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Research Papers

Actuator Constrained Motion Cueing Algorithm for a Redundantly Actuated Stewart Platform

[+] Author and Article Information
Justin Pradipta

Institute for System Dynamics,
University of Stuttgart,
Stuttgart D-70563, Germany
e-mail: justin.pradipta@isys.uni-stuttgart.de

Oliver Sawodny

Institute for System Dynamics,
University of Stuttgart,
Stuttgart D-70563, Germany
e-mail: sawodny@isys.uni-stuttgart.de

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 25, 2015; final manuscript received January 17, 2016; published online March 30, 2016. Assoc. Editor: Dumitru I. Caruntu.

J. Dyn. Sys., Meas., Control 138(6), 061007 (Mar 30, 2016) (10 pages) Paper No: DS-15-1399; doi: 10.1115/1.4032556 History: Received August 25, 2015; Revised January 17, 2016

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.

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Figures

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Fig. 1

Redundantly actuated pneumatic motion platform for a full flight simulator, “Project ServoFlight” at the Institute for System Dynamics, University of Stuttgart [7,22]

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Fig. 2

Simplified representation of the platform with the corresponding vectors and coordinate systems

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Fig. 3

Platform workspace validation algorithm with zero platform orientation angles β=[ϕ θ ψ]T=0

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Fig. 4

Platform available workspace, area in red: Seven cylinders configuration, and area in blue: Six cylinders configuration. (a) x- and z-positions as yp set to zero, (b) y- and z-positions as xp set to zero, and (c) x- and y-positions viewed from the top.

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Fig. 5

Washout filter and tilt coordination block diagram, with the top channel as the translational channel, bottom channel the rotational channel, and in the middle is the tilt coordination

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Fig. 6

Actuator constrained MCA overall system scheme

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Fig. 7

Actuator acceleration constrain as a function of actuator position. The shaded area is the allowable acceleration value. The red line on top is the maximum, and the blue line on the bottom is the minimum.

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Fig. 8

Comparison of the actuator constrained MCA and the classical washout filter responses at the middle cylinder, withthe dashed line as the maximum cylinder stroke, and thevertical line at 1 s is the aircraft step reference accelerations, with step sizes: (a) z¨p,A=1 m/s2, (b) z¨p,A=2 m/s2, (c) z¨p,A=4 m/s2, (d) z¨p,A=6 m/s2, and (e) z¨p,A=8 m/s2

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Fig. 9

The actuator constrained MCA output in form of the optimal acceleration in z-direction with input of step acceleration of 8 m/s2 in z-direction

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Fig. 10

Platform position and orientation during the normal test scenario

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Fig. 11

Seven actuators positions during the normal test scenario

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Fig. 12

Platform position and orientation during the extreme scenario

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Fig. 13

Seven actuators positions during the extreme test scenario

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