A Novel Data-Assisted Model and Discrete-Time Sliding Mode Steering Controller of Robotic Fish

In this paper, a novel dynamic model with the data-assisted method of robotic fish is proposed, and a discrete-time sliding mode controller for robotic fish is implemented to effectively control the steering. Considering the interference including frequency nonlinearity and angular speed delay term etc., the dynamic model is established based on experimental data which is added into the nonlinear interference. Aiming at solving the problem of the uncertainty of robotic fish system, a discrete-time sliding mode controller for steering control is designed, in the sense that not only the nonlinear interference can be weakened and the strong robustness can be obtained but also the steering angle of robotic fish can converge to the reference angle asymptotically. The simulation results show the effectiveness of the proposed dynamic model and the discrete-time sliding mode controller for steering tracking.