Data-Driven Robust Attitude Tracking Control of Unmanned Underwater Vehicles with Performance Constraints

This paper studies the data-driven attitude tracking control issue for an unmanned underwater vehicle (UUV) with disturbances. First, a new polynomial finite-time prescribed performance function (FTPF) is introduced to avoid the problem of the computation number increasing as the exponential term increases in the conventional exponential FTPF. By using the new polynomial FTPF, the tracking error is converted into a constrained form. Then, an estimator is designed for estimating the unknown pseudo-partitioned Jacobian matrix (PJM) in the linearization model, and a discrete-time nonlinear disturbance observer (DNDO) is adopted for observing unknown disturbances. It is worth noting that the DNDO can avoid the large overshoot by introducing a saturated function. With the help of the estimator for the PJM, the DNDO, and the constrained error, a data-driven robust control strategy with performance constraints is designed to fulfill accurate attitude tracking control of the UUV, which ensures that the tracking error draws into a prescribed region in a predetermined time. Eventually, the control strategy is verified by numerical simulations.