Experimental study of a modified command governor adaptive controller for depth control of an unmanned underwater vehicle

Command governor based adaptive control (CGAC) is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles (UUVs) with parameter variations. CGAC is derived from standard model reference adaptive control (MRAC) by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay. Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs, it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC. As a solution, this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance. The new modified CGAC (M-CGAC) has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction, significantly improves the overall tracking performance, uses less control force, and increases the robustness to noise and time delay. Thus, M-CGAC is a viable adaptive control algorithm for current and future UUV applications.