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In marine missions that involve 3D path following tasks, the overall goal of Underwater Vehicles (UVs) is the successful completion of a path previously specified by the operator. This implies that the path must be followed by the UV as closely as possible and arrive at a location for collection by a vessel. In this paper, an Online Interval Type-2 Fuzzy Extreme Learning Machine (OIT2-FELM) is suggested to achieve a robust following behaviour along a predefined 3D path using a Remotely Operated Underwater Vehicle (ROV). The proposed machine is a fast sequential learning scheme to the training of a more generalised model of TSK Interval Type-2 Fuzzy Inference Systems (TSK IT2 FISs) equivalent to Single Layer Feedforward Neural Networks (SLFNs). Learning new input data in the OIT2-FELM can be done one-by-one or chunk-by-chunk with a fixed or varying size. The OIT2-FELM is implemented in a hierarchical navigation strategy (HNS) as the main guidance mechanism to infer local control motions and to provide the ROV with the necessary autonomy to complete a predefined 3D path. For local path-planning, the OIT2-FELM performs signal classification for obstacle avoidance and target detection based on data collected by an on-board scan sonar. To evaluate the performance of the proposed OIT2-FELM, two different experiments are suggested. First, a number of benchmark problems in the field of non-linear system identification, regression and classification problems are used. Secondly, a number of experiments to the completion of a predefined 3D path using an ROV is implemented. Compared to other fuzzy strategies, the OIT2-FELM offered two significant capabilities. On the one hand, the OIT2-FELM provides a better treatment of uncertainty and noisy signals in underwater environments while improving the ROV's performance. Secondly, online learning in OIT2-FELM allows continuous knowledge discovery from survey data to infer the surroundings of the ROV. Experiment results to the completion of 3D paths show the effectiveness of the proposed approach to handle uncertainty and produce reasonable classification predictions (∼90.5% accuracy in testing data).

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot’s energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.

Several strategies to deal with the trajectory tracking problem of Unmanned Underwater Vehicles are encountered, from traditional controllers such as Proportional Integral Derivative (PID) or Lyapunov-based, to backstepping, sliding mode, and neural network approaches. However, most of them are model-based controllers where it is imperative to have an accurate knowledge of the vehicle hydrodynamic parameters. Despite some sliding mode and neural network-based controllers are reported as model-free, just a few of them consider a solution with finite-time convergence, which brings strong robustness and fast convergence compared with asymptotic or exponential solutions and it can also help to reduce the power consumption of the vehicle thrusters. This work aims to implement a model-free high-order sliding-mode controller and synthesize it with a time-base generator to achieve finite-time convergence. The time-base was included by parametrizing the control gain at the sliding surface. Numerical simulations validated the finite-time convergence of the controller for different time-bases even in the presence of high ocean currents. The performance of the obtained solution was also evaluated by the Root Mean Square (RMS) value of the control coefficients computed for the thrusters, as a parameter to measure the power consumption of the vehicle when following a trajectory. Computational results showed a reduction of up to 50% in the power consumption from the thrusters when compared with other solutions.

The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in the literature that exhibits robust performance against external disturbances. However, they are either model-based or require an observer to deal with the disturbances. Moreover, most of them have been evaluated only by numerical simulations. In this paper, the feasibility of a model-free high-order sliding mode controller for the station-keeping problem is validated. The proposed controller was evaluated through numerical simulations and experiments in a semi-Olympic swimming pool, introducing external disturbances that remained unknown to the controller. Results have shown robust performance in terms of the root mean square error (RMSE) of the vehicle position. The simulation resulted in the outstanding station-keeping of the BlueROV2 vehicle, as the tracking errors were kept to zero throughout the simulation, even in the presence of strong ocean currents. The experimental results demonstrated the robustness of the controller, which was able to maintain the RMSE in the range of 1–4 cm for the depth of the vehicle, outperforming related work, even when the disturbance was large enough to produce thruster saturation.

In this paper, an evolutionary General Type-2 Radial Basis Function Neural Network (GT2-RBFNN) for trajectory planning in Remotely Operated underwater Vehicles (ROVs) is suggested. The GT2-RBFNN is used as a data-driven learning system to orient the current position of an ROV in underwater environments. To determine the parameters of GT2-RBFNN, Galactic Swarm Optimisation (GSO) was implemented. A BlueROV2 and a squared water container of 2.5m×2.5m× 3.5m were employed to run all experiments. To control the ROV position, a sensory system that consists of a compass, a micro data sonar, a ping sonar and a pressure sensor was integrated. First, a Proportional Derivative fuzzy controller was implemented to control the depth and yaw positions of the ROV. Secondly, the GT2-RBFNN was applied to discriminate between two different types of contours, i.e. corners and walls in order to follow an obstacle-free trajectory. To compare the efficiency of the GT2-RBFNN, a number of learning techniques that are based on Extreme Learning Machine (ELM) and evolutionary optimisation were implemented. Based on our results, a high trade-off between model simplicity and low computational burden are provided by the GT2-RBFNN.

The perinatal mortality rate is very high throughout the world. A fetal monitor may be used remotely, and this would tackle the problem of continuous monitoring of high-risk pregnancies. There is evidence that current technology is of low reliability, and, therefore, of low precision to identify fetal health. In medical technological implementation, a safe, efficient, and reliable operation must be guaranteed, and the main problem is that remote fetal monitor gathers just a few samples, so the hypothesis of classical theory is not met. We are proposing an approach that improves the data&rsquo, s lack of reliability that accompanies the use of a remote fetal monitor. The method refers to how, by using the existing technologies and the initial experimental data, it is possible to apply probabilistic models that are truly representative of each application. This leads to the characterization of properties of the statistics used to generate a representative probabilistic model without the need to consider the hard suppositions. Results show that, for every case study, it was possible to improve estimations of measurement uncertainty. The proposed method is a useful tool to increase the reliability of medical technology, especially for pieces of equipment where a health care professional is not available.

Submarine gliders are specialized systems used in applications such as environmental monitoring of marine fauna, in the oil industry, among others. The glider launch and capture is a costly process that requires substantial technological and human resources, so the orderly and error-free storage of data is of fundamental importance due to the subsequent analysis. The amount of information being obtained from the seabed is increasing, this leads to the need to develop robust and low-cost ad-hocsystems for this type of application. The challenge is the integration of the different software layers in the storage system because the monitored variables must be ordered according to different glider operations such as calibration data update and navigation. Additionally, to avoid data corruption in the memory chip, error control coding must be used. The goal of this paper is to present a novel design of different layers of software integrated into a datalogger: reception, error control, and storage logic for the different glider operations. The design of the datalogger is based on a NAND flash memory chip and an MSP430 microcontroller. To correct bit-flipping errors, a BCH code that corrects 4 errors for every 255 bits is implemented into the microcontroller. The design and evaluation are performed for different glider operations, and for different lengths and correction capabilities of the BCH module. A test to calculate the storage time has been carried out. This test shows that in the case of 256 bytes per sample, at 30 samples per minute, and 1 GB of storage capacity, it is possible to collect data from the glider sensors for 84 days. The results obtained show that our device is a useful option for storing underwater sensor data due to its real-time storage, power consumption, small size, easy integration, and its reliability, where the bit error rate BER is of 2.4 ×10−11.

Proposed in this paper is a model-free and chattering-free second order sliding mode control (2nd-SMC) in combination with a backpropagation neural network (BP-NN) control scheme for underwater vehicles to deal with external disturbances (i.e., ocean currents) and parameter variations caused, for instance, by the continuous interchange of tools. The compound controller, here called the neuro-sliding control (NSC), takes advantage of the 2nd-SMC robustness and fast response to drive the position tracking error to zero. Simultaneously, the BP-NN contributes with its capability to estimate and to compensate online the hydrodynamic variations of the vehicle. When a change in the vehicle&rsquo, s hydrodynamics occurs, the 2nd-SMC may no longer be able to compensate for the variations since its feedback gains are tuned for a different condition, thus, in order to preserve the desired performance, it is necessary to re-tune the feedback gains, which a cumbersome and time consuming task. To solve this, a viable choice is to implement a BP-NN control scheme along with the 2nd-SMC that adds or removes energy from the system according to the current condition it is in, in order to keep, or even improve, its performance. The effectiveness of the proposed compound controller was supported by experiments carried out on a mini-ROV.

In this work is presented part of the advances in the development of a coastal underwater glider in Mexico by CIDESI, where is shown the design and modelling of the propulsion system, that consist in a variable buoyancy system type piston tank. The main goal of this work is define the main parameters of design that should be taken in account to design a variable buoyancy system and how them are related with the performance of the vehicle and power consumption.

This work describes the development, control and testing of a low cost Micro Autonomous Underwater Vehicle, named AR2D2. The main objective of this work is to apply a nonlinear PD controller to stabilize vehicle attitude and position, by using an Inertial Measurement Unit and a low-cost ultrasonic device. The stability of close loop system with the nonlinear PD controller is proven using a Lyapunovs theory. Therefore, the performance of the micro AUV is validated by simulation and real-time experiments.

For scientific or commercial off shores, the optimization should be in matter of time and costs to make them safer and affordable. Naturally ROV vehicles are exposed to underwater uncertainties and more over to modulability, which means tool or instrumentation change depending on the task to be performed, so their parameters change too and a tuning of the automatic controllers of the vehicle are required. This work proposes a controller composed by Back Propagation Neural Network and Second Order Sliding Mode which make the vehicle adaptable to changes in their parameters: the Neuro Sliding Control. Experimental results of the proposed controller on a small dimension ROV in one degree of freedom are shown, demonstrating its effectiveness.

This work describes the modeling, control and development of a low cost Micro Autonomous Underwater Vehicle (&mu, AUV), named AR2D2. The main objective of this work is to make the vehicle to detect and follow an object with defined color by means of the readings of a depth sensor and the information provided by an artificial vision system. A nonlinear PD (Proportional-Derivative) controller is implemented on the vehicle in order to stabilize the heave and surge movements. A formal stability proof of the closed-loop system using Lyapunov&rsquo, s theory is given. Furthermore, the performance of the &mu, AUV is validated through numerical simulations in MatLab and real-time experiments.

This paper describes the mechanical considerations, electronic architecture and the control strategies of an Autonomous Underwater Vehicle (AUV) Kay Juul H. Real time experiments for pitch and roll control are presented. The applied control technique was the sliding PID control, which main advantages are: second order sliding mode control, model free control and avoids the chattering effect. Satisfactory real time results were obtained for pitch and roll regulation. The contribution of this work is the use of the Sliding PID control in a hybrid glider, this nonlinear control technique has been used in many complex systems including underwater ROVs (Remotely Operated Vehicle).

For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

In this paper we describe the mechanical and electronic design of KAY JUUL, an academic underwater glider, we also show the results from experimental tests developed in a pool. A Sliding PD Control scheme have been applied in the system, due to we don't know the parameters of the KAY JUUL yet, the control output variables were selected based on simulation results and theoretical analysis of the ROGUE, an academic glider developed by Princeton University. Satisfactory results were expected, have been achieved and are presented here.

For a long time, PID-like controllers have been successfully used in academic and industrial tasks. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a need. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool they are fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tuned PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

New technological advancements in the communication channel of time delay teleoperation systems have attracted a lot of attention, which have produced recently new control schemes, under a variety of conditions, including unknown (either constant or varying) time delay and parametric uncertainty. In this paper, a well-known model free, exact differentiator, is used to estimate the full state along with a chattering free second order sliding mode controller to guarantee robust impedance tracking under constant, but unknown, time delay of nonlinear multi-degree of freedom (n - DOF) robots. In addition, a new nominal reference is tailored in order to introduce a change of coordinates in the slave closed-loop dynamics. Experimental results, that validate the predicted behavior, are presented and discussed using the Phantom Premium 1.0 as the master robot and the Catalyst-5 virtual model as the slave robot, whose dynamics is solved online.

This paper presents the improvement of an ultrasonic pulse generator for a pipeline inspection gauge (PIG), which uses 64 transducers for inspecting distances up to 100 km with an axial resolution fixed at 3 mm and variable speeds between 0 and 2 m/s. An ultrasonic pulse generator is composed of a high-voltage (HV) MOSFETs, driver logic and an HV power supply. We used a DC-HV DC converter device as the HV power supply because it reduces the size of the ultrasound system considerably. However, pipeline geometry and inspection effects such as hammer and shock cause a variable pulse repetition frequency (PRF), producing voltage drops, poor quality of the HV pulse generated, failures in the dimensioning of defects and damage to devices by over-voltage. Our improvement is to implement a control scheme to maintain the high quality of the HV regardless of the variable PRF. To achieve this, we characterized three transfer functions of the DC-HV DC converter, varying the connected load to 10%, 45% and 80%. For the characterization, we used the least squares technique, considering an autoregressive exogenous (ARX) model. Later, we compared three control schemes: (1) proportional-integral-derivative (PID) tuned by simultaneous optimization of several responses (SOSR), (2) PID tuned by a neural network (NN) and (3) PI tuned by the analytical design method (ADM). The metrics used to compare the control schemes were the recovery time, the maximum over-voltage and the excess energy when the shock and hammer effects happen to occur. Finally, to verify the improvement of the HV pulser, we compared the ultrasonic pulses generated for various frequencies and amplitudes using the pulse generator with and without the control scheme.

The response of the ROGUE underwater glider controlled by a scheme called Sliding PD control is shown and described. This control law has been widely used in robot manipulators, quadrotors and underwater ROV's. This is a model-free and chattering-free controller that makes motion robust to disturbance and uncertainty. The ROGUE dynamic model calculated on the vertical plane take into account a ballast tank fixed at the buoyancy center, a movable mass capable of motion just along the longitudinal axis and eliminates the offset static point mass. These considerations simplify significantly the equations of motion. Through simulation results this nonlinear controller demonstrates that it ensures a steady glide by directly controlling the movable mass position along with the ballast mass.

This paper was developed by the Department of Automatic Control, CINVESTAV in collaboration with the Center for engineering and Industrial Development CIDESI, Queretaro. This document involves the design and construction of a hybrid underwater Glider. The control objective is to follow a trajectory that is currently implemented by commercial gliders. The dynamic model is used to propose a control strategy based on functions of energy. The used control strategy used is an adaptive control, known as the algorithm of Slotine-Li in its Adaptive version and for the stability proof a candidate Lyapunov function was used. Finally it should be mentioned that we have results on simulation and real-time experiments. In addition the Gliders model is expressed in a matrix form.

This paper presents a dual-user teleoperation scheme to perform a collaborative task using n-DOF nonlinear manipulators as masters and slave. Impedance controllers for the manipulators are implemented in order to achieve a desired dynamic behavior depending on the user's necessities. Furthermore, a sliding mode controller is introduced to cope with the time delay in the communication channels and the uncertainty in the slave. Since the slave teleoperator is in contact with a rigid environment, the slave controller requires a free of chattering control strategy, which makes first order sliding mode teleoperation control unsuitable. Then a higher order sliding mode based impedance controller is proposed to guarantee robust impedance tracking under constant, but unknown time delay. Therefore, a position scaling factor is incorporated to deal with the different workspaces among masters and slave. The validity of the proposed control scheme is demonstrated via experimentation on a 3-DOF dual-user teleoperation system. While the haptic devices Phantom Premium 1.0A and a Phantom Omni are used as masters, a virtual industrial manipulator Catalyst-5 is used as slave. The dual-user system is tested not only in presence of constant unknown time delay in each of the communication channels but also in free and constrained motion regime.

Los vehículos submarinos tele operados tipo ROV ( Remotely Operated Vehicle por sus siglas en inglés) han aumentado su utilidad con el tiempo ya que son capaces de realizar tareas debajo del agua donde un ser humano podría no llegar o ser peligroso…

En el presenta trabajo de tesis, se pretende mediante el desarrollo de un sistema informático ofrecer una plataforma virtual de entretenimiento de pilotos de ROV (Acrónimo del inglés Remote Operated Vehicle, Vehículo operado a distancia).

En este proyecto se realiza el estudio, diseño y evaluación de un algoritmo de autosintonizado de ganancias para el control PID con redes neuronales. En la primera parte del proyecto se explica brevemente qué es un ROV y sus aplicaciones. También se describe el planteamiento del problema que compete a esta tesis, se explica brevemente los antecedentes del uso de redes neuronales como auto-sintonizador, se menciona también el objetivo principal de esta tesis y la hipótesis que se pretendió alcanzar. En el capítulo 2 se presenta el análisis de fundamentos el cual está dividido en subsecciones, como son los antecedentes del ROV KAXAN, análisis del modelo hidridinámico, modelo matemático de las corrientes acuáticas, la dinámica de los propulsores, el uso de ambientes virtuales #D, y por ultimo las redes neuronales como auto-sintonizados de ganancias, en el capítulo 3 sepresentan los resultados del control auto-sintonizado evaluado en simulación en el ambiente virtual #D, también se presenta la evaluación en el banco de pruebas para 1GDL, y finalmente se muestra la implementación real en un mini-submarino sub-actuado llamado NU-UKUL JA.

"En esta tesis se presenta un sistema de teleoperación bilateral, donde el maestro es el dispositivo háptico Phantom Premium 1.0, el canal de comunicación es el protocolo serial UART y el esclavo el submarino Neptune SB-1. El esclavo es instrumentado con un sensor de humedad, una unidad de medición inercial, un sensor de presión y un sensor de fuerza. El sensor de humedad es empleado para detectar alguna fuga de agua dentro del submarino. La unidad de medición inercial (IMU) provee información acerca de la aceleración lineal del vehículo y la velocidad angular. La IMU utilizada es la ADIS16400 de la marca AnalogDevices, que se basa en tres giroscopios y tres acelerometros que permiten hacer la medición del ángulo: , para posteriormente transmitirlo al sistema embebido BeagleBone Black por medio del canal de comunicación SPI. El sensor de fuerza nos permite medir la fuerza de contacto entre el vehículo submarino y el entorno. Esto con el fin, de que el operador humano tenga la la sensación de telepresencia, al recibir la retroalimentación de fuerza producida por contactos sostenidos y colisiones frontales. Eliminando con ello, la necesidad de confiar únicamente en la teleoperación visual. El sensor de presión es utilizado para obtener el valor de profundad a la que se encuentra el vehículo submarino, este sensor se comunica por medio del canal de comunicación I2C. Una de las partes principales del sistema de teleoperación es la recolección de los datos de los sensores, la ejecución del algoritmo de control y la comunicación, tareas que son realizadas por el sistema embebido de bajo costo BeagleBone Black. Adicionalmente, con el fin de evaluar el tiempo de respuesta, el comportamiento del algoritmo de control y la arquitectura electrónica, se llevaron a cabo la identificación del sistema y experimentos de control de profundidad. La identificación del sistema, que describe la dinámica del vehículo en el plano vertical, se obtuvo a partir de datos experimentales del sensor de presión. La función de trasferencia obtenida se utilizó para ajustar las ganancias del algoritmo de control de profundidad. El algoritmo de control empleado es un PD, el cual controla la velocidad de los motores laterales."