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Model Predictive Control of a Tail-Actuated Robotic Fish

[+] Author Affiliations
Maria L. Castaño, Xiaobo Tan

Michigan State University, East Lansing, MI

Paper No. DSCC2016-9918, pp. V001T03A006; 11 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME


The increase of potential threats to the integrity of our aquatic ecosystems has caused global concerns which have led to interest in the use autonomous aquatic robots to monitor such environments. In recent years, underwater robots that propel and maneuver themselves like real fish, often called robotic fish, have emerged as mobile sensing platforms for freshwater and marine environments. These robots achieve locomotion via actively controlled fins, and actuation is often achieved via oscillatory inputs. Given these types of applications, accuracy and energy-saving in trajectory control is of importance for mission successes. In this work, we propose a nonlinear model predictive control (NMPC) approach to path following of a tail-actuated robotic fish. In this design, we use bias and amplitude of the tail-beat as the input to be determined by the NMPC. The effectiveness of the proposed approach is demonstrated via simulation.

Copyright © 2016 by ASME



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