Tracking Error Constraints and Integral Guidance Mechanisms-Based Antidisturbance Path-Tracking Control for a Robotic Fish

Abstract

This work reports a path-tracking control method based on tracking error constraints and integral guidance mechanisms with anti-interference for a robotic fish. A virtual control input is presented, and the adaptive integral light-of-sight rule is developed to eliminate the deviation of the sideslip position and improve the anti-interference ability of the robot. The raw and large adaptive controllers of a robotic fish are constructed to fit the model uncertainty and flow field disturbance brought by neural network functions. The approximation values compensate for the system’s control input to improve the environmental adaptability of the body. Moreover, this work employs event-triggering mechanisms to reduce the trigger frequency of the actuator and improve tracking speed. Barrier Lyapunov theory confirms that the proposed strategy has uniform ultimate boundedness. Simulation and experimental results indicate that the scheme improves the error convergence rate and steady-state characteristics of the robotic fish.