Modeling, Analysis and Control of Underwater Vehicle SROV

A new type of Remotely Operated Vehicle (ROV) has been designed by Marin Mätteknik (MMT) in cooperation with Reach Subsea and Kystdesign AS; the Surveyor ROV. Although MMT is successfully using the SROV in day-to-day operations, no mathematical model describing the system has previously been derived. In this thesis project, a mathematical model describing the SROV is developed through system identification techniques. Experiments to facilitate parameter estimation of the model are designed and consequently performed. The gathered data sets are investigated to determine how well they are suited for parameter estimation. Estimation of the continuous-time model parameters are carried out using a Kalman filter running on the input-output data obtained through the experiments. Comparisons between this method and results obtained through a subspacebased identification Matlab method are performed. Model validation is carried out using numerous performance measures. The thesis has shown that a coupled LPV model may be a feasible approach to the modeling problem, and also makes suggestions that could possiblyimprove on the results. As an alternative to the current control system, simulations of closed-loop responses of the identified system model using a Model Predictive Control (MPC) structure are undertaken and presented. The simulations show that good performance is achievable using the MPC lgorithm. Noticeably, the current control system has difficulties attenuating deviations from angular velocity set points. The MPC scheme has been shown to effectively suppress such control errors in simulations

Continuous time state-space model identification with application to magnetic bearing systems

This thesis presents the identification of continuous time linear multi-variable systems using state-space models. A data-driven approach in realization by the subspace methods is carried out in developing the models. In this thesis, the approach by subspace methods is considered for both open-loop and closed-loop continuous time system identification. The Laguerre filter network, the instrumental variables and the frequency sampling filters are adopted in the framework of subspace model identification. More specifically, the Laguerre filters play a role in avoiding problems with differentiation in the Laplace operator, which leads to a simple algebraic relation. It also has the ability to cope with noise at high frequency region due to its orthogonality functions. The instrumental variables help to eliminate the process and measurement noise that may occur in the systems. The frequency sampling filters are used to compress the raw data, eliminate measurement noise so to obtain a set of clean and unbiased step response data. The combination of these techniques allows for the estimation of high quality models, in which, it leads to successful performance of the continuous time system identification overall. The application based on a magnetic bearing system apparatus is used to demonstrate the efficacy of the proposed techniques

Stochastic Theory of Continuous-Time State-Space Identification

Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation proble

Stochastic Theory of Continuous-Time State-Space Identification

This paper presents theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input-output sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs, thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem