints boundaries of a pneumatic Hexapod SBMP. Their algorithm uses an online optimisation algorithm to extract a optimum motion cues based on the actuators' limitations and the current position of the SBMP.…”

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.…”

“…The adaptive MCA for 2-DoF SBMPs using the gradient descent optimization method, to extract the optimal coefficient parameters of the filters based on the motion sensation error and the position and velocity limitations of the SBMP, was developed by Nehaoua et al [17]. Pradipta and Sawodny [15] introduced the actuator constrained MCA for a pneumatic hexapod platform. They employed the online optimisation method to find the best motion signals based on the current position of the SBMP and the actuators' limitations.…”

“…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.…”

Dynamics evaluation of parallel manipulators considering gravity and driving force uniformity

“…2,3 Thus, the parallel manipulators have been continually tried to develop as some industrial applications which require high speed/acceleration in the past several decades, such as parallel/hybrid machining equipment, 4–7 industrial robots, 8–10 and motion simulators. 11,12 Dynamics is the decisive factor which affects the performance of these high-speed industrial equipment, and it has great significance to carry out dynamics research of parallel manipulators.…”