Nowadays underwater vehicles are important part of shore and offshore industries. These vehicles have significant role in scientific, research, commercial and military applications such as submarine observations, structural iection, iection of underwater pipelines and cables, mapping, drilling operations and etc. Although remotely operated vehicles (ROV) have been employed for detection and tracking, their range of operation is constrained by the length of the tether. Furthermore, the need for a support vessel and an ROV operator adds to the cost of monitoring operation. One way to circumvent these problems is to render the autonomous underwater vehicles (AUV), that is, they execute the task with minimal human intervention. It is obvious that the operation of these robots depends on good performance of their control and guidance system. There are some problems to design the control system of these robots such as nonlinearity and interaction of motion equations in different directions and disturbances from environment. In this thesis, a control system in directions of heading and depth is designed for autonomous underwater vehicle with nonlinear model predictive control based on disturbance observer. Therefore in beginning nonlinear and multivariable dynamic equations of robot motion in six degree of freedom for REMUS model are presented. Dynamic equations are converted into two single-input single-output subsystems to design the control system. This conversion can be done by this assumption that the interferences between movements in other directions are disturbance. On this basis, nonlinear model predictive control is designed by assumption that the states and the disturbance, created from interferences between movements in other directions, is available. The prediction model, used in the controller design, is carried out via Taylor series expansion. In order to estimate disturbance, two disturbance observers are introduced separately. A basic idea in the design of the first observer /estimator is to modify the estimation by the difference between the estimated output and the actual output. But second observer is based on sliding mode observer. In this observer, disturbance is estimated by using estimation the state. In fact the nonlinear model predictive controller with the sliding mode observer uses partial state feedback. The ability of this controller to track reference heading and depth in each subsystem and also the ability of two observers to estimate the disturbance are shown by simulation. Also the performance of this controller to control the six degree of freedom equations is shown. Keywords: Autonomous underwater vehicle, dynamic, heading and depth control, nonlinear model predictive control, disturbance observer, sliding mode observer, guidance system.
