Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles

The effectiveness of subsea intervention has been found to be dependent upon the capability of an autonomous underwater vehicle's (AUV's) or remotely operated underwater vehicle's (ROV's) auto-positioning system. However, these vessel's dynamics vary considerably with operating condition, and are strongly coupled; they are expensive and difficult to derive, theoretically or through conventional testing, making the design of conventional autopilots difficult to achieve. Multi-input-multi-output self-tuning controllers offer a possible solution. Two such schemes are presented. The first is an implicit linear quadratic online, self-tuning controller, and the other uses a robust control law based on a first-order approximation of the open-loop dynamics and online recursive identification. The controllers' performance is evaluated by examining their behavior when controlling a comprehensive nonlinear simulation of an ROV and its navigation system. An interesting offshoot of this study is the application of recursive system identification techniques to the derivation of ROV models from data gathered from the trials; the potential advantages of this method are discussed.