Synchronization of Animal-Inspired Multiple High-Lift Fins in an Underwater Vehicle Using Olivo–Cerebellar Dynamics

The development of neuroscience-based control methodologies and their integration with the high-lift unsteady hydrodynamics of control surfaces inspired by swimming and flying animals are the subjects of this paper. A biology-inspired rigid autonomous undersea vehicle called the biorobotic autonomous undersea vehicle (BAUV) has been developed at the Naval Undersea Warfare Center (NUWC), Newport, RI. The BAUV is equipped with six simultaneously rolling and pitching fins for generating large unsteady control forces for performing agile maneuvers. First, as an exploratory example, we introduce the van der Pol oscillator as an oscillatory controller for the BAUV and we describe experiments performed to examine the fin forces (thrust and lift) and electric power requirement, and to demonstrate the effectiveness of the oscillator's limit cycle property for disturbance rejection effectiveness. We then describe a BAUV control system that includes six inferior-olive (IO) neuron models for control of the pitch and roll motion of the six foils. These IO neurons exhibit limit cycle oscillation (LCO). For control of the BAUV, these IO neurons must oscillate in synchronism with specific relative phases. We present here four feedback linearizing control systems of varying complexity for control of the relative phases of the IO neurons. It is shown that each of the IO control systems accomplishes asymptotic regulation of the phases and thus enables the foils to produce the required control forces. The first controller has a global synchronization property, but the remaining controllers accomplish local synchronization. We present simulation results for tracking piecewise, time-varying phase angle commands as well as experimental results for control of the BAUV by IO neurons. The results show that with appropriate phasing of the fins, an optimal graceful gait of the BAUV is achieved where no untoward force or moment is present. An analog hardware version of the local controller with a cluster of six IO neurons has also been built, which allows five of the signals to rapidly synchronize to the reference, with or without prescribed phase shift, much like in the simulations. The designed controllers can be used in any platform or multivariate BAUV-like system requiring fast, accurate phase control. Laboratory test results for the phase synchronization of two servomotors (roll and pitch) using the designed analog hardware controller are also shown.