Search

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For increasing the solar energy harvesting performance, any solar tracking system must track the sun at the right angle during periods of daytime. We proposed an enhanced solar tracking instrument to solve the issues of low sunlight incident angles and manual orientation/elevation calibrations. This novel tracker used two Global Positioning System (GPS) receivers with Real-Time Clock (RTC) forming a satellite compass and combined with an inclinometer to follow the sun trajectory correctly without manual aligning the heading of system to orientation of North-South. Such a dual-axis instrument was controlled by a microcontroller unit (MCU) integrated with servo-motors, satellite compass and inclinometer. The embedded Solar Position Algorithm (SPA) can determine the angles of the sun vector at the given date, time and geographical information from the satellite compass. The power generated by the proposed tracking instrument, compared with a fixed-tilted photovoltaic (PV) tracker, was proven in scenarios of fixing, rotating and moving. The proposed solar tracker with satellite compass after field measurement extracted more energy than the fixed-tilted solar tracker under fixed location with mostly clear day 35.91%, in different locations with clear day 38.72% and 38.40%; in heading variation and moving conditions were 60.00% and 113.70%. Therefore, this solar tracker is not only suitable for PV grid but also well suitable for mobile vehicles with keeping the panel aligned with the sun automatically, whether geographical position in northern or southern Hemisphere independently and without solar panel tilted orientation adjustment to achieve the robust, convenient and reliable requirements. • An enhanced open-loop tracking system with GPS for auto-positioning • Satellite compass consisted of GPS and RTC calibrates solar position in real time. • Tracking operation controlled by MCU with embedded SPA and satellite compass [ABSTRACT FROM AUTHOR]

We analysed the interpersonal interaction between players during official matches considering the coupling of different playing positions. 2D coordinates and technical actions of 204 male professional football players during eight matches were obtained using a video-based tracking system. Dyads were composed of the nearest opponents and the interpersonal distance and frequency were computed. Dyadic distances were used to estimate the pressure index of defensive teams and the relationship with technical performance. Full-backs presented the greatest distances of their opponents. Dyads between defenders and forwards presented the shortest distances and were more frequent. In moments with ball possession the midfielders and forwards have more than one defensive player as the nearest opponent to them. The pressure index of defensive teams was significantly negatively and positively correlated with tackles performed by them, and successful passes of offensive teams, respectively, suggesting that defensive players more distant from the offensive players performed less tackles and provided better conditions for their opponents to perform passes. Interpersonal interaction is influenced by playing positions and maintaining proximity to the opponents can improve defensive technical performance. Information about the interpersonal interactions in real match situations provides the fundamentals for future research and assists coaches in planning tactical training. [ABSTRACT FROM AUTHOR]

Combining the current development status of intelligent robots, obstacle avoidance and automatic tracking are the focus of robot travel problems.Based on the development status of domestic intelligent control systems, current technology, etc, this paper uses arduino as the core control system, combined with infrared tracking module. Four modules, such as ultrasonic obstacle avoidance module, motor drive module and power module, have designed a good control scheme, thus realizing the intelligent tracking and obstacle avoidance function of the wheeled robot. [ABSTRACT FROM AUTHOR]

Agriculture is the primary occupation in India and is called India's backbone. But of late, a lot of problems are being faced in agriculture by the farmers. One of the major problems being water scarcity. As per surveys, almost 20 percent of the agricultural land is wasted due to water scarcity and becomes a barren land. Thus, this research gives an idea of smart irrigation system. This irrigation system uses three sensors namely temperature sensor, humidity sensor and soil moisture sensor, and fuzzy logic is used to operate the solenoid valve. The data from the sensors is then sent to the cloud by using adafruit.io and the farmer can view the moisture level, humidity level and temperature recorded by these sensors. All the operations are governed by an Arduino and the power supply for the Arduino is given by a solar panel which uses LDR (Light Dependent Resistor) and makes it into an automatic tracking system. The crops are grown in two tubs and comparison of growth of plant with automated irrigation and normal irrigation is carried out. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the Microsoft's RGB-D sensor Kinect and a customized 3- axis pan-tilt-roll machinery, a new intelligent sensing and tracking system was designed and manufactured. In order to simulate human natural visual sensing behavior, this system adopts the sensing function from Kinect installed upon the 3-axis motion machinery, controlled by an expert PID control algorithm based on the adaptive Kalman filter, so as to guarantee automatic real-time visual tracking and to observe human movements and receive his/her position information. This system is designed to be applied directly to the video production of distance education, aiming to improve distance education itself and resolve difficulties in keeping account of users learning state. It also has great potential of functioning as a basic platform where many other human-computer natural interactions can be extended. [ABSTRACT FROM AUTHOR]

Search and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others. [ABSTRACT FROM AUTHOR]

This Article examines employee location tracking through smart phone apps and GPS devices attached to or embedded within an employee's personal or company vehicle. For each form of tracking, this Article provides separate frameworks for employers to follow when conducting individual employee misconduct investigations and when tracking an entire group of employees for non-investigatory purposes. Beginning with GPS tracking for individual misconduct investigations, this Article contends that such tracking should be used only as a means to corroborate evidence that an employee has committed a terminable offense, that an employer may resort to this technique only after alternative investigative methods fail to generate enough evidence to warrant disciplinary action, and that the tracking must cease once the alleged wrongdoing has been corroborated. Turning to noninvestigatory forms of GPS tracking, such as the monitoring of an employer's entire fleet of taxi-cab or rideshare drivers, this Article proposes that such tracking should be performed only for legitimate business purposes and only if employees consent to the tracking in advance. Whether performed for investigatory or non-investigatory purposes, this Article further proposes that employers should avoid collecting GPS tracking data while employees are off-duty. Finally, this Article addresses employee tracking through smart phone apps, including employer-owned apps and apps designed for time-keeping purposes. Because employees who use such apps typically consent to the monitoring of their phone's location, this Article concludes that employees tracked in this manner cannot reasonably expect privacy in such monitoring. As a result, this Article predicts that appbased employee tracking will not trigger Fourth Amendment protection, nor will it be sufficient to sustain a privacy-related tort claim, such that employers will typically not face civil liability for tracking employees through smart phone apps. [ABSTRACT FROM AUTHOR]

Tracking of signals in Nuclear Magnetic Resonance (NMR) spectra is a basic technique used in drug discovery, systems and structural biology. Current experimental setups allow to measure hundreds of spectra, which require analysis, ideally in an automated and reproducible manner. In this study, we present a novel approach to the automate tracking of signals in NMR spectroscopy. We model perturbation of the signal position with Gaussian process regression, which allows for scalability and robustness. The proposed method outperforms the other existing tracking routines, and is applicable for complex data featuring common hindrances such as: missing signals, noise and crossing trajectories. [ABSTRACT FROM AUTHOR]

Most target tracking is based on a lot of samples training to build the model of the target, which is then carried on the tracking processing. This will need to choose a lot of tracked target samples for learning and training. However, there are all kinds of deformation of the training samples, including variety of light and scale, and so on, causing the long computation time, high computational complexity, and less robustness. The traditional kernel correlation filtering (KCF) tracking is through online learning of the first frame in the target vide. It then uses cyclic matrix to strengthen samples robustness, reducing the complexity of the calculation and time. But, the traditional KCF nuclear is unsatisfactory used for complex scenarios and quick treatment. In this paper, under the framework of the KCF, the target context information is introduced to make the tracking have better robustness and a better effect to deal with complex scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to archive the robust tracking of moving objects under moving camera environment where the similar moving objects cross each other. Tracking with moving camera sometimes fails to track the object with similar color objects or similar background. Proposed approach is a particle filter based approach. It introduces the likelihood calculated by probabilistic background model which is constructed using dense optical flow and fast density estimation. Proposed approach introduces SVM (Support Vector Machine) 1 to judge the scene where it is difficult to construct the probabilistic background model with non-uniform optical flow. This SVM uses the degree histogram of optical flow. Usefulness of proposed approach is evaluated in the experiments using actual video and the performance is compared with recent tracking approaches by quantitative evaluations. [ABSTRACT FROM AUTHOR]

Robot-based tracking of spontaneous respiration-triggered motion of human tissue becomes increasingly important in robot-guided imaging, interventional radiology, surgery and radiotherapy. This work provides a first general comparative assessment of the tracking performances that can potentially be achieved by common control schemes under realistic conditions. Cartesian exemplary spontaneous respiratory motions of 185 min duration were recorded from a healthy male subject and transformed into reference trajectories for tracking experiments. Prediction error was modeled by random perturbation of these trajectories with increasing amplitude over the prediction horizon. The controlled system, an industrial robot, was represented by an identified ARX model. A repetitive (RC), adaptive (ALC), feedforward (FFC) and model-based predictive controller (MPC) were implemented. ALC adaptively identifies robot latency and compensates this latency by according time-shift of reference coordinates. Control performance was assessed based on the control error as well as this errors’ estimated worst case dosimetric consequence in a radiotherapy application. Deviations from periodicity in reference trajectories were statistically quantified and reproduced for simulation-based assessment of RC. ALC, FFC and MPC were assessed experimentally. Realistic deviations from periodicity made performance of RC unprofitable, even with ideal function of controller and period-robustness techniques. The same applies potentially to related methods such as iterative learning control. ALC, FFC and MPC performed comparably for ideal prediction. Despite realistic prediction error, FFC and MPC achieved submillimeter control error, significantly outperforming ALC. Thus, FFC or MPC should be employed, RC and ALC should not be employed in robot-based tracking of spontaneous respiration-triggered motion of human tissue. [ABSTRACT FROM AUTHOR]

This paper presents a robust online multiple object tracking (MOT) approach based on multiple features. Our approach is able to handle MOT problems, like long-term and heavy occlusions and close similarity between target appearance models. The proposed MOT algorithm is based on the concept of multi-feature fusion. It selects the best position of the tracked target by using a robust appearance model representation. The appearance model of a target is built with a color model, a sparse appearance model, a motion model and a spatial information model. In order to select the optimal candidate (detection response) of the target, we calculate a linear affinity function that integrates similarity scores coming from each feature. In our MOT system, we formulate the problem as a data association problem between a set of detections and a set of targets according to their joint probability values. The proposed method has been evaluated on public video sequences. Compared with the state-of-the-art, we demonstrate that our MOT framework achieves competitive results and is capable of handling several challenging problems. [ABSTRACT FROM AUTHOR]

In this paper, a brief overview of real time implementation of next generation Robust, Tracking, Disturbance rejecting, Aggressive (RTDA) controller and Model Predictive Control (MPC) is provided. The control algorithm is implemented through MATLAB. The plant model used in controller design is obtained using system identification tool and integral response method. The controller model is developed in Simulink using jMPC tool, which will be executed in real time. The outputs obtained are tested for various constraint values to obtain the desirable results. The implementation of Hardware in Loop is done by interfacing it with MATLAB using Arduino as data acquisition unit. The performance of RTDA is compared with those of MPC and Proportional Integral controller. [ABSTRACT FROM AUTHOR]

Control of the non-minimum phase (NMP) system is challenging, especially in the presence of modelling uncertainties and external disturbances. To this end, this paper presents a combined feedforward and model-assisted Active Disturbance Rejection Control (MADRC) strategy. Based on the nominal model, the feedforward controller is used to produce a tracking performance that has minimum settling time subject to a prescribed undershoot constraint. On the other hand, the unknown disturbances and uncertain dynamics beyond the nominal model are compensated by MADRC. Since the conventional Extended State Observer (ESO) is not suitable for the NMP system, a model-assisted ESO (MESO) is proposed based on the nominal observable canonical form. The convergence of MESO is proved in time domain. The stability, steady-state characteristics and robustness of the closed-loop system are analyzed in frequency domain. The proposed strategy has only one tuning parameter, i.e., the bandwidth of MESO, which can be readily determined with a prescribed robustness level. Some comparative examples are given to show the efficacy of the proposed method. This paper depicts a promising prospect of the model-assisted ADRC in dealing with complex systems. [ABSTRACT FROM AUTHOR]

Massive Sargassum stranding events affect erratically numerous countries from the Gulf of Guinea to the Gulf of Mexico. Forecasting transport and stranding of Sargassum aggregates require progress in detection and drift modelling. Here we evaluate the role of currents and wind, i.e. windage, on Sargassum drift. Sargassum drift is computed from automatic tracking using MODIS 1 km Sargassum detection dataset, and compared to reference surface current and wind estimates from collocated drifters and altimetric products. First, we confirm the strong total wind effect of ≈3 % (≈2 % of pure windage), but also show the existence of a deflection angle of ≈10° between Sargassum drift and wind directions. Second, our results suggest reducing the role of currents on drift to 80 % of its velocity, likely because of Sargassum resistance to flow. These results should significantly improve our understanding of the drivers of Sargassum dynamics and the forecast of stranding events. • Automatic tracking of Sargassum aggregations on MODIS coarse resolution • 200-cases dataset of Sargassum velocities • Wind effect evaluated at 3 % of the wind speed, deviated 10° to the right. • Currents effect evaluated at 80 % its full speed. [ABSTRACT FROM AUTHOR]

The design of air defence, traditional command control system is very challenging which has been used with basic methodologies. Traditional design is associated with unstructured and uncorrelated data and requires huge lines of code using hard disk drive (HDD) in the system. Hence an attempt was made for a better simplified database management system (DBMS) software data access methodology, which processed the incoming airborne data, message in RDBMS database to achieve full automation on real-time. The transaction is accomplished through SQL pass through method from the host decision making system into database. An algorithm of track identification during midcourse track separation was undertaken for prototype development on DBMS data access methodology. In this methodology Oracle C++ calls interface embedded query call was used from the host interface system. The purpose of this development was to find a comparison of online process timing between HDD and SSD using commercial database, and to evaluate performance of dynamic processing of RDBMS Database for identification of target vehicle and booster after separation. Produced experimentation results from improved performance of the proposed methodology on which futuristic command control system can rely. [ABSTRACT FROM AUTHOR]

Vison based tracking in aerial images has its own significance in the areas of both civil and defense applications. A novel algorithm called aerial tracking learning detection which works on the basis of the popular tracking learning detection algorithm to effectively track single and multiple objects in aerial images is proposed in this study. Tracking learning detection (TLD) considers both appearance and motion features for tracking. It can handle occlusion to certain extent, and can work well on long duration video sequences. However, when objects are tracked in aerial images taken from platforms like unmanned air vehicle, the problems of frequent pose change, scale and illumination variations arise adding to low resolution, noise and jitter introduced by motion of the camera. The proposed algorithm incorporates compensation for the camera movement, algorithmic modifications in combining appearance and motion cues for detection and tracking of multiple objects and enhancements in the form of inter object distance measure for improved performance of the tracker when there are many identical objects in proximity. This algorithm has been tested on a large number of aerial sequences including benchmark videos, TLD dataset and many classified unmanned air vehicle sequences and has shown better performance in comparison to TLD. [ABSTRACT FROM AUTHOR]

The percentage of electricity produced from renewable sources increased significantly in recent years. However, photovoltaic panels have a low efficiency (15-18%). Current and future concerns focus on performance increase correlated with a low price cost for the photovoltaic panels. The most common method of increasing the energy production of photovoltaic panels is based on tracking systems. The paper presents an experimental model of tracking system for photovoltaic panels pneumatic driven by a programmable logic controller. [ABSTRACT FROM AUTHOR]

Real-time location tracking of individuals has become relatively easy with the widespread availability of commercial wearable devices that use geographical positioning information to provide location-based services. One application of this technology is to allow parents to monitor the location of their children. This paper investigates child location tracking technology in the US and the UK and compares its privacy implications. Although overall the price levels and the technical capabilities are the same, we find that the features of the technology are different depending on the social context. This can be attributed to national regulations and law that shape how a technology can be used. These laws and regulations, influenced by cultural frameworks, values, and morality, differ considerably between the countries. Clarifying the expected impacts of technology on the lives of users and other stakeholders in terms of these contextual factors will help to inform public debate about technical possibilities and societal needs. [ABSTRACT FROM AUTHOR]

An improved Mean-Shift (MS) tracker called joint CB-LBWH, which uses a combined weighted-histogram scheme of CBWH (Corrected Background-Weighted Histogram) and LBWH (likelihood-based Background-Weighted Histogram), is presented. Joint CB-LBWH is based on the notion that target representation employs both feature saliency and confidence to form a compound weighted histogram criterion. As the more prominent and confident features mean more significant for tracking the target, the tuned histogram by joint CB-LBWH can reduce the interference of background in target localization effectively. Comparative experimental results show that the proposed joint CB-LBWH scheme can significantly improve the efficiency and robustness of MS tracker when heavy occlusions and complex scenes exist. [ABSTRACT FROM AUTHOR]

Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehicle appearance and illumination, besides the occlusion and overlapping incidents. Particle filter has been proven as an approach which can overcome nonlinear and non-Gaussian situations caused by cluttered background and occlusion incidents. Unfortunately, conventional particle filter approach encounters particle degeneracy especially during and after the occlusion. Particle filter with sampling important resampling (SIR) is an important step to overcome the drawback of particle filter, but SIR faced the problem of sample impoverishment when heavy particles are statistically selected many times. In this work, genetic algorithm has been proposed to be implemented in the particle filter resampling stage, where the estimated position can converge faster to hit the real position of target vehicle under various occlusion incidents. The experimental results show that the improved particle filter with genetic algorithm resampling method manages to increase the tracking accuracy and meanwhile reduce the particle sample size in the resampling stage. [ABSTRACT FROM AUTHOR]

Copyright of Revista Kronos is the property of Revista Kronos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: We propose a generic online multi-target track-before-detect (MT-TBD) that is applicable on confidence maps used as observations. The proposed tracker is based on particle filtering and automatically initializes tracks. The main novelty is the inclusion of the target ID in the particle state, enabling the algorithm to deal with unknown and large number of targets. To overcome the problem of mixing IDs of targets close to each other, we propose a probabilistic model of target birth and death based on a Markov Random Field (MRF) applied to the particle IDs. Each particle ID is managed using the information carried by neighboring particles. The assignment of the IDs to the targets is performed using Mean-Shift clustering and supported by a Gaussian Mixture Model. We also show that the computational complexity of MT-TBD is proportional only to the number of particles. To compare our method with recent state-of-the-art works, we include a postprocessing stage suited for multi-person tracking. We validate the method on real-world and crowded scenarios, and demonstrate its robustness in scenes presenting different perspective views and targets very close to each other. [Copyright &y& Elsevier]

The popular SIFT, SURF and other local features exist computational complexity, poor real-time performance for matching and other follow-up steps. Therefore, a novel visual tracking feature algorithm is proposed based on the FAST corner detection, in order to overcome the impact of noise and outdoor lighting changes in the practical application and, which can quickly match the feature points. Experiments indicate that the proposed tracking feature can ensure the better matching accuracy and robustness than the original visual tracking features, with the lower computational and real-time processing. [ABSTRACT FROM AUTHOR]

Abstract: We present the development and evaluation of a realtime indoor localisation system for tracking people. Our aim was to track a person’s indoor position using dead-reckoning, while limiting position error without depending on extensive wireless network infrastructure. The Indoor People Tracker used wearable motion sensors, a floor-plan map and a limited wireless sensor network for proximity ranging. We evaluated how the position accuracy of the Indoor People Tracker was affected by floor-plan map features, wireless proximity range and motion information. The advantage of the Indoor People Tracker was found; it was able to achieve accurate position resolution with minimal error, while not depending on wireless proximity. [Copyright &y& Elsevier]

Highlights: [•] A robust algorithm for 2D visual tracking and 3D pose estimation is proposed. [•] We focus on partial occlusions that distort the region properties of an object. [•] Optimal pose of an object is estimated via particle filters in a decoupled manner. [•] The degree of trust between system’s predictions and measurements is controlled. [•] The resulting methodology improves tracking performance in clutter and occlusion. [ABSTRACT FROM AUTHOR]

Highlights: [•] We improve people tracking using object past history to make more reliable choices. [•] We model object history using a Finite State Automaton. [•] The method tracks objects in complex scenarios with a large number of occlusions. [•] The method is robust with respect to most problems caused by the detection phase. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we focus on the joint problem of tracking humans and recognizing human action in scenarios such as a kitchen scenario or a scenario where a robot cooperates with a human, e.g., for a manufacturing task. In these scenarios, the human directly interacts with objects physically by using/manipulating them or by, e.g., pointing at them such as in “Give me that…”. To recognize these types of human actions is difficult because (a) they ought to be recognized independent of scene parameters such as viewing direction and (b) the actions are parametric, where the parameters are either object-dependent or as, e.g., in the case of a pointing direction convey important information. One common way to achieve recognition is by using 3D human body tracking followed by action recognition based on the captured tracking data. For the kind of scenarios considered here we would like to argue that 3D body tracking and action recognition should be seen as an intertwined problem that is primed by the objects on which the actions are applied. In this paper, we are looking at human body tracking and action recognition from a object-driven perspective. Instead of the space of human body poses we consider the space of the object affordances, i.e., the space of possible actions that are applied on a given object. This way, 3D body tracking reduces to action tracking in the object (and context) primed parameter space of the object affordances. This reduces the high-dimensional joint-space to a low-dimensional action space. In our approach, we use parametric hidden Markov models to represent parametric movements; particle filtering is used to track in the space of action parameters. We demonstrate its effectiveness on synthetic and on real image sequences using human-upper body single arm actions that involve objects. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel fusion framework to combine the diverse outputs of arbitrary trackers, which are typically not directly combinable, allowing for significantly increasing the tracking quality. Our main idea is first to transform individual tracking outputs such as motion inliers, bounding boxes, or specific target image features to a shared pixel-based representation and then to run a fusion step on this representation. The fusion process additionally provides a segmentation, which, in turn, further allows for a dynamic weighting of the specific trackers’ contributions. In particular, we demonstrate our fusion concept by combining three diverse heterogeneous tracking approaches that significantly differ in methodology as well as in their reported outputs. In the experiments we show that the proposed fusion strategy can successfully handle highly complex non-rigid object scenarios where the individual trackers and state-of-the-art (non-rigid object and fusion based) trackers fail. We demonstrate high performance on a large number of challenging sequences, where we clearly outperform the individual trackers as well as state-of-the-art tracking approaches. [Copyright &y& Elsevier]

Since the dynamics of unmanned underwater vehicle (UUV) is with properties of high nonlinearity and uncertainty, a robust tracking control method based on error space theory was proposed to deal with the complex marine environment and variation of parameters or module configuration. The error space system including the dynamics of reference platform and tracking errors was constructed, where the error variable is the error between the state of reference platform and UUV that act as state variables of state space. Taking account of sea wave disturbance, simulations were performed to validate the robustness of depth tracking control algorithm. Results show that the tracking accuracy and tracking capacity of the system are improved with robustness enhanced obviously. [ABSTRACT FROM AUTHOR]