The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

Behavior-based planning and prosecution architecture for Autonomous Underwater Vehicles in Ocean Observatories

This paper discusses the autonomy framework proposed for the mobile instruments such as Autonomous Underwater Vehicles (AUVs) and gliders. Paper focuses on the challenges faced by these clusters of mobile platform in executive tasks such as adaptive sampling in the hostile underwater environment. Collaborations between these mobile instruments are essential to capture the environmental changes and track them for time-series analysis. This paper looks into the challenges imposed by the underwater communication infrastructure and presents the nested autonomy architecture as a solution to overcome these challenges. The autonomy architecture is separated from the low-level control architecture of these instruments, which is called the `backseat driver'. The back-seat driver paradigm is implemented on the Mission Oriented Object Suite (MOOS) developed at MIT. The autonomy is achieved by generating multiple behaviors (multiple objective functions) linked to the internal state of the platform as well as the environment. Optimization engine called the MOOS-IvP is used to pick the best action for the given instance based on the mission at hand. At sea operational scenarios and results are presented to demonstrate the proposed autonomy architecture for Ocean Observatory Initiative (OOI). Keywords: Autonomous Underwater Vehicles (AUVs), Underwater Gliders, MOOS, MOOS DB, MOOS-IvP, OOI-CI, behavior-based autonom

Effect of Communication Delays on the Successful Coordination of a Group of Biomimetic AUVs

In this paper, the influence of delays on the ability of a formation control algorithm to coordinate a group of twelve Biomimetic Autonomous Underwater Vehicles (BAUVs) is investigated. In this study the formation control algorithm is a decentralized methodology based on the behavioural mechanisms of fish within school structures. Incorporated within this algorithm is a representation of the well-known and frequently used communication protocol, Time-Division-Multiple-Access (TDMA). TDMA operates by assigning each vehicle a specific timeslot during which it can broadcast to the remaining members of the group. The size of this timeslot varies depending on a number of operational parameters such as the size of the message being transmitted, the hardware used and the distance between neighbouring vehicles. Therefore, in this work, numerous timeslot sizes are tested that range from theoretical possible values through to values used in practice. The formation control algorithm and the TDMA protocol have been implemented within a validated mathematical of the RoboSalmon BAUV designed and manufactured at the University of Glasgow. The results demonstrate a significant deterioration in the ability of the formation control algorithms as the timeslot size is increased. This deterioration is due to the fact that as the timeslot size is increased, the interim period between successive communication updates increases and as a result, the error between where the formation control algorithm estimates each vehicle to be and where they actually are, increases. As a result, since the algorithm no longer has an accurate representation of the positioning of neighbouring vehicles, it is no longer capable of selecting the correct behavioural equation and subsequently, is unable to coordinate the vehicles to form a stable group structure

Improving the energy efficiency of autonomous underwater vehicles by learning to model disturbances

Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller. © 2013 IEEE

An Autonomous Surface Vehicle for Long Term Operations

Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto

Adaptive depth control algorithms for an autonomous underwater vehicle

Research on Autonomous Underwater Vehicle (AUV) has been increasing in the recent years. These robotics have the ability to revolutionize access to the oceans to address critical problems facing the marine community such as underwater search and mapping, water column observations climate change assessment, marine habitat monitoring, and underwater mine detection. In this thesis an adaptive nonlinear controller for steering the dynamic model of an autonomous underwater vehicle (AUV) onto a predefined path at a constant forward speed along a desired path is being presented. In this we have used two different controllers for dive plane control of an AUV. In one case, the diving dynamics of an AUV is derived under the assumptions that the pitch angle of AUV is small in the diving plane motion of the vehicle. Autonomous Underwater Vehicles’ dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to determine due to the variations of the hydrodynamic coefficients with different operating conditions of the environment. These kinds of difficulties cause modeling inaccuracies of AUV’s dynamics. So in order to achieve robustness against parameter uncertainty, system identification technique based on Model Reference Adaptive Controller (MRAC) using MIT rule and Fuzzy Logic Controller (FLC) is employed for modeling the AUV dynamics. In this thesis MRAC technique is being proposed for the dynamics control and for the kinematics control of an Autonomous Underwater Vehicle we have used the FLC .Simulation results are being shown which shows effective dive-plane control in spite of the dynamic uncertainties. However in the second case we have proposed a dive plane controller based on Lyapunov theory and Backstepping techniques, where the dynamics of the AUV is being considered without keeping any restricting assumptions on AUV’s pitch angle