Model Identification and Course Controller Design for Unmanned Surface Vehicle
Dongdong Mu, Guofeng Wang, Yunsheng Fan, Yongsheng Zhao
International Journal of Control and Automation
This paper addresses two interrelated problems concerning the course control of unmanned surface vehicle (USV), namely, the model identification and the course controller design. First of all, field experiments (turning test and zig-zag test) are carried out to collect experimental data, then recursive least squares and data fitting methods are used to identify the parameters of the Norrbin model. Considering the uncertainty of the model, the method of non singular terminal sliding mode (NTSM )
... is employed to design course controller. Meanwhile, in order to reduce control gain, disturbance observer (DOB) is used to compensate the external disturbance, and DOB-NTSM course controller is proposed. But the inherent characteristic of NTSM can lead to chattering of control input, RBF neural network (NN) is employed to reduce the chattering phenomenon. Finally, classical PID and NTSM control law are compared with the DOB-NTSM. Simulation results show that DOB-NTSM course controller has good control effect, which proves its correctness and feasibility. authenticity of the identification results are proved in Section 2.4. On the basis of identifying the model of USV, course controller is designed. Paper  proposes a controller for pod-driven surface ship based on adaptive backstepping with integrator. Paper  uses RBF NN to approach the part which is difficult to calculate of the control law. By combining the advantages of the robustness of PID and fast learning speed of wavelet network, a wavelet-network-based PID course controller is proposed in  . In this paper, on the basis of considering the model uncertainty, NTSM is employed to design course controller. The advantages of this paper lie in that it presents a complete set of programs to identify the model parameters of USV and the method of identification is simple and easy to implement. In addition, considering the influence of model uncertainty and external disturbance, NTSM is employed to design course controller. At the same time, DOB is designed to compensate disturbance and RBF NN is used to weaken the chattering of the sign function. The rest of the paper is organized as follows. Section 2 introduces the field experiments and model identification. In Section 3, DOB-NTSM course controller is designed. In Section 4, numerical simulations are carried out to show the rapidity and robustness of our design. Finally, some conclusions are made and future research directions are introduced in Section 5.