Facilitating Model-Based Control Through Software-Hardware Co-Design

Joao Ramos, Benjamin Katz, Meng Yee Michael Chuah, Sangbae Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper exemplifies the design process for legged machines capable of dynamic behaviors. In order to achieve high performance robots, it is crucial to guarantee harmonious integration between software and hardware. Hence, the development of such capable robotic platforms must address design requirements that meet the assumptions of typical model-based controllers but also respect the physical limitations of a real system. First, we show that proper hardware design choices can greatly aid the control algorithm by approximating the physical robot to the template assumptions. We include actuation and sensing design examples that allows a simple model to capture a major portion of the natural dynamic behavior of the physical machine. Results are applied to a real robot (Figure 1) and we show that the adopted methodology is able to address typical problems in legged robots such as high bandwidth force control and robustness to impact. Finally, a simple model-based balance controller that takes advantage of the fidelity of the template model to the real machine is implemented. These are examples of software-hardware codesign processes that vastly facilitate robotic control.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781538630815
StatePublished - Sep 10 2018
Externally publishedYes
Event2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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