Real-time Implementation Research Articles

Extreme learning machine model with honey badger algorithm based state-of-charge estimation of lithium-ion battery

Accurate state-of-charge (SOC) detection was still a challenging task to complete due to complex battery dynamics and constantly changing external conditions. The formula for SOC was difficult to determine since external parameters including voltage, current, temperature, and battery arrangement were complex. Also the methods for estimating SOC that were already in use were not always appropriate for the same car operating in various road and climatic conditions. In all situations, the conventional methodologies did not deliver an accurate estimation performance. Here, a unique optimization-based Extreme Learning Machine (ELM) was created to accurately determine a battery's SOC and enhance the operation and safety of battery systems. A lithium ion battery was first created, and data on its current, voltage, SOC, capacity, duration, and discharge rate were gathered to produce a real-time dataset at several temperatures, including 00,250 and 450. The dataset underwent additional pre-processing to standardize the values and enhance the accuracy of the data. To determine the precise state of the battery, these pre-data were loaded into the ELM model. However, the performance of ELM was significantly influenced by the length of training and the number of neurons in a hidden layer. An advanced Honey Badger Optimization Algorithm (HBA) was used to choose the appropriate hidden neurons and increase the estimation accuracy in order to overcome this problem. The proposed SOC estimation model provides 97% accuracy in the FUDS drive cycle and 99% accuracy in the US06 drive cycle. The proposed model provides a well performance for estimating SOC in lithium-ion battery at various temperature, also the proposed model was fit for real time implementation.

Deep-Learning-Based High-Intensity Focused Ultrasound Lesion Segmentation in Multi-Wavelength Photoacoustic Imaging.

Photoacoustic (PA) imaging can be used to monitor high-intensity focused ultrasound (HIFU) therapies because ablation changes the optical absorption spectrum of the tissue, and this change can be detected with PA imaging. Multi-wavelength photoacoustic (MWPA) imaging makes this change easier to detect by repeating PA imaging at multiple optical wavelengths and sampling the optical absorption spectrum more thoroughly. Real-time pixel-wise classification in MWPA imaging can assist clinicians in monitoring HIFU lesion formation and will be a crucial milestone towards full HIFU therapy automation based on artificial intelligence. In this paper, we present a deep-learning-based approach to segment HIFU lesions in MWPA images. Ex vivo bovine tissue is ablated with HIFU and imaged via MWPA imaging. The acquired MWPA images are then used to train and test a convolutional neural network (CNN) for lesion segmentation. Traditional machine learning algorithms are also trained and tested to compare with the CNN, and the results show that the performance of the CNN significantly exceeds traditional machine learning algorithms. Feature selection is conducted to reduce the number of wavelengths to facilitate real-time implementation while retaining good segmentation performance. This study demonstrates the feasibility and high performance of the deep-learning-based lesion segmentation method in MWPA imaging to monitor HIFU lesion formation and the potential to implement this method in real time.

Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions

The hybridization of multiple energy sources is crucial for electric vehicles to deliver the same performance as modern fossil fuel-based automobiles. This paper presents a torque and speed control strategy for an electric vehicle DC traction motor by regulating the power flow from two energy sources, namely battery and supercapacitor systems, to resist unpredictable disturbances. A control system with three control loops is applied to regulate the speed of the traction motor. The outer speed control loop is a nonlinear state feedback controller with a disturbance observer, which is capable of handling non-linear systems with unpredictable disturbances. The inner voltage and current control loop are precisely tuned PI controllers. Using an adaptive energy management method that varies depending on the state of charge of the source, the total reference current needed to support the load demand is split between two sources. A laboratory prototype system model is generated, and the performance is analyzed under motoring and regenerative braking conditions. For monitoring and controlling the system, the dSPACE DS1202 real-time simulator is employed. The performance of the proposed control system is evaluated and it is found that the settling time to settle within a tolerance band of 1% is 0.75 s.

Development and testing of a ground recognition system for tractor field operations

To overcome the problems of poor recognition accuracy, slow response speed, and unsuitable for farmland ground environment based on the traditional ground recognition method of vehicle mathematical model, this paper proposes a ground recognition system for tractor field operation based on wheel acceleration. A wheel acceleration sensor and data acquisition device are designed, and a ground recognition algorithm based on Principal Component Analysis - Genetic Algorithm optimization of an Extreme Learning Machine (PCA-GA-ELM) is investigated. The system can collect acceleration samples and extract statistical features in different frequency bands in real time according to the cycle of wheel rotation and then use the trained recognition model to recognize the ground type. In the actual operation process, this system only needs to collect the acceleration test data of one rotation of the tractor wheels to achieve the recognition of the ground. The test set data show that this system achieves 96 % discrimination accuracy, higher than the 87 % of the ELM model without genetic algorithm optimization and 92 % of the GA-BP neural network model. In addition, the real-time ground recognition test shows that the increase in operation speed will slightly reduce the accuracy of the recognition model, but the overall can still maintain 94–98 % accuracy, indicating that the system has good stability. This system can provide a reference for real-time speed adjustment, implement position, and other control parameters during tractor operation.

Scaled Conjugate-Artificial Neural Network-Based novel framework for enhancing the power quality of Grid-Tied Microgrid systems

The exponential increase in the dependence on electrical power by rapid industrialization and increasing population has given rise to the need of supplying electric power efficiently and with good power quality (PQ). The evolution of renewable energy sources and their increased penetration into traditional grid systems has increased the complexity of electric power transmission and distribution. The significant increase in the use of complex converters, Flexible AC Transmission System devices and complex nonlinear loads in modern power systems have amplified the issues associated with the production and distribution of electricity having proper PQ. The proper functioning of the power system network requires healthy quality of power, maintained both at the load and source end. The Dynamic Voltage Restorer is a prospective D-FACTS (Distribution Flexible AC Transmission System) device that has been widely incorporated for addressing the PQ issues caused by irregularities in the distribution grid's voltage, current, or frequency. This research article attempts to enhance PQ indices of Photovoltaic, Fuel Cell, DVR and energy storage (battery) based Microgrid systems through a proposed Scaled Conjugate based Artificial Neural Network (SC-ANN) in the Matlab/Simulink architecture. The suggested technique’s effectiveness is verified by introducing severe PQ faults such as sag and swell and numerous power system responses have been comprehensively studied by comparing them with traditional Fuzzy Logic Controller and Proportional Integral controllers. The results obtained validates the efficiency of the proposed controller over FLC and PI methods in maintaining the system power indices constant during the onset of PQ faults by reducing harmonics and oscillations thereby enhancing the overall system reliability, efficiency and stability paving its way for real-time implementation in the areas of MG system.

In Cabin Driver Monitoring and Alerting System For Passenger cars using Machine Learning.

The primary causes of road accidents are driver errors. Such as excessive speeding, driver distraction, and driver sleepiness. Diver sleepiness and distraction can be controlled by implementing advanced technology such as an in-cabin driver monitoring system. This is a technological solution that uses a machine learning algorithm to improve road safety. This paper describes a new real-time driver monitoring and alerting system that solely monitors driver Sleepiness, diver distraction, and seatbelt wearing status in order to prevent road accidents. It employs the YOLOv5 deep learning algorithm to detect various types of distraction and For seatbelt wearing status and computer vision’s cvzone framework is used to detect sleepiness using 3D facial landmarking. In this system, The camera is mounted in the centre of the vehicle’s dashboard for data collection and real-time implementation. The Raspberry Pi 3b module is used for the Realtime implementation. According to the findings of this study, the system is capable of real-time implementation, and the machine learning model used in the study achieves an accuracy of more than 90%.

Selective Harmonic Mitigation: Limitations of Classical Control Strategies and Benefits of Model Predictive Control

Selective harmonic mitigation pulsewidth modulation (SHMPWM) combined with model predictive control (MPC) is a promising approach for grid-connected power converters. SHMPWM can guarantee grid code compliance in steady state, e.g. grid harmonic injection, with a reduced output converter filter, while MPC improves dynamic response and allows grid code compliance in the event of grid transients. This paper presents a survey of the MPC strategies already published in the literature developed for their use with SHMPWM. The existing strategies fall into two categories: direct model predictive control with an implicit selective harmonic mitigation modulator, and direct model predictive control based on finite control set (FCS-MPC). One representative control strategy of each group is compared to each other and to the performance of classical proportional- integral (PI) controllers combined with SHMPWM. The goal is to identify the potential benefits of MPC for grid-connected power converters, and determine the main advantages and limitations of the two selected state-of-the-art control strategies. Their performance is assessed through Hardware-in-the-Loop (HIL) experimental results in terms of real-time implementation, harmonic content grid code compliance, dynamic response and performance under grid transients.

Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control

This article presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power grid. The approach involves a novel data-driven “basis-dictionary free” lifting of the system dynamics into a higher dimensional linear space over which a model predictive control is exercised, making it both scalable and rapid for practical real-time implementation. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Koopman-inspired deep neural network</i> (KDNN) encoder–decoder architecture for the linear embedding of the underlying networked dynamics under distributive control is presented, in which the end-to-end components of the KDNN, comprising of a triple of transforms is learned from the system trajectory data in one go: a neural network (NN)-based lifting to a higher dimension, a linear dynamics within that higher dimension, and an NN-based projection to the original space. This data-learned approach relieves the burden of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> selection of the nonlinear basis functions (e.g., radial or polynomial) used in conventional approaches for lifting to a higher dimensional linear space. We validate the efficacy and robustness of the approach via application to the standard IEEE 39-bus system.

The Use of Model-Predicted Internal States from Locally Linear, Gain-Scheduled, Reduced-Order Physics-Based Models in Model-Predictive Control Strategies

The objective of the research is to develop reduced-order state-space models that can predict internal states that are not directly observable with existing sensors. In doing so, battery management systems (BMS) can make real-time control decisions based upon sensor inferences. For example, estimating local electrode Li and electrolyte Li-ion concentrations or local electrostatic potential differences between electrode and electrolyte phases offer valuable knowledge in predicting behaviors such as Li plating, dendrite growth, or electrode fracture. Such indirect predictive capabilities, for example, can enable fast-charging protocols that avoid damage mechanisms.This talk explores the mathematical feasibility of estimating local Li concentrations and electrostatic potentials using observed current-voltage sequences. The approach for determining internal battery states begins with an estimator that uses observed input-output data from a physics-based reduced-order state-space model. If the model satisfies the property of observability, a sufficiently long window of input-output data can predict unique internal-state trajectories that are compatible with the observable data. The research considers understanding the effects of sampling time and state of charge on internal-state observability.Efficient estimation algorithms must incorporate battery physics and chemistry yet be sufficiently simple as to run in real-time on a battery management system (BMS). The present research first develops a physics-based pseudo-two-dimensional (P2D) model, which describes battery thermodynamics, transport, and kinetics, results in a system of nonlinear, coupled, differential-algebraic equations that can be solved computationally, but too slow for real-time implementation. The P2D model is then reduced to a gain-scheduled, locally linear, state-space model that can be run in real-time. Both models are implemented within the MATLAB/Simulink framework that facilitates development and evaluation of control strategies.

Формування динамічних моделей газотурбінних двигунів для використання в системах автоматичного керування та контролю

The subject of the study is the process of forming a mathematical model (MM) of an aviation gas turbine engine, which provides the calculation of parameters of the work process in stable and transient operating modes for use in the evaluation of dynamic characteristics, in the analysis and synthesis of automatic engine and aircraft control systems, as part of aviation simulators, and in on-board control and diagnostics algorithms. The goal is to substantiate the structure and methodology of MM formation, intended for use in real and accelerated time scale systems. Task: formulation of requirements for MM, substantiation of the interaction between static and dynamic submodels, substantiation of the composition of arguments and the way of considering the influence of external conditions, as well as the position of the elements of the mechanization of the gas flow duct. For this, the methods of the theory of airjet engines are used. The following results were obtained: the requirements for MM of aircraft gas turbine engines designed to solve the problems of engine and aircraft control were formulated, and the structural forms of MM were substantiated, which ensure high accuracy of modeling with minimal complexity and the possibility of real-time implementation. The scientific and practical novelty of the obtained results is as follows: the requirements for dynamic MM of aircraft engines have been summarized, the problems of structural implementation and combination of static and dynamic submodels, the rational selection of their input parameters and considering the influence of external conditions on static and dynamic characteristics have been analyzed, the software implementation of MM in in the Matlab Simulink environment of a two-shaft turbofan engine and compared the simulation results obtained using the developed simplified MM and the original nonlinear thermo-gas-dynamic model based on the solution of the joint operation equations of components. It is shown that in the working range of modes, the MM error does not exceed 5%, the dynamic errors of the rotor rotation frequencies are less than 4%, the compressor pressure and gas temperature are less than 7%, or the thrust is less than 10%. Errors in estimating the duration of acceleration and deceleration of the engine are within 0.2...0.6 s.

The ascent of network traffic classification in the dark net: A survey

The Darknet is a section of the internet that is encrypted and untraceable, making it a popular location for illicit and illegal activities. However, the anonymity and encryption provided by the network also make identifying and classifying network traffic significantly more difficult. The objective of this study was to provide a comprehensive review of the latest advancements in methods used for classifying darknet network traffic. The authors explored various techniques and methods used to classify traffic, along with the challenges and limitations faced by researchers and practitioners in this field. The study found that current methods for traffic classification in the Darknet have an average classification error rate of around 20%, due to the high level of anonymity and encryption present in the Darknet, which makes it difficult to extract features for classification. The authors analysed several quantitative values, including accuracy rates ranging from 60% to 97%, simplicity of execution ranging from 1 to 9 steps, real-time implementation ranging from less than 1 second to over 60 seconds, unknown traffic identification ranging from 30% to 95%, encrypted traffic classification ranging from 30% to 95%, and time and space complexity ranging from O(1) to O(2n). The study examined various approaches used to classify traffic in the Darknet, including machine learning, deep learning, and hybrid methods. The authors found that deep learning algorithms were effective in accurately classifying traffic on the Darknet, but the lack of labelled data and the dynamic nature of the Darknet limited their use. Despite these challenges, the study concluded that proper traffic classification is crucial for identifying malicious activity and improving the security of the Darknet. Overall, the study suggests that, although significant challenges remain, there is potential for further development and improvement of network traffic classification in the Darknet.

Model-based and model-free collision detection and identification for a parallel Delta robot with uncertainties

This paper presents two methods for human collision detection and identification for a parallel Delta robot considering uncertainties. In the model-based, a generalized momentum observer based on the explicit model of the Delta robot is used to estimate a proposed uncertainty model without collision. A linear regression algorithm is approached to recognize actuator hysteresis friction models and modeling error parameters, constructing the uncertainty model. The boundary and convergence of the identification parameters are guaranteed based on a least square method. Then, the estimated uncertainty is compared with the generalized momentum observer to detect and identify collision impacts as external torques. In the model-free, the neural network model for actuated torque prediction controlling the Delta robot is developed based on a model-driven and data-driven approach. The training procedure of the proposed network only uses no collision datasets. The model-free method uses a neural network to provide detection signals and external torques identified by comparing its conservative predictions to the measured torques when a collision occurs. In this work, only low-cost current sensors and low-resolution encoders are attached to the Delta robot system for generating the datasets. Finally, the identification capacity of the proposed methods is also compared with a force sensor to verify the effectiveness in real-time implementation.

Simplified adaptive backstepping control for uncertain nonlinear systems with unknown input saturation and its application

The calculation complex problem is one of the significant obstacles in the real-time implementation of traditional backstepping controllers. In this paper, we consider the simplification problem of the adaptive backstepping tracking controller for uncertain nonlinear systems with unknown input saturation. Compared with traditional backstepping controllers, virtual stabilizing functions and their derivatives are needless in our controller, which effectively reduces the computation burden. And then the one-order compensation subsystem is employed to eliminate the effect of nonlinear uncertainties based on the adaptive control scheme and the congelation of variables technique. Next, the performance of the closed-loop nonlinear system is analyzed based on the Lyapunov stability theorem. In the final, simulation and experimental results are given to show the effectiveness of our controller.

Real-Time Fixed Priority Scheduling Synthesis using Affine DataFlow Graphs: from Theory to Practice

The major drawback of using static schedules to execute dataflow applications is their high inflexibility. In real-time systems, periodic schedules make it easier to assert safety guarantees and to decrease the schedule size, but their characteristics remain hard to compute. This article presents an approach to automatically generate fixed priority schedules from a dataflow specification. To do so, precedence dependencies between actors in the dataflow graphs are abstracted, as well as the task periods, by using affine relations . This abstraction allows us to synthesize schedules efficiently considering two main objectives: the maximization of throughput and the minimization of buffer sizes. Given a dataflow graph to execute in a real-time environment, we transform it into an Affine DataFlow Graph (ADFG) and compute the task priorities, their mapping, the number of delays in the buffers, and the buffer sizes. This article is the first to present an overview of both theoretical and practical aspects of ADFG. On the theoretical side, it presents corrections and improvements on the fixed priority case. On the practical side, benchmark evaluations demonstrate the robustness and maturity of the approach that our scheduling synthesizer implements. Synthesized schedules are evaluated by using scheduling simulation and real-time implementation. Last but not least, the synthesized periods reach the optimal throughput if enough processors are available, and most of the time the periods reach the maximal processor utilization factor in the uni-processor case. Moreover, execution time of the synthesis is about only one second for the main proposed algorithms.

Optical Time-Mapped Spectrograms (II): Fractional Talbot Designs

Real-time implementations of joint time-frequency analysis over instantaneous bandwidths above the GHz range remain challenging. In a companion paper, we have proposed an analog photonic processing scheme that enables computing a short-time Fourier transform (STFT), or spectrogram (SP), of an incoming arbitrary broadband signal, over tens-of-GHz analysis bandwidths, in a continuous, gapless and real-time manner. The proposed method involves a temporal sampling of the signal under test (SUT) with a periodic train of interfering, linearly chirped optical pulses followed by group-velocity dispersion to map the spectra of consecutive and overlapping truncated sections of the SUT along the time domain. This scheme offers a notable design versatility to customize the performance specifications of the computed SP, but it generally involves a sub-optimal non-uniform sampling of the SUT and it requires the use of a bulky and expensive pulsed optical source. In this communication, we show that this previous general scheme can be easily configured to ensure an optimal, uniform sampling of the SUT by simply setting the involved dispersive lines to satisfy a fractional self-imaging condition, while keeping all the advantages (e.g., design versatility) of the original scheme. Moreover, the resulting design is further adapted to entirely avoid the need for a pulsed source, using instead a more efficient and simpler phase-only temporal sampling of the SUT, e.g., implemented through electro-optic phase modulation. We derive the design conditions and performance trade-offs of the proposed time-mapped STFT schemes based on dispersion-induced fractional Talbot self-imaging. Through numerical simulations and experimental demonstration, we confirm the potential of this simple and efficient approach for real-time SP analysis of arbitrary signals over instantaneous bandwidths above a few tens of GHz, with MHz frequency resolutions and ultrahigh processing speeds, approaching billions of FTs per second.

Current Limit Avoidance Algorithms for DEMO Operation

Tokamaks are the most promising devices to prove the feasibility of energy production using nuclear fusion on Earth which is foreseen as a possible source of energy for the next centuries. In large tokamaks with superconducting poloidal field (PF) coils, the problem of avoiding saturation of the currents is of paramount importance, especially for a reactor such as the European demonstration fusion power plant DEMO. Indeed, reaching the current limits during plasma operation may cause a loss of control of the plasma shape and/or current, leading to a major disruption. Therefore, a current limit avoidance (CLA) system is essential to assure safe operation. Three different algorithms to be implemented within a CLA system are proposed in this paper: two are based on online solutions of constrained optimization problems, while the third one relies on dynamic allocation. The performance assessment for all the proposed solutions is carried out by considering challenging operation scenarios for the DEMO reactor, such as the case where more than one PF current simultaneously saturates during the discharge. An evaluation of the computational burden needed to solve the allocation problem for the various proposed alternatives is also presented, which shows the compliance of the optimization-based approaches with the envisaged deadlines for real-time implementation of the DEMO plasma magnetic control system.

Electrochemical and Optical Sensors for Real-Time Detection of Nitrate in Water.

The health and integrity of our water sources are vital for the existence of all forms of life. However, with the growth in population and anthropogenic activities, the quality of water is being impacted globally, particularly due to a widespread problem of nitrate contamination that poses numerous health risks. To address this issue, investigations into various detection methods for the development of in situ real-time monitoring devices have attracted the attention of many researchers. Among the most prominent detection methods are chromatography, colorimetry, electrochemistry, and spectroscopy. While all these methods have their pros and cons, electrochemical and optical methods have emerged as robust and efficient techniques that offer cost-effective, accurate, sensitive, and reliable measurements. This review provides an overview of techniques that are ideal for field-deployable nitrate sensing applications, with an emphasis on electrochemical and optical detection methods. It discusses the underlying principles, recent advances, and various measurement techniques. Additionally, the review explores the current developments in real-time nitrate sensors and discusses the challenges of real-time implementation.

Conditioned adaptive barrier-based double integral super twisting SMC for trajectory tracking of a quadcopter and hardware in loop using IGWO algorithm

Researchers working in the field of unmanned aerial vehicles are observing rapid developments in the field, such as the use of quadrotors to grasp, manipulate, and transport objects. These advancements are made possible through sophisticated control systems, new types of actuation and sensing technologies, and a greater understanding of the aerodynamic effects and gyroscopic moment generated by the rotating blades of the quadrotor involved. Therefore, this research work proposes four novel optimized control laws, i.e., conditioned adaptive barrier-based double integral super twisting sliding mode controller (CABDIST-SMC), BF double integral SMC (BF-DISMC), BF integral SMC, and barrier function-based SMC control laws, for attitude, heading, position, and altitude trajectory tracking of the quadcopter. The non-linear model of the quadcopter is developed using the Lagrange formalism in MATLAB ODE-45 environment, including the gyroscopic moments and aerodynamic effects, and a Lyapunov stability analysis is performed to ensure the asymptotic stability of the system. A 3D-helical complex trajectory is employed to analyze the performance and consistency of the proposed optimized controllers. For performance comparison, the proposed four variants of the SMC are compared with three traditional control strategies, i.e., backstepping SMC, optimized adaptive SMC via particle swarm optimization algorithm, and improved adaptive SMC. These traditional control strategies have also been developed and tested in MATLAB-Simulink. All the controllers are optimized using the improved grey wolf optimization algorithm, and to determine the best control law, six performance indexes, i.e., mean absolute percentage error (MAPE), root mean square error (RMSE), integral square error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and integral time square error (ITSE) are used. A hardware-in-loop (HIL) test is also performed to validate the performance of the designed novel control law. The C2000 Delfino MCU F28379D launchpad is used for performing the HIL validation test. The simulated results showed that the performance of optimized CABDIST-SMC surpassed all other control laws for altitude, heading, position, and attitude trajectory tracking. It has achieved MAPE of 0.02%, RMSE of 3.9912e−5, ISE of 3.1859e−8, IAE of 7.9823e−4, ITAE of 0.0080, and ITSE of 3.1849e−7 in case of yaw angle control. The proposed control laws have successfully passed the HIL validation test, and real-time implementation of the proposed control laws indicates no significant variations compared to the simulated results.

Field Oriented Control of Brushless Direct Current Motor for Electric Vehicle Robot Obstacle Detection and Avoidance

This paper presents an approach to implement Field Oriented Control (FOC) brushless directcurrent motor using PID Controller for electric vehicle obstacle detection and avoidance. A state spacemodel of the BLDC motor with FOC was model and simulated using MATLAB/Simulink to validate thestability of the proposed system. The overall system was validated in real time implementation using microcontroller and Ultrasonic Sensor. The detection and recognition of objects, as well as making anautonomous decision to change its trajectory which is based on predetermined threshold distance of 5cmwere analyzed.

Collision avoidance fault-tolerant control for dynamic positioning vessels under thruster faults

This paper explores a collision avoidance fault-tolerant control scheme for dynamic positioning vessels in the presence of velocity constraints and thruster faults. First, the collision avoidance problem for multiple static obstacles is described as security zone constraint and integrated into the controller design via an artificial potential function. Second, an indirect error vector including the gradient of potential function and vessel velocity information is constructed, and a time-varying asymmetric barrier Lyapunov function is adopted to concurrently guarantee the satisfaction of velocity constraints. With the help of robust adaptive techniques, the derived controller not only provides robustness against unknown parameters affections, ocean disturbances, and thruster faults, but also has a simple structure and inexpensive online computations for real-time implementation. Finally, the asymptotic convergence of the gradient of potential function and vessel velocity is demonstrated, while a positioning control task for a dynamic positioning vessel is presented to evaluate the effectiveness of the proposed method.