Model Predictive Control of a Tail-Actuated Robotic Fish

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.