Diving Autopilot Design for Underwater Vehicles Using an Adaptive Neuro-Fuzzy Sliding Mode Controller

The problem of motion control of underwater vehicles in both the fully actuated and under-actuated configurations is often confronted by the marine technology community. This paper presents a nonlinear control method for autonomous underwater vehicles (AUVs) traveling along a planned planar path in both actuation configurations. The common objectives of path-following control for both fully actuated and under-actuated vehicles are described, and the differences in the necessary path-following control designs are analyzed, showing that the side-slip angle of the vehicle plays an important role in the evolution of the dynamics of AUVs with different actuation configurations. Based on the presented analysis, nonlinear controllers for the two types of AUV configurations are proposed, and the inherent characteristics of under-actuation and full actuation are revealed by a dedicated analysis of numerical simulation paradigms, the results of which will be instrumental in guiding marine technology engineers in the practical design and control of AUVs.

The subject of the paper is the control of vertical motion of the underwater vehicle operation under ice cover. Underwater vehicles are usually controlled by planes and/or by water ballasting using a compensating tank. But, taking into account the very low allowed operation speed of underwater vehicles near ice cover, ballasting remains the mostly effective way of control. The vertical motion is influenced by the vehicle’s residual buoyancy which depends on two variables: (1) a pressure hull volume reduction depth-wise, (2) seawater density variation depth-wise, as well as (3) a vertical force impulse induced by an unexpected contact with ice cover. The control of vertical motion of the vehicle may be done better by use of seawater density current information, which enhances the reliability and controllability of underwater vehicle maneuvering. Also the information on a vertical distance to ice cover and a corresponding vertical velocity shall be used for reduction of the vehicle vertical speed, i.e. for diminution of ice contact force impulse. So, the instrumentation for the underwater vehicle for seawater density metering and also for measuring of vertical distance to ice cover is very useful for effective motion control of underwater vehicle The control laws for the compensating tank ballast control are developed in the paper. To demonstrate the advantages of the developed control method, a computer simulation of underwater vehicle vertical maneuvers under ice cover is performed.

Unmanned, underwater vehicles have been developed considerably in recent years. Remotely operated vehicles (ROVs) are increasingly used for routine inspection and maintenance tasks but have a range that is limited by the umbilical cable. For long range operations, such as oceanographic exploration and surveying, autonomous underwater vehicles (AUVs) are emerging which haveon-board power and are equipped with advanced control capabilities to carry out tasks with the minimum of human intervention. AUVs typically resemble torpedoes in that mosthave control surfaces and a single propulsion unit, and must move forwards to manoeuvre. Such vehicles are called flight vehicles. This paper describes techniques which are candidates for control of a flight AUV and identifies controllers used on some existing vehicles. Since underwater vehicle dynamics are nonlinear, fuzzy logic and sliding mode control were felt to have promise for autopilot application due to their potential robustness. Following development using a comprehensive simulation programme, the controllers were tested using the experimental vehicle, Subzero II, and their performance compared with that of a classical linear controller. The relative merits of the methods for practical implementation are discussed.

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine.

Considering the effects in the gravity, buoyancy, thrust and hydrodynamic on the underwater vehicle, based on the perspective of the dynamic control, established a relatively complete dynamic model of underwater vehicle, analyzed and designed the control system on this base. The control system is consisted of two control loop. Dynamic compensation of the within control loop based on the dynamic characteristic of the vehicle, by the role of the within control loop, the vehicle became an easy to control and a decoupled linear system. Outer control loop achieved a negative feedback control through the use of proportional and differential item on the actual vehicle pose and the posture deviation expected. Adjusted by adjusting the parameter matrix Kd, Kpcan get the desired attenuation of the error, which can achieve precise motion control of underwater vehicles. Simulation results show that: the control model, in the paper, can be built for dynamic control of underwater vehicles, there is a strong anti-interference ability, can better realize the theory of time-varying trajectory tracking.

In this paper, a new approach to stability analysis of nonlinear dynamics of an underactuated autonomous underwater vehicle (AUV) is presented. AUV is a highly nonlinear robotic system whose dynamic model includes coupled terms due to the hydrodynamic damping factors. It is difficult to analyze the stability of a nonlinear dynamical system through Routh's stability approach because it contains nonlinear dynamic parameters owing to hydrodynamic damping coefficients. It is also difficult to analyze the stability of AUVs using Lyapunov's criterion and LaSalle's invariance principle. In this paper, we proposed the extended-Routh's stability approach to verify the stability of such nonlinear dynamic systems. This extended-Routh's stability approach is much easier as compared to the other existing methods. Numerical simulations are presented to demonstrate the efficacy of the proposed stability verification of the nonlinear dynamic systems, e.g., an AUV system dynamics.

Non-parametric system identification with Gaussian Processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with low amount of data. Multi-output Gaussian processes and its aptitude to model the dynamic system of an underactuated AUV without losing the relationships between tied outputs is used. The simulation of a first-principles model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes are compared with the popular technique of recurrent neural network show that Multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing a measurement of confidence.

Two wheeled self balancing robots have dynamic behaviour and are unstable in nature. Due to this, stabilizing of these robots has become a field of interest to many researchers. Around the equilibrium point of two wheeled self balancing mobile robot, the pitch angles are very small. Hence the balancing is achieved commonly by using linear control methods such as LQR(Linear Quadratic Regulator) control, PID(Proportional Integral Derivative) control etc. But the robots with large pitch angles tends to be in a region of high nonlinear behavior. Hence the linear control techniques fails in maintaining the balance of the robot. To remedy this problem and for improving the performances of the robot, an SDRE(State Dependent Riccati Equation) controller is designed along with SDC (State Dependent Coefficient) Matrix. Simulations are done to check the stability using SDRE controllers. A comparison is done using SDRE, PID and LQR controllers for the system with nonlinear dynamics. Also a comparison is done using PID and LQR controllers for the system with linear dynamics. From the simulations it is clear that SDRE controller shows excellent balancing for the two wheeled self balancing robot when compared to PID and LQR controllers.

This article discusses a challenge of the hydrodynamic coefficient identification in autonomous underwater vehicles (AUVs). The considered coefficients correspond to a nonlinear mathematical model of an AUV maneuvering in the horizontal plane. The dynamics of underwater vehicles is intrinsically nonlinear. Hence, to reproduce different types of maneuvers, such as straight line, zigzag, or turning in circles, a nonlinear mathematical model is important. The proposed identification approach combines an analytical and semiempirical estimation method (ASE), based on the hydrodynamics and geometric characteristics of submarine vehicles, with a parameter estimator based on the extended Kalman filter. The identification starts with ASE estimations, and then the estimator based on the extended Kalman filter adjusts the hydrodynamic coefficient estimates based on the experimental data. The experimental data were obtained with the sensors of the Pirajuba AUV during sea trials. This identification method is a cyclical estimation process, where the less reliable parameters are adjusted to the experimental data while the most reliable parameters are kept fixed. Therefore, in the next identification iteration, the originally fixed model parameters are adjusted to the experimental data, keeping the already identified values. Finally, the identified model is used to simulate the vehicle's movement, and the movement variables are compared to the experimental data, thus validating the identified model.

Design optimization of an AUV for performing depth control maneuver

Autonomous underwater vehicles (AUV) are tasked to ever longer deployments so energy management issues are timely and relevant. Energy shortages can occur due to dynamic ocean conditions that vary temporally and spatially in unpredictable ways. This is compounded by underwater communication challenges. Proposed, is an on-going energy evaluation that assesses the AUV ability to complete the mission through an agent that considers the AUV on-line states, non-linear dynamics, recent learned history, and past history to project an energy shortage. When a shortage occurs an onboard knowledge-based agent re-plans the AUV survey mission using on-line learning with a genetic algorithm given the energy budget, mission duration, and the remaining survey area dimensions. The validated agent is especially effective in the case studied for an energy shortfall resulting from increasing the surveyed area by a factor of 2, for a factor of 2 drop in energy. An agent that effectively monitors and re-plans optimal missions with energy considerations, especially for side scan sonars, is quite novel and increases the operational options of AUVs on long deployments.

Models of the open‐loop dynamics for prototype Remotely Operated Underwater Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) are required for performance prediction and autopilot design. This article describes techniques which can be used to derive these models. The methods may be conveniently grouped into predictive techniques i.e., those which require only vehicle design data and which predict dynamics before the prototype is built and testing techniques i.e., methods which measure open‐loop dynamics from the trials performance of a prototype ROV. The relative benefits and drawbacks of the various methods are examined.

This article develops a novel event-triggered sliding mode control (ETSMC) approach with variable threshold to deal with trajectory tracking matters of autonomous underwater vehicles (AUVs) accompanied by actuator saturation and external disturbances, which can effectively reduce the communication burden between the controller and actuator. The proposed scheme will be very practical when some extreme situations occur. First, the closed-loop system is split into two parts: fixed terms determined by the system itself and nonlinear terms caused by uncertain factors. The nonlinear terms are estimated through adaptive technique. Then the apposite event-triggered mechanism, adaptive laws, and modeled actuator saturation characteristics are designed. The correctness of the presented scheme is illustrated via the stability analysis in the sequel, and the Zeno phenomenon is certificated to be excluded simultaneously. Finally, two different reference motion trajectories are adopted in the simulation experiments, which can indicate that the proposed ETSMC possesses performance superiority and only requires to consume a small amount of communication resources in trajectory tracking control of AUVs. Significance Statement Through the research of this article, we propose a novel event-triggered sliding mode control method with variable threshold applied to autonomous underwater vehicles (AUVs). When conducting ocean exploration work, we usually need the AUVs to follow particular trajectories. By using the proposed method, it can greatly reduce the loss of communication resources inside the system.

This article tackles the finite‐time global trajectory tracking control problem of the autonomous underwater vehicle (AUV) in presence of input saturation constraints, actuator faults, unknown dynamics, and external disturbances. First, we describe the orientation of the AUV by rotation matrix instead of classical Euler angle or unit quaternion such that the AUV's dynamics could be globally formulated without singularity and unwinding phenomenon. After that, a smooth dead zone‐based model is introduced here to linearize the actuator model, leaving that the adaptive laws could be suitable for the solution of input saturation and actuator faults. Considering that the difficulty of model dynamic acquirement, together with the complicity of rotation‐matrix‐based representation, would trouble deployment of the controller. The minimum learning parameter technology is thereby utilized to approximated the dynamic nonlinearity of the AUV. On the basis of these, a rotation‐matrix‐based sliding mode control scheme is technically proposed. It is proved that the tracking errors can be stabilized to a small neighborhood of origin within a settling time. Finally, several set numerical experiments are conducted to assess the effectiveness and show the advantages of the proposed control scheme.