Abstract
This paper proposes a neural network-based nonsingular terminal sliding mode controller with prescribed performances for the target tracking problem of underactuated underwater robots. Firstly, the mathematical formulation of the target tracking problem is presented with an underactuated underwater robot model and the corresponding control objectives. Then, the target tracking errors from the line-of-sight guidance law are transformed using the prescribed performance technique to achieve good dynamic performance and steady-state performance that meet the pre-set conditions. Meanwhile, considering the model’s uncertainties and the external disturbances to the underwater robots, a target tracking controller is proposed based on the radial basis function (RBF) neural network and the non-singular terminal sliding mode control. Lyapunov stability analysis and homogeneity theory prove the tracking errors can converge on a small region that contains the origin with prescribed performance in finite time. In the simulation comparison, the controller proposed in this paper had better dynamic performance, steady-state performance and chattering supression. In particular, the steady-state error of the tracking error was lower, and the convergence time of the tracking error in the vertical distance was reduced by 19.1%.
Highlights
In recent years, underwater robots have been widely used in various underwater tasks
As underwater robots generally have the characteristics of many uncertainties, high nonlinearity and strong coupling dynamics, and work in an environment with unknown external disturbances, it is typically difficult for traditional linear controllers to achieve good tracking control performance
( ) The above stability analysis proves that the tracking errors δ −δ, β,α, Ze can converge to the neighborhood of the zero without any singularity in finite time with the prescribed performance, and the proposed controller can solve the problem of underwater robot target tracking with the external disturbances and modeling uncertainties
Summary
Underwater robots have been widely used in various underwater tasks. Taha Elmokadem et al designed a target tracking controller based on terminal sliding mode control, and proved that the tracking errors can converge to zero within a specified finite time [18]. Omid Elhaki et al created a neural network-based target tracking controller for an underactuated AUV with a prescribed performance to overcome unmodeled dynamics and external disturbances [10]. From previous research, in order to achieve robustness to uncertainties and external disturbances, realize finite-time convergence, attenuate chattering and obtain the tracking error’s prescribed performance simultaneously, this paper proposes a neural network nonsingular terminal sliding mode controller with prescribed performance for the target tracking problem of underactuated underwater robots. The non-singular terminal sliding mode controller was developed to ensure that the underwater robot is robust to external disturbances and modeling uncertainties, and guarantee finite-time convergence of the tracking errors.
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