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Navigation and guidance control system of AUV with trajectory estimation of linear modelling
Abstract
This paper put forwards a study on the development of navigation and guidance systems for AUV. The restriction in AUV model and estimation on the degree of freedom are recognized as the common problem in AUV's navigation and guidance systems. In this respect a linear model, derived from the linearization using the Jacobian matrix, will be utilized. The so obtained linear model is then estimated by the Ensemble Kalman Filter (EnKF). The implementation of EnKF algorithm on the linear model is carried out by establishing two simulations, namely by generating 300 and 400 ensembles, respectively. The simulations exhibit that the generation of 400 ensembles will give more accurate results in comparison to the generation of 300 ensembles. Furthermore, the best simulation yields the tracking accuracy between the real and simulated trajectories, in translational modes, is in the order of 99.88%, and in rotational modes is in the order of 99.99%.
... The value of the stock may fluctuate either in the form of increase or decrease. So investors should be able to predict whether stock prices are increasing or decreasing [3]. One method is the method of estimating the increase and decrease in stock prices. ...
... One method is the method of estimating the increase and decrease in stock prices. Estimation are made as a problem can very often be solved by using previous information or data related or relevant to the problem [3]. Kalman filter is a method of estimating state variables of a discrete linear dynamic system that minimizes the estimation error covariance [4]. ...
... However, in actual circumstances, there is often a continuous nonlinear dynamic system that requires another approach which is an extension of the Kalman filter called Ensemble Kalman Filter (EnKF). In the EnKF method, the algorithm is executed by generating a certain number of ensembles to calculate the mean value and the error covariance of the state variable [3]. The development of the EnKF method through modification of algorithm by adding a square root scheme at EnKF correction stage which can result in Ensemble Kalman Filter Square Root (EnKF-SR) method. ...
Shares are securities as the possession or equity evidence of an individual or corporation over an enterprise, especially public companies whose activity is stock trading. Investment in stocks trading is most likely to be the option of investors as stocks trading offers attractive profits. In determining a choice of safe investment in the stocks, the investors require a way of assessing the stock prices to buy so as to help optimize their profits. An effective method of analysis which will reduce the risk the investors may bear is by predicting or estimating the stock price. Estimation is carried out as a problem sometimes can be solved by using previous information or data related or relevant to the problem. The contribution of this paper is that the estimates of stock prices in high, low, and close categorycan be utilized as investors' consideration for decision making in investment. In this paper, stock price estimation was made by using the Ensemble Kalman Filter Square Root method (EnKF-SR) and Ensemble Kalman Filter method (EnKF). The simulation results showed that the resulted estimation by applying EnKF method was more accurate than that by the EnKF-SR, with an estimation error of about 0.2 % by EnKF and an estimation error of 2.6 % by EnKF-SR.
... ASV have been applied in many aspects, such as water quality monitoring [5], risk assessment [6] and network centric operations [7]. There are also some papers on modeling, control and estimation of ASV and autonomous unmanned vehicle [8], for example tracking control using neural network approach [9], adaptive dynamic surface control [10], path following [11], obstacle detection and avoidance [12,13], target tracking [14], stability analysis [1], estimation using square root ensemble Kalman filter [2,15], Proportional Integral Derivative (PID) control design [16], control design using Sliding Mode Control (SMC) [17][18][19], sliding PID control design [20,21], using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for Optimizing PID Parameters on Autonomous Underwater Vehicle (AUV) [22], estimation using ensemble Kalman filter [23,24] and estimation using fuzzy Kalman filter [25]. ...
... By substituting (23) into (24), we obtain ...
Autonomous Surface Vehicle (ASV) is a vehicle that is operated in the water surface without any person in the vehicle. Since there is no person in the ASV, a motion controller is essentially needed. The control system is used to make sure that the water vehicle is moving at the desired speed. In this paper, we use a Touristant ASV with the following specifications: the length is 4 meters, the diameter is 1.625 meters, and the height is 1.027 meters. The main contribution of this paper is applying the Sliding Mode Control system to the Touristant ASV model under the influence of environmental factors. The environmental factors considered in this work are wind speed and wave height. The Touristant ASV model is nonlinear and uses three degree of freedom (DOF), namely surge, sway and yaw. The simulation results show that the performance of the closed-loop system by using the SMC method depends on the environmental factors. If environmental factors are higher, then the resulting error is also higher. The average error difference between those resulted from the simulation without environmental factors and those with the influence of environmental factors is 0.05% for surge, sway and yaw motions.
... So, in this paper, we want to apply the non-linear system to the Kalman Filter and Extended Kalman Filter to get to know how the result of estimation the 12.7 x 99 mm caliber projectile motion are. Therefore, we want to calculate the time computation and we need accurate position estimation [13] [14] using Kalman Filter that is compared with Extended Kalman Filter. This is simulated with Matlab. ...
... Equation (10)- (13) are nonlinear. We use Kalman Filter for estimation. ...
A Projectile is a shot object which fired from firearms or air rifles. There are various sizes of projectile. One of them is 12.7 x 99 mm caliber projectile. The higher speed of 12.7 x 99 mm caliber projectile makes the less accurate, it is for the shot to hit the right target. It is necessary to estimate that the projectile reaches the right target. The projectile movements are modelled by three degrees of freedom (3-DOF) and have a non-linear equation. So the estimation used is The Extended Kalman Filter, but it would be combined with Kalman Filter Method. Measurement data is assumed to be linier. And we have all data measurement of projectile motion. The estimation results are simulated with Matlab. The final result of these studies show that Extended Kalman Filter has small error than Kalman Filter with error estimation of EKF is 92.02% smaller in the x-position, 97.38% smaller in the z-position, 94.99% smaller in velocity projectile, and 95.45% smaller in gamma angle and the computational time is 0.925% faster than KF method.
... In the current world of military industry, many countries have prepared their weapons for air, sea and land uses. We know that our country is an archipelagic country, so it is of necessity to have such a vehicle for a defense system [1] [2] The development of underwater defense technology commonly focuses on defense. The NKRI's need for an applicable and multifunctional technology is a very important issue in the current effort to modernize unmanned submarines functioning as the Main Weapon System Equipment (ALUTSISTA), which is applied as a spy technology or automatic weapon [3] [4] Most underwater vehicles being developed by many countries today are unmanned underwater robots or unmanned submarines. ...
The development of underwater defense technology is commonly related to its usage for security and defense of a country. The need of NKRI (the Republic of Indonesia) for an applicable and multifunctional technology for highly improved unmanned submarines is urgent considering the current necessity of unmanned technology modernization functioning as The Main Weapon System Equipments (ALUTSISTA) to be applied as a spy technology or automatic weapon. This paper focus is on a motion control system design with the motion equation of 2 Degree of Freedom (DOF) applied to an unmanned submarine system or also called a Remote Operated Vehicle (ROV). ROV requires a control system to control its maneuvering motion when underwater, especially in a straight line motion. The ROV motion equation of 2-DOF consisting of surge and roll motions is in the form of a nonlinear equation. The system control design applied to the ROV system used the Proportional Controller method combined with Sliding Mode Control. The simulation results of the Proportional SMC control system with the motion equation of 2-DOF on the ROV system show that the system is stable with an accuracy of surge and roll motions of 95% - 99%.
... The EnKF method is very reliable for both nonlinear and linear models. The EnKF method has been frequently applied for motion estimation of the 3-DOF Autonomous Surface Vehicle (ASV) model [5] [6] , the 6-DOF Autonomous Underwater Vehicle (AUV) [7] [8]and nonlinear missile movement mode [9]. Th EnKF has been applied for estimation of profit [10] [11]and crude oil price [12]. ...
Post-stroke is a stage a patient undergoes if the patient has had a previous stroke. Stroke is a big and serious problem. As the second most common cause of disability of people at age of over 60 years. For patients having experienced a stroke, rehabilitation is a way to make them able to do activities of daily living as before. Stroke Rehabilitation is a comprehensive medical management and rehabilitation (in medical, emotional, social, and vocational aspects) concerning disabilities caused by stroke through a neuro-rehabilitation approach with the aim of optimizing recovery. The finger prosthetic arm robot is one of the results of the health technology development to help accelerate the rehabilitation process specifically for finger movements. One of the efforts to develop a finger robot is to estimate the movement of the fingers, in this case the finger size used is taken from those of Javanese people in Indonesia as the data to be simulated. In this paper is an estimation of the finger motion,particularly that of the third finger of the right hand, conducted using the Ensemble Kalman Filter (EnKF) method. The simulation results produced the third finger motion estimates with an accuracy of around 92% - 99%.
... Martinez (2015) [277] implemented a non-linear dynamic model (NLDM) for an underwater vehicle, using a 3-DoF linear dynamic model to allow online estimation of sea current parameters before and during navigation. Herlambang [278] performed linearization on an AUV model using a Jacobian matrix to obtain a linear model that is then estimated by an ensemble Kalman filter (EnKF). Dinc, and later, Khaghani (2015) [279]- [281], introduced a hydrodynamic motion model of the AUV, which addresses the dynamic coupling with Coriolis and centripetal forces acting upon the vehicle. ...
The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.
... The trajectory is used as a guide to direct that the missile reach the target by following the given trajectory. Several studies have been carried out regarding the estimation of positions such as the one implemented to AUV using Ensemble and Extended Kalman Filter [3,4,5] and Square Root Ensemble Kalman Filter (SR-EnKF) method [6] [7]and Kalman Filter with fuzzy [8], and that implemented on temperature steam drum estimation [9]. Position estimation has also been applied to ASV using the Extended Kalman Filter method [10] To maintain the accuracy of the trajectory continuously, in this study, the missile trajectory estimation was made by using Unscented Kalman Filter (UKF) Algorithm. ...
Missiles are military rocket weapons having an automatic control system to locate its targets or adjust its direction. Indonesia itself, which is a country of archipelago, covers air area of its largest territory, followed by sea area and land area. Logically, the existence of missile defense equipment (the main weapon system) or precisely the type of long-range missile is acceptable to support the defense and security of the Republic of Indonesia, but its consequences to be operated in the territory of Indonesia itself, in case of an occufanct of an error in targeting the target, will fall on of harm to its own national territory. Therefore, trajectory estimation for guided missiles is the basic requirement for guided missiles to be aimed at the precise targets. The trajectory is used as a guide to direct that the missile reach the target by following the given path. To maintain the accuracy of the trajectory continuously, the missile trajectory estimation was made by using Unscented Kalman Filter (UKF) Algorithm. This algorithm was used to estimate nonlinear dynamic models The simulation results showed that the UKF method was effective, showing the accuracy of 97% by the UKF method
... Another estimate is the extension of the Kalman filter called the ensemble Kalman filter (EnKF). In the EnKF method, the algorithm is executed by producing many specific ensembles to calculate the average and error covariance of state variables [17]. In using the EnKF method, some schemes can be implemented in the EnKF method, which is the square root scheme that can be implemented in EnKF. ...
As a maritime country with a large area, besides the need to defend itself with the military, it also needs to protect itself with aerospace technology that can be controlled automatically. This research aims to develop an air defense system that can control guided missiles automatically with high accuracy. The right method can provide a high level of accuracy in controlling missiles to the targeted object. With the backpropagation neural network method for optimal control output feedback, it can process information data from the radar to control missile’s movement with a high degree of accuracy. The controller uses optimal control output feedback, which is equipped with a lock system and utilizes an accelerometer that can detect the slope of the missile and a gyroscope that can detect the slope between the target direction of the missile to follow the target, control the position, and direction of the missile. The target speed of movement can be easily identified and followed by the missile through the lock system. Sampling data comes from signals generated by radars located in defense areas and from missiles. Each part’s data processing speed is calculated using a fast algorithm that is reliable and has a level of accuracy and fast processing. Data processing impacts on the accuracy of missile movements on any change in the position and motion of targets and target speed. Improved maneuvering accuracy in the first training system can detect 1000 files with a load of 273, while in the last training, the system can detect 1000 files without a load period. So the missile can be guided to hit the target without obstacles when maneuvering.
... To analyze the fluctuation of oil prices, a proper effort shall be made by estimating world crude oil prices so that the consortium of oil entrepreneurs can predict when world oil prices fall or rise and determine policies in production, then reduce the causes of the problem that occur due to oil price instability. Many studies on estimation are carried out in all scientific fields, including estimation of stock price [3,4], estimation of profit company [5,6], and estimation of AUV and ASV position [7,8,9], For that end, this paper applied the method of estimating world crude oil prices by H-infinty to determine the right decision or action the oil entrepreneurs shall make regarding the world crude oil. ...
In international or globalised trading, commodity trading does not only depend on the
commodity, but also the role that companies have in commodity production and distribution. One
step made by oil companies clearly determine the world oil prices associated with oil deposits
(inventory) and drilling. The strategy they adopt in production will also be have strong effects on
the world oil price trends. To observe the fluctuation of the world oil prices, an effort shall be
made by estimating the world crude oil prices so that entrepreneurs can predict when the world oil
prices fall or rise and determine policies in the production and use of oil. This study was to aplly
method of the world crude oil price estimation, namely H-Infinity to determine the right decision
the oil entrepreneurs shall make regarding the world crude oil. The simulation results showed that
the application the H-Infinity method had an accuracy of around 97-98%.
... Therefore, a software is needed to estimate blood stock for the blood banks. Many studies on estimation are carried out in all scientific fields, including estimation of stock price and profit company [2,3], steam drum water level [4], estimation of AUV trajectory and ASV position [5,6,7], and estimation of missile trajectories [8]. ...
Every hospital is required to have a Hospital Blood Bank (HBB), a service unit of the
hospital responsible for the availability of blood for safe transfusion, of high quality and sufficient
to support health services in the hospital and other health care centers. PMI (Indonesian Red
Cross) continues to campaign for blood donations as part of a lifestyle (lifestyle). Every year, PMI
set its targets of up to 4.5 million blood bags to meet the national blood needs, adjusted to the
standards of the International Health Institute (WHO), which is 2% of the population for each day.
With a continuous campaign by PMI, the stability of blood stock and distribution on target must
be maintained, and the importance of blood distribution both blood coming from blood donors and
blood distributed to PMI or other regional hospitals must be taken into account, therefore a
software is required to estimate blood stock.for the blood banks. In this paper an estimation of
Whole Blood (WB) and Anti-Hemophiliate Factor (AHF) blood demand was made at PMI
Surabaya. Estimation is made because a problem can sometimes be solved by using the previous
information or data related to the problem. One estimation method used was Extended Kalman
Filter (EKF), an estimation method with a fairly high degree of accuracy. Based on the simulation
results, a numerical study was obtained based on the number of iterations, and that with 350
iterations showed a higher accuracy than those with 250 and 150 iterations. The accuracy reached
was within the range of 95-98 %.
... Estimates are made because a problem can sometimes be solved using presvious information or data related to the problem. One estimation method is the Ensemble Kalman Filter (EnKF) which functions to minimize covariance error estimation by generating a number of ensembles [3]. The development of the EnKF method is the Kalman Filter Square Root Ensemble (EnKF-SR) which is obtained by adding the square root factor at the EnKF correction stage [4]. ...
... Some of the developments of the Kalman Filter (KF) method are Ensemble Kalman Filter (EnKF), Ensemble Kalman Filter Square Root (EnKF-SR), Fuzzy Kalman Filter (FKF), Unscented Kalman Filter (UKF), and Unscented Kalman Filter Square Root (UKF-SR). EnKF is the development of KF by generating a number of good ensembles, that is, 100, 200, or 300 ensembles [2,3]. EnKF-SR is the development of EnKF by adding a square root scheme at the correction stage [4,5]. ...
Stock market is established in order to bring together the stock sellers and buyers. Securities often traded in the stock market are shares. Shares are securities as proof of participation or ownership of a person or legal entity in a company. In choosing a safe and appropriate investment in stocks, investors need a way to assess the price of the shares to be purchased or the ability of the stock to provide dividends in the future, so as to optimize profits. The correct way to analyze the risk for investors in investing is to estimate the stock price. The purpose of this paper is to analyze the comparison of share price estimates using the Unscented Kalman Filter (UKF) and Unscented Kalman Filter Square Root (UKF-SR) methods. The simulation results show that both methods have a significantly high accuracy of less than 2%. We conclude that the two methods can be used to estimate the stock prices.
... Estimation is made for problem solving that requires previous information so that the next step can be determined in solving the problem. Estimation methods are often used in robotics such as for estimation of AUV trajectory [4,5,6] and Missile Trajectory [7], Maglev ball position estimation [8]. Kalman filter is a method of estimating state variables of a discrete linear dynamic system that minimizes the estimation error covariance. ...
The equipment unit which has the most vital role in the steam-powered electric power plant is boiler. Steam drum boiler is a tank functioning to separate fluida into has phase and liquid phase. The existence in boiler system has a vital role. The controlled variables in the steam drum boiler are water level and the steam temperature. If the water level is higher than the determined level, then the gas phase resulted will contain steam endangering the following process and making the resulted steam going to turbine get less, and the by causing damages to pipes in the boiler. On the contrary, if less than the height of determined water level, the resulted height will result in dry steam likely to endanger steam drum. Thus an error was observed between the determined. This paper studied the implementation of the Ensemble Kalman Filter Square Root (EnKF-SR) method in nonlinear model of the steam drum boiler equation. The computation to estimate the height of water level and the temperature of steam was by simulation using Matlab software. Thus an error was observed between the determined water level and the steam temperature, and that of estimated water level and steam temperature. The result of simulation by Ensemble Kalman Filter Square Root (EnKF-SR) on the nonlinear model of steam drum boiler showed that the error was less than 2%. The implementation of EnKF-SR on the steam drum boiler r model comprises of three simulations, each of which generates 200, 300 and 400 ensembles. The best simulation exhibited the error between the real condition and the estimated result, by generating 400 ensemble. The simulation in water level in order of 0.00002145 m, whereas in the steam temperature was some 0.00002121 kelvin.
... AUV work independently means without direct control by humans. AUV can be used for underwater exploration, mapping, underwater defense system equipment, sensor off board submarines, inspection of underwater structures, natural resources and the condition of the Earth's surface plates etc [4]. Two important things required to analyze the Autonomous Underwater Vehicle (AUV) that is Earth Fixed Frame (EFF) and Body Fixed Frame (BFF). ...
... AUV adalah kendaraan yang digerakkan melalui air dengan sistem propulsi, dikendalikan dan dikemudikan oleh komputer onboard dengan enam derajat kebebasan (DOF) manuver, sehingga dapat melaksanakan tugas yang telah ditentukan sepenuhnya dengan sendirinya. Manfaat AUV tidak hanya mengeksplorasi sumber daya laut, melainkan juga untuk pemetaan bawah laut dan sebagai peralatan sistem pertahanan bawah laut [3,4]. ...
The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided inertial navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.
This study presents the development of navigation system of AUV. It is initially in the form of a nonlinear system model to determine the trajectory for the AUV motion control. The non-linear system model of UNUSAITS AUV is then implemented in the Square Root Ensemble Kalman filter (SR-EnKF) to estimate the trajectory of the AUV. The developed EnKF algorithm covers two types of simulations. The contribution from this paper is trajectory or position estimation of nonlinear UNUSAITS AUV system with 6-DOF AUV model. The result of model estimation is presented based on numeric study and simulation. The first simulation is dedicated to generate 400 ensembles, and the second simulation is to generate 400 ensembles. The second simulation shows that the 500 ensembles give more accurate results. A further examination on the accuracy has been performed by accounting for the RMSE of actual condition and the estimation from the simulation, which yields the range of accuracy between 97 % up to 99%.
AUV has 6 degrees of freedom derived from a propulsion system that regulates the angular speed of AUV and the fin system that adjusts the angle of the fin and rudder positions. In this paper, an AUV motion control system design for diving was developed, that is, the control system of surge, heave and pitch motions by applying Sliding Mode Control (SMC) method. The AUV specification used in this study was an UNUSAITS AUV with a length of 1.5 meters, a weight of 16 kilograms using controller of arduino mega 2.0. The main contribution of this paper is Control system for nonlinear models of 3-DOF UNUSAITS AUV. The results of simulation showed that the SMC method could be effectively applied as a 3-DOF motion control system with an error of 5% for surge motion and 0.01% for heave motion and 2% for pitch motion.
An Autonomous Surface Vehicle (ASV) is a vehicle in the form of a ship on the surface of the water that can move without a crew on it or operate automatically. This study used the Touristant ASV with a length of 4 meters, a diameter of 1.5 meters, and a height of 1.3 meters. The contribution of this paper is the estimation of ASV position and ASV motion influenced by wind speed and wave height. The estimation method used is the Ensemble Kalman Filter (EnKF) method. EnkF is applied to the nonlinear ASV model to obtain a small position error. In our simulations, we conducted 3 scenarios based on the number of generated ensembles, that are 100, 200 and 300 ensembles. The position error generated from the simulation showed that the simulation with the lower position error has an accuracy more than 95%. The position error of x is 0.009 meters, the position error of y is 0.008 meters, and the position error of XY plane is 0.01 meters.
Marine researchers need consistent historical and georeferenced data from the marine environment in order to constantly monitor the biological condition of the habitat or to document delicate archeological sites. To overcome the difficulties related to the acquisition of high quantity of worthy data and to the accurate estimation of the position, the development of easy-to-use IT tools could certainly help. This article aims to present a tool that can equip different type of underwater vehicles capable of estimating its position during his surveys using its on-board sensors. The estimation algorithm is based on the UKF technique and some preliminary results of its performances are presented.
In this paper, the numerical study of designing on navigation and stability control system for AUV is studied. The study started by initiating hydrostatic forces, added masses, lift force, drag forces and thrust forces. Determining the hydrodynamic force which is the basic need to know the numerical case study on designing on navigation and stability control system for AUV where Autonomous Underwater vehicles (AUV). AUV is capably underwater vehicle in moving automatically without direct control by humans according to the trajectory. The result of numerical study is properly to be the reference for the next developing for AUV.
This article presents a unified state-space model for ship maneuvering, station-keeping, and control in a seaway. The frequency-dependent potential and viscous damping terms, which in classic theory results in a convolution integral not suited for real-time simulation, is compactly represented by using a state-space formulation. The separation of the vessel model into a low-frequency model (represented by zero-frequency added mass and damping) and a wave-frequency model (represented by motion transfer functions or RAOs), which is commonly used for simulation, is hence made superfluous.
A nonlinear differential equation of the Riccati type is derived for the covariance matrix of the optimal filtering error. The solution of this "variance equation" com-pletely specifies the optimal filter for either finite or infinite smoothing intervals and stationary or nonstationary statistics. The variance equation is closely related to the Hamiltonian (canonical) differential equations of the calculus of variations. Analytic solutions are available in some cases. The significance of the variance equation is illustrated by examples which duplicate, simplify, or extend earlier results in this field. The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results. In several examples, the estimation problem and its dual are discussed side-by-side. Properties of the variance equation are of great interest in the theory of adaptive systems. Some aspects of this are considered briefly.
This paper describes the estimation of hydrodynamic coefficients and the control algorithm based on a nonlinear mathematical modeling for a test bed autonomous underwater vehicle (AUV) named by SNUUV I (Seoul National University Underwater Vehicle I).A six degree of freedom mathematical model for SNUUV I is derived with linear and nonlinear hydrodynamic coefficients, which are estimated with the help of a potential code and also the system identification using multi-variable regression.A navigation algorithm is developed using three ranging sonars, pressure sensor and two inclinometers keeping towing tank applications in mind. Based on the mathematical model, a simulation program using a model-based control algorithm is designed for heading control and wall following control of SNUUV I.It is demonstrated numerically that the navigation system together with controller guides the vehicle to follow the desired heading and path with a sufficient accuracy. Therefore the model-based control algorithm can be designed efficiently using the system identification method based on vehicle motion experiments with the appropriate navigation system.
There is a great demand for autonomous underwater vehicles (AUVs) to investigate artificial underwater structures such as piles and caissons in harbours, and risers and jackets of deep-sea oilfields. This paper proposes an autonomous investigation method of underwater structures using AUVs that is implemented by initially detecting the target objects, localizing them, then approaching them by taking video images while closely tracing their shape. A laser ranging system and a navigation method based on the relative position with respect to the target objects are introduced to realize this behaviour.
This paper proposes a multistage rule-based preci- sion positioning control method for the linear piezoelectrically actuated table (LPAT). During the coarse-tuning stage, the LPAT is actuated by coarse voltage schemes toward the target of 20 μm at a higher velocity, and during the fine-tuning stage, it is steadily and accurately driven by the fine voltage scheme to reach the target position. The rule-based method is employed to establish the control rules for the voltages and displacements of the two stages using statistical methods. The experimental results demonstrate that the proposed control method can quickly reach steady state, and the steady-state error can be reduced to less than or equal to 0.02 μm for small travel (±0.1 μm) and large travel (±20 mm).
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. It presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. The 2nd edition includes a partial rewrite of Chapters 13 an 14, and the Appendix. In addition, there is a completely new Chapter on "Spurious correlations, localization and inflation", and an updated and improved sampling discussion in Chap 11. © 2009 Springer-Verlag Berlin Heidelberg. All rights are reserved.
Optimization with Jacobian Approach for ITS AUV System
- T Herlambang
- E Djatmiko
- H Nurhadi
Modular Modelling and Control for Autonomous Vehicle (AUV)
- yang
Square Root Ensemble Kalman Filter (SR-EnKF) for Estimation of Missile Position
- T Herlambang
Optimization with Jacobian Approach for ITS AUV System
- herlambang