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) autopositioning system. However, these vessels' dynamics vary considerably with operating condition, are strongly coupled, and are expensive and difficult to derive, theoretically or by conventional testing, making the design of conventional autopilots difficult to achieve. Multi-inputhultioutput self-tuning controllers are a possible solution. Two such schemes are presented; the first is an implicit linear quadratic on-line self-tuning controller, and the other uses a robust control law based on a first-order approximation of the open-loop dynamics and on-line recursive identification. The performance of these controllers is evaluated by examining their behavior when controlling a comprehensive nonlinear simulation of an ROV and its navigation system. An interesting off-shoot of this study is the application of recursive system identification techniques to the derivation of ROV models from trials data; the potential advantages of this method are discussed.
Models of the open‐loop dynamics for prototype Remotely Operated Underwater Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) are required for performance prediction and autopilot design. This article describes techniques which can be used to derive these models. The methods may be conveniently grouped into predictive techniques i.e., those which require only vehicle design data and which predict dynamics before the prototype is built and testing techniques i.e., methods which measure open‐loop dynamics from the trials performance of a prototype ROV. The relative benefits and drawbacks of the various methods are examined.
Dynamic models for Remotely Operated Underwater Vehicles (ROVs) are essential for the creation of autopilots and pilot training simulators. However the derivation of these models is difficult because ROV dynamics are strongly coupled, highly nonlinear and ill-defined. In addition, expensive, specialised testing equipment is required for conventional modelling.An alternative approach which offers the potential for inexpensive and accurate ROV models uses data gathered during simple free-running trials, processed by System Identification (SI) and Parameter Estimation (PE) algorithms. SI and PE algorithms are tested on simulated ROV trials data and compared using time domain results and statistical methods.
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