Successful controller development involves three distinct stages, namely, control law design, code debugging and field test. For Autonomous Underwater Vehicle (AUV) applications, the first two stages require special strategies. Since the dynamics of an AUV is highly nonlinear, and the environment that an AUV operates in is noisy with external disturbance that cannot be neglected, a robust control law must be considered in the first stage. The control law design is even more difficult when optimal criteria are also involved. In the second stage, since the software architecture on an AUV is very complicated, debugging the controllers alone without all the software routines running together often can not reveal subtle faults in the controller code. Thorough debugging needs at-sea test, which is costly. Therefore, a platform that can help designers debug and evaluate controller performance before any at-sea experiment is highly desirable. Recently, a 6 Degree of Freedom (DOF) AUV simulation toolbox was developed for the Ocean Explorer (OEX) series AUVs developed at Florida Atlantic University. The simulation toolbox is an ideal platform for controller in-lab debugging and evaluation. This paper first presents a novel robust controller design methodology, named the Sliding Mode Fuzzy Controller (SMFC). It combines sliding mode control and fuzzy logic control to create a robust, easy on-line tunable controller structure. A formal proof of the robustness of the proposed nonlinear sliding mode control is also given. A pitch and a heading controller have been designed with the presented structure and the controller code was tested on the simulation software package as well as at sea. The simulated and at-sea test data are compared. The whole controller design procedure described in this paper clearly demonstrates the advantage of using the simulation toolbox to debug and test the controller in-lab. Moreover, the pitch and heading controller have been used in the real system for more than 2 years, and have also been successfully ported to other types of vehicles without any major modification on the controller parameters. The similarity of the controller performances on different vehicles further demonstrates the robustness of the proposed Sliding Mode Fuzzy Controller. The main contribution of this paper is to provide useful insights into the design and implementation of the proposed control architecture, and its application in AUV control.
Neuro-Fuzzy Controller Design of the Anti-Lock Braking System.
