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

A survey on uninhabited underwater vehicles (UUV)

ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated

TWINBOT: Autonomous Underwater Cooperative Transportation

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots’ end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles’ limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUV

Velocity control of ROV using modified integral SMC with optimization tuning based on Lyapunov analysis

Remotely Operated Vehicle also known as ROV is a vehicle with high nonlinearity and uncertainty parameters that requires a robust control system to maintain stability. The nonlinearity and uncertainty of ROV are caused by underwater environmental conditions and by the movement of the vehicle. SMC is one of the control systems that can overcome nonlinearity and uncertainty with the given robust system. This work aims to control velocity of the vehicle with proposes the use of modified integral SMC compensate error in ROV and the use of particle swarm optimization (PSO) to optimize the adjustment of SMC parameters. The ROV used in this paper has a configuration of six thrusters with five DoF movements that can be controlled. Modified integral sliding mode is used to control all force direction to increase the convergence of speed error. Adjustment optimization techniques with PSO are used to determine four values of sliding control parameters for five DoF. Using Lyapunov stability approach control law of sliding mode is derived and its global stability proved mathematically. Simulation results are conducted to evaluate the effectiveness of Modified Integral SMC and compared with nonlinear control

A robust dynamic region-based control scheme for an autonomous underwater vehicle

Intelligent control of an autonomous underwater vehicle (AUV) requires a control scheme which is robust to external perturbations. These perturbations are highly uncertain and can prevent the AUV from accomplishing its mission. A well-known robust control called sliding mode control (SMC) and its development have been introduced. However, it produces a chattering effect which requires more energy. To overcome this problem, this paper presents a novel robust dynamic region-based control scheme. An AUV needs to be able not only to track a moving target as a region but also to position itself inside the region. The proposed controller is developed based on an adaptive sliding mode scheme. An adaptive element is useful for the AUV to attenuate the effect of external disturbances and also the chattering effect. Additionally, the application of the dynamic-region concept can reduce the energy demand. Simulations are performed to illustrate the effectiveness of the proposed controller. Furthermore, a Lyapunov-like function is presented for stability analysis. It is demonstrated that the proposed controller works better then an adaptive sliding mode without the region boundary scheme and a fuzzy sliding mode controller

Continuous adaptive sliding-mode control scheme for an autonomous underwater vehicle with region-based approach

Set point method has been typically used for trajectory tracking of Autonomous Underwater Vehicle (AUV). However, this method has several limitations. In this regard, region based method has been applied in trajectory tracking of AUV in order to solve the limitations of set point method. The main idea behind the region-based method is the tracking target of an AUV set as a region, so that the AUV will maintain its position under weak ocean current. This method uses lower energy compared to set point method because the AUV will not turn on its thrusters as long as it maintains its position within the region. Realistically, there is also strong current that can drift vehicle away from the required region. The purpose of the thesis is to develop a robust controller with region-based method. Robust control enables an AUV to reject the disturbance and re-enter the region even under the influence of external disturbance. Based on the literature review, adaptive sliding mode control was chosen as the proposed controller in this study. Sliding mode control is known for its insensitivity towards uncertainty and external disturbance. Adaptive component was introduced to replace switching component. This substitute enables AUV to reject external disturbance better compared to conventional sliding mode control. The stability of the proposed controller was analyzed using Lyapunov function. The energy consumption of region based method was compared with the set point tracking method. It has been shown from this study that the energy consumption for region-based method is indeed lower than set point method. The effectiveness of the proposed controller was compared with adaptive controller using simulation under the influence of ocean current. Underwater vehicle model used in the simulation was Omni Directional Intelligent Navigator (ODIN). It has been proven that the proposed controller performed better compared to adaptive controller. The proposed controller had managed to handle ocean current and re-enter the region

Task-space dynamic control of underwater robots

This thesis is concerned with the control aspects for underwater tasks performed by marine robots. The mathematical models of an underwater vehicle and an underwater vehicle with an onboard manipulator are discussed together with their associated properties. The task-space regulation problem for an underwater vehicle is addressed where the desired target is commonly specified as a point. A new control technique is proposed where the multiple targets are defined as sub-regions. A fuzzy technique is used to handle these multiple sub-region criteria effectively. Due to the unknown gravitational and buoyancy forces, an adaptive term is adopted in the proposed controller. An extension to a region boundary-based control law is then proposed for an underwater vehicle to illustrate the flexibility of the region reaching concept. In this novel controller, a desired target is defined as a boundary instead of a point or region. For a mapping of the uncertain restoring forces, a least-squares estimation algorithm and the inverse Jacobian matrix are utilised in the adaptive control law. To realise a new tracking control concept for a kinematically redundant robot, subregion tracking control schemes with a sub-tasks objective are developed for a UVMS. In this concept, the desired objective is specified as a moving sub-region instead of a trajectory. In addition, due to the system being kinematically redundant, the controller also enables the use of self-motion of the system to perform sub-tasks (drag minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint limits)

Towards automated sample collection and return in extreme underwater environments

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Billings, G., Walter, M., Pizarro, O., Johnson-Roberson, M., & Camilli, R. Towards automated sample collection and return in extreme underwater environments. Journal of Field Robotics, 2(1), (2022): 1351–1385, https://doi.org/10.55417/fr.2022045.In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in search-for-life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators. We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.This work was funded under a NASA PSTAR grant, number NNX16AL08G, and by the National Science Foundation under grants IIS-1830660 and IIS-1830500. The authors would like to thank the Costa Rican Ministry of Environment and Energy and National System of Conservation Areas for permitting research operations at the Costa Rican shelf margin, and the Schmidt Ocean Institute (including the captain and crew of the R/V Falkor and ROV SuBastian) for their generous support and making the FK181210 expedition safe and highly successful. Additionally, the authors would like to thank the Greek Ministry of Foreign Affairs for permitting the 2019 Kolumbo Expedition to the Kolumbo and Santorini calderas, as well as Prof. Evi Nomikou and Dr. Aggelos Mallios for their expert guidance and tireless contributions to the expedition

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Gain range limitation of conventional proportional‐integral‐derivative (PID) controllers has made them unsuitable for application in a delayed environment. These controllers are also not suitable for use in a Wireless Highway Addressable Remote Transducer (WirelessHART) protocol networked control setup. This is due to stochastic network‐induced delay and uncertainties such as packet dropout. The use of setpoint weighting strategy has been proposed to improve the performance of the PID in such environments. However, the stochastic delay still makes it difficult to achieve optimal performance. This chapter proposes an adaptation to the setpoint weighting technique. The proposed approach will be used to adapt the setpoint weighting structure to variation in WirelessHART network‐induced delay through fuzzy inference. Result comparison of the proposed approach with both setpoint weighting and proportional‐integral (PI) control strategy shows improved setpoint tracking and load regulation. For the first‐, second‐ and third‐order systems considered, analysis of the results in the time domain shows that in terms of overshoot, undershoot, rise time, and settling times, the proposed approach outperforms both the setpoint weighting and the PI controller. The approach also shows faster recovery from disturbance effect