The invention provides an intelligent
attitude control method. The control performance of an
underwater robot is improved by utilizing a
machine learning technology. The method comprises the following steps: acquiring attitude data in an
underwater environment through a sensor, such as an attitude angle, a linear velocity and an
angular velocity; inputting the attitude data into a
machine learning model for analysis and learning; predicting and optimizing an
attitude control command of the
underwater robot by using the
machine learning model; and transmitting the optimized
attitude control command to an
actuator of the
underwater robot so as to realize accurate attitude control. In the method, a
machine learning model is trained through a
supervised learning algorithm or a
reinforcement learning algorithm so as to optimize the prediction and adjustment process of an attitude control command according to historical data and feedback information. The historical data comprises sensor data and corresponding attitude control commands, and the feedback information comprises the attitude, the position and the sensor data of the
underwater robot. By continuously updating a
machine learning model, the method can adapt to changes of different underwater environments, and attitude control targets such as
energy consumption, stability and maneuvering characteristics are optimized. The intelligent attitude control method has the following advantages: the attitude control precision and stability of the
underwater robot are improved; the
system adapts to changes of different underwater environments; the control performance is continuously improved through learning and optimization of a
machine learning model; the device is widely applicable to the fields of
ocean exploration, underwater pipeline maintenance,
seabed resource development, diving operation and the like.