English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Thesis

Optimal control of motion simulators

MPS-Authors
/persons/resource/persons192766

Katliar,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Ressource
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Katliar, M. (2020). Optimal control of motion simulators. PhD Thesis, Albert-Ludwigs-Universität, Freiburg i.Br., Germany.


Cite as: http://hdl.handle.net/21.11116/0000-0007-7FB2-2
Abstract
Accurate reproduction of the feeling of motion is the primary goal of motion simulation. The subjective experience of self-motion results from the processing of visual and inertial sensory information in the human central nervous system (CNS). In contrast to the real experience, the simulation capabilities are restricted by the motion simulator workspace, which makes the exact reproduction of the inertial sensory input generally impossible. It is possible,however, to minimize the mismatch between the desired and the actual inertial signal, while satisfying the constraints imposed by the motion simulator. This naturally leads to the formulation of the motion simulation problem as a constrained optimal control problem (OCP).If this OCP is solved in real-time on a moving horizon, we enter the field of model predictive control (MPC). The MPC-based motion simulation is computationally expensive due to the (large) size of the problem and the need to take the generally non-linear dynamics of the simulator into account. The solution time must be small enough to achieve the desired control rate. Use of appropriate algorithms and a thoughtful approach to software development are needed to make the MPC implementation real-time capable.Another important problem is the offline design of motion simulators. The optimal design parameters can be obtained from an optimization problem, which can be solved simultaneously with finding optimal control inputs in an offline context.This work focuses on developing efficient approaches for solving optimization problems arising in the field of motion simulation. In the offline context, a method to simultaneously optimize simulator trajectories and design parameters is developed. In the online context, real-time capable implementations for two motion simulators at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany are created and their performance is analyzed. One of the implementations is experimentally validated by measuring the inertial signal tracking error for realistic motion simulation scenarios.The efficiency of the software implementation plays a critical role in real-time MPC. In order to simplify the development of MPC controllers and to be able to reuse algorithms, a number of software packages exist. Such software packages must be efficient, convenient to use, and provide a sufficient degree of control over the implementation details to the user. To satisfy these conflicting demands, the tmpc library was developed in the context of this work and was used to implement the real-time controllers for the two motion simulators. The library is released as open source in the hope that it will be useful for developing further real-time MPC applications.