Sliding mode control is one of the most effective methods of controlling nonlinear systems with bounded uncertainty. Exponential convergence of tracking error is one of the most important problem of classic sliding mode control. One way to solve this problem is use of terminal sliding mode control. The great thing about terminal sliding mode control, is it’, s robustness in face of model uncertainty and external disturbances while can guarantee tracking error converge to zero in finite time simultaneously. Usually terminal sliding mode controller, is limited by singularity at the origin and infinite control signal. This article attempts to the singularity problem in controlling underwater robots and decreasing the convergence time by defining a new sliding surface for terminal sliding mode controller. simulation results shows the efficiency of proposed controller as it effectively improves the convergence time and accuracy in under water robots with are faced by structural and environmental uncertainties.

In this project guidance and control of underwater robot, including three engines and propeller attached to it, by Fuzzy control has been investigated. Fuzzy control is done based on human experience and required laws. The robot can also be controlled and guided by the user. This robot may be used in sea or swimming pool environment for finding goal point and locate in desired direction. In addition, this robot is able to follow a definite path and to do searching operation in that path. This robot is also able to overtake barriers on the surface or under the surface and forward toward target and path the barriers using fuzzy rules. In other words, while facing any barriers using fuzzy rules. In other words, while facing any barrier, robot decides necessary decision in such a way not to collide with it and path it. For simulation of the required system, webots’ software which is strong software for mobile robot simulation and Maple’s material software is used. This robot is capable to distance any obstacle in 3D and move towards the goal. Obstacle avoidance is done by fuzzy rules. Means when face any obstacle, the underwater robot decide for not collision and passes it. For the simulation system, Webots that is strong software for mobile robot simulation and mathematical software Maple is used.

In this paper, a new controller is presented based on robust feedback linearization controller in combination with integral-exponential error dynamics and potential functions for tracking control of an underwater robot in an obstacle-rich environment. underwater robots are considered as nonlinear, under actuated systems with indefinite, uncertain dynamics. In this research, by assuming a boundary for external disturbances and uncertainties a proposed robust control method has been put to use. Along with the robust feedback linearization algorithm which has been developed based on the dynamics of the nonlinear error defined for the underwater robot, and in order to avoid the obstacles, the control laws are combined with the virtual potential functions. The considered virtual potential functions make a repulsive force between the robot and the obstacles which intersect the desired path and then they bring about a safe move of the robot in obstacle-rich environments. Finally, the performance of the proposed new control algorithm is compared with the results of the implementation of classical sliding mode control laws. The results show the effectiveness of potentially directed proposed controller through obstacle-rich paths which operate far better facing obstacles.

Using kinetic models for the navigation of underwater robots is an important issue that has recently attracted the attention of many researchers. They are used as an auxiliary tool alongside the common navigation algorithms that use the kinematic models of the robots. Their use in underwater navigation is more crucial as the GPS and radio signals are not available in underwater environments. To use a kinetic model for the navigation of an underwater vehicle, it is required to have accurate values of its hydrodynamic coefficients, where the linear and pressure drag coefficients are among the most crucial parameters to be identified. In this paper, the drag coefficients of a sample remotely operated vehicle (ROV) are estimated using Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). For this purpose, a six DOF model of the underwater vehicle is used to simulate its motion. Then, the inputs and outputs of the simulated model are imported into the estimation algorithms to identify the drag coefficients of the robot. The results show that the UKF identifies the hydrodynamic coefficients more accurately than EKF. Also, by comparing the simulated maneuvers of the robot using the identified coefficients and the exact coefficients of the robot, it is observed that the coefficients identified by UKF lead to more accurate trajectories as compared to the coefficients identified by EKF.

Determining a dynamic model for an underwater robot is of great importance in design of guidance and control system. Researchers always need a complete knowledge about hydrodynamic stability derivatives coefficients of vehicle with sufficient accuracy to design a successful control system for underwater vehicles. The selection of proper actuator in control system is important on the global performance of the system and the costs of the project. Usually, the effect of dynamic stability derivative coefficients is not considered in the design of actuators; therefore, in the present study, it is tried to investigate the effect of these coefficients in the design of actuators. For this purpose, firstly, the equations of motion for an underwater robot are presented. Then, hydrodynamic coefficients that contains static and dynamic coefficients are determined, using a rapid computational code and, then, the effect of hydrodynamic stability derivatives coefficients on the operational dynamic parameters of vehicle such as the bandwidth of the system dynamics and its role in the control system are considered. Finally, the selection of appropriate actuator for the underwater robot and the effects of natural frequency of actuators on the system performance are studied.

Implementing control and guidance systems on an autonomous underwater vehicle requires spending much money, which in case of failure, will suffer heavy losses. For this purpose, the complete method of evaluating an autonomous underwater vehicle's guidance, control, and navigation system is the hardware in the loop test before the diving and buoyancy so that the validation of the designed system can be evaluated in conditions close to reality.This research discusses the implementation of the hardware in the loop test to guide the path of a remotely operated vehicle. Considering the dynamics of the underwater robot - which is highly non-linear and dependent; First, the design of a free model sliding mode controller in the presence of underwater currents as a disturbance in the form of software in the loop is discussed. Second, the software in the loop test is performed by applying the control signal obtained from the proposed controller to the driver of the electric motor as the robot's proportion, whose mathematical model has been identified in the laboratory. Finally, by gradually replacing the hardware drives of the robot in the loop, the hardware in loop test of six degrees of freedom of the remotely operated vehicle is done in real-time in the presence of environmental disturbances. The proximity of the hardware in the loop test results with the software test in the loop shows the correctness of the work done. Meanwhile, the performance of the proposed controller is compared with the PID controller in trajectory tracking. In the end, the accuracy and validity of the implemented hardware table are examined by analyzing the frequency spectrum of the measurements of the hardware in the loop rig.

Estimation of the hydrodynamic coefficients of an autonomous underwater vehicle is the purpose of this paper. The Kalman filter method has been used for estimating the AUV hydrodynamic coefficients. To estimate hydrodynamic coefficients without knowing their initial values, spatial and temporal information of the AUV are needed. This information can be collected through different methods including experimental methods which gather the required information by the sensors installed on the AUV. In this paper, outputs related to the time and location information are collected using robot maneuver simulation in CFX software, with a movable grid being used to simulate the robot maneuver. In movable grid method, when the mesh quality has been reduced by a certain level, the meshing around the robot is summoned to the WB environment and after performing the new meshing, it is returned to the CFX environment to continue the simulation. To derive the hydrodynamic coefficients of the autonomous underwater vehicle, a sinusoidal maneuver at three degrees of freedom is selected for simulation in CFX software. The collected results from the sine maneuver simulation are applied as input to the Kalman filter estimator code. The hydrodynamic coefficients whose extraction is desired, are defined as unknown parameters in the robot control equations. In this maneuver the hydrodynamic coefficients have been extracted with good accuracy. Also to improve the Matlab code and increase the accuracy of extracting hydrodynamic coefficients, the control equation are written in the matrix form. Thus, the number of extracted coefficients are decreased but the coefficients are extracted with higher accuracy.