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This book covers a number of models and control types. An integrated nonlinear state-space mode of the marine propulsion system is developed. This is based upon physical principles that incorporate uncertainties arising from engine thermodynamics and disturbances arising from propeller hydrodynamics. The mode employs artificial neural networks to depict the nonlinearities of the thermochemical processes of engine power/torque generation and the engine–turbocharger dynamical interaction; neural nets combine the required mathematical flexibility and formalism with numerical training and calibration options using either thermodynamic engine models or measured data series. The neural state-space model is decomposed appropriately to provide a linearised perturbation model suitable for controller synthesis.
The proportional–integral (derivative) control law is examined under the perspective of shaft speed regulation for enhanced disturbance rejection of the propeller load. The typical marine shafting system dynamics and configuration allow for a smart implementation of the D-term on shaft torque feedback.
Full state-feedback control is examined for increased robustness of the compensated plant against parametric uncertainty and neglected dynamics. The H¥ requirements on the closed-loop transfer matrix are appropriately decomposed to similar ones on scalar transfer functions, which give specifications that are easier to manipulate.
In effect, the methods are comparatively assessed and suggestions and practical applications are given. This synthetic approach to propulsion plant control and operational problems should prove useful for both theoreticians and practitioners, and can be easilty adopted for the control of other proceses or systems outside the marine field, as well.
