Backstepping Control Strategy of an Autonomous Underwater Vehicle Based on Probability Gain

Yudong Peng, Longchuan Guo () and Qinghua Meng
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Yudong Peng: School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Longchuan Guo: School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Qinghua Meng: School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Mathematics, 2022, vol. 10, issue 21, 1-16

Abstract: In this paper, an underwater robot system with nonlinear characteristics is studied by a backstepping method. Based on the state preservation problem of an Autonomous Underwater Vehicle (AUV), this paper applies the backstepping probabilistic gain controller to the nonlinear system of the AUV for the first time. Under the comprehensive influence of underwater resistance, turbulence, and driving force, the motion of the AUV has strong coupling, strong nonlinearity, and an unpredictable state. At this time, the system’s output feedback can solve the problem of an unmeasurable state. In order to achieve a good control effect and extend the cruising range of the AUV, first, this paper will select the state error to make it a new control objective. The system’s control is transformed into the selection of system parameters, which greatly simplifies the degree of calculation. Second, this paper introduces the concept of a stochastic backstepping control strategy, in which the robot’s actuators work discontinuously. The actuator works only when there is a random disturbance, and the control effect is not diminished. Finally, the backstepping probabilistic gain controller is designed according to the nonlinear system applied to the simulation model for verification, and the final result confirms the effect of the controller design.

Keywords: complex nonlinear systems; state keeping; Bernoulli distribution; feedback control with backstepping; probability gain control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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