Research On Dynamical Model Identification And Generalized Predictive Control Of Autonomous Underwater Vehicle

The 21^th century is the century that man investigate, develop and utilize peacefully the sea, with the development of developing and utilizing the sea, more and more researchers apply themselves to the development of autonomous underwater vehicle(AUV) which can explore the underwater circumstance and accomplish the special missions. As a vehicle which works in complicated oceanic environments, automation and safety are its main character. And intelligent control is the key technology to keep AUV autonomous and safe. Intelligent control includes autonomous mission planning, motion control and status monitoring. So, research on autonomous mission planning, motion control and status monitoring for AUV has the important meaning to improve AUV's intelligence and application.AUV is complicated non-linear dynamical plant, Generalized Predictive Control(GPC) can systematically take into account real plants constraints in real-time, and is robust with respect to modeling errors, sensor noise. In this paper, some research works about AUV motion control based on GPC are carried out. The dynamical models of the AUV are build with Newton-Euler equations and neural networks and used as multi-step predictive model. With predictive model, based on the passed plant status and future inputs and reference outputs, the future inputs can be predicted, the optimization is real-time. With the actual output error of the real plant, the predictive model or the controller parameters are adjusted. So the optimization is close-loop.Based on analyzing the mechanical structure of the "Beaver" AUV, to describe the motion of AUV, the world reference frame and the body reference frame are used. The transformation matrix for position and attitude vector between two reference frames are also presented. And the transformation matrix is also simplified to "Beaver" AUV. Based on Newton-Euler equations, the AUV dynamical model for yaw and surge are build, the parameters of the dynamical model are also identified with least square method, and the identification error is considered. The dynamics of the propeller for the "Beaver" AUV is also analyzed. The on-line learning for Dynamical Recurrent Neural Networks(DRNN) is proposed and realized with sliding window mode, the nonlinear dynamical plant with white noise is identified on-line with the DRNN, fusing the parallel model and series-parallel model, the improved recurrent mode is proposed. This can not only improve the convergence of the DRNN on-line learning but also filter out the noise. And the DRNN with on-line learning is applied to the "Beaver" AUV dynamical model identification successfully.To GPC, considering the unknown parameters or slowly changing of predictive model and the constraints to the inputs, the indirect and direct adaptive GPC to linear dynamical plant are programmed. The dynamics of the "Beaver" AUV is analyzed. Because a specific non-linear dynamical equations can be linearzed with on-line changing parameters, with Euler differencing equation, the non-linear speed and position dynamical model for AUV are controlled with GPC.The modified Elman Neural Networks is used as multi-step predictive model, the derivative to reason the Neural Generalized Predictive Control(NGPC) law is analyzed elaborately, the on-line learning and off-line learning NGPC is realized to control the non-linear dynamical plant, the output error is also analyzed, the on-line learning and off-line learning NGPC is applied to the control for "Beaver" AUV yaw and surge speed successfully. Because the improved output recurrent mode, the neural networks based GPC is more robust than CARMA model based GPC. When the controlled dynamical plant is polluted with slow changing noise, the control effect to on-line learning GPC is better than off-line GPC.