[+] Author and Article Information
Maria Castano

Smart Microsystems Lab, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 USA

Xiaobo Tan

Smart Microsystems Lab, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 USA

1Corresponding author.

ASME doi:10.1115/1.4043152 History: Received May 05, 2018; Revised March 08, 2019


There has been an increased interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like real fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish presents significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach for path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches.

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