Motion control of robotic fish based on RBF neural network adaptive sliding mode

DOI:	10.11809/bqzbgcxb2023.04.032
Keywords:	biomimetic robotic fish; RBF neural network; sliding mode control; trajectory tracking control; spiral downward motion
Abstract:	To address the problem of controlling the spatial motion of robotic fish under the disturbance of external environment such as water disturbance and waves, this paper uses a flexible 3 degree of freedom pectoral fin propulsion biomimetic robotic fish as the research objects. Based on setting the optimal motion trajectory of the pectoral fin, the control input is simplified to the phase difference of the flapping wing angle, and a hydrodynamic model of the biomimetic robotic fish under the disturbance conditions is established. Due to the complexity of fluid motion and the influence of the robotic fish deformation on the kinetic parameters, the RBF neural network adaptive sliding mode control method of the robotic fish is proposed. The RBF neural network is used for online approximation of the uncertainty terms and the water flow disturbance terms in the kinetic model, and the adaptive sliding mode controller is combined to adjust the control parameters in real time. Then, the asymptotic stability of the designed controller is proved by Liapunov’s stability theorem. The experimental results show that the robotic fish have a large error only in the initial stage during the spiral downward motion, and both their motion trajectory and the desired trajectory basically match after about 8 s, which verifies the effectiveness of the method described in this paper.
Issue:	Vol. 44 No. 4 (2023)
Published:	2023-04-28
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