In the process of research on ship path tracking control, considering that the rudder angle needs to be optimized and the rudder amplitude and speed are constrained, this paper proposes a model predictive control (MPC) algorithm. High-order nonlinear observers are designed to avoid the impact of environmental interference and solve the problem that the highorder state value of the system is not easy to measure. At the same time, the speed state of the ship and the total unknown items including model uncertainties and external interference are estimated. The prediction model in the article uses a separate ship model that considers the response system of the steering gear, which makes the ship motion control more in line with the actual situation and improves the accuracy. Finally, it is verified by Matlab simulation. The designed controller enables the ship to track the reference path under the time-varying disturbance of wind, waves and currents, and the rudder angle is small and smooth. The results show the effectiveness of the design.
Aiming at the mathematical model of ship with internal parameter uncertainty and external disturbance, in order to solve the problem of insufficient observation ability of conventional ADRC when external disturbance changes greatly, this paper combined neural network and sliding mode designing to improve and optimize the ADRC. An extended state observer is designed to estimate the total disturbance based on the nomoto model, using the method of nonlinear sliding mode design of ADRC error feedback control law, which makes the physical meaning of the parameters of the apparent ease of setting.
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