Research on Anti-Interference Nonlinear Adaptive Control Method for Autonomous Underwater Gliders

In this paper, the characteristics of underactuation are analyzed according to the working principle of underwater glider, a complete 6DOF model of underwater glider is established, and the error equation of 3D path tracking is given. Based on the line-of-sight navigation method in three-dimensional space, the nonlinear adaptive control strategy is designed, and the roll angle control law, pitch angle control law, and depth control law of underwater glider are given. The tracking error of underwater glider is proved to be convergent by Lyapunov stability theorem. Finally, the results of marine experiments verify that the nonlinear controller designed in this paper can overcome the influence of constant current disturbance under certain conditions and has good robustness and good tracking effect under different ocean current sizes and course angles. Of course, one control method cannot be applicable to all sea conditions. When the angle between the size and direction of the ocean current exceeds a certain value, the tracking control of the underwater glider will show obvious divergence. Therefore, the stability range against the interference of the sea current is given in this paper.

Application of Nonlinear Adaptive Control in Temperature of Chinese Solar Greenhouses

The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. It is difficult for the conventional control method to deal with the above problems. To address these problems, a dynamic model of Chinese solar greenhouses was developed based on energy conservation laws, and a nonlinear adaptive control strategy combined with a Radial Basis Function neural network was presented to deal with temperature control. In this approach, nonlinear adaptive controller parameters were determined through the generalized minimum variance laws, while unmodeled dynamics were estimated by a Radial Basis Function neural network. The control strategy consisted of a linear adaptive controller, a neural network nonlinear adaptive controller, and a switching mechanism. The research results show that the mean errors were 0.8460 and 0.2967, corresponding to a conventional PID method and the presented nonlinear adaptive scheme, respectively. The standard errors of the conventional PID method and the nonlinear adaptive control strategy were 1.8480 and 1.3342, respectively. The experimental results fully prove that the presented control scheme achieves better control performance, which meets the actual requirements.