Underwater Control of Mobile Robot

A self-sufficient submerged vehicle (AUV) is a robot which ventures submerged without obliging data from an administrator. AUVs constitute some piece of a bigger gathering of undersea frameworks known as unmanned submerged vehicles, an arrangement that incorporates non-self-governing remotely worked submerged vehicles (ROVs) – controlled and fueled from the surface by an administrator/pilot by means of an umbilical or utilizing remote control. In military applications AUVs are all the more regularly alluded to just as unmanned undersea vehicles (UUVs). Research on Autonomous Underwater Vehicle (AUV) has been expanding in the later a long time. They discover application in safeguard association for submerged mine identification and area observation, in oil/gas commercial ventures for recognition of spillage in the pipelines, in business reason for the vicinity of tiny life, centralization of different components, in trash field mapping and in numerous other marine commercial ventures. In this project I have developed and modeled an underwater robot. Also I have developed and used fuzzy logic for control of them

Adaptive depth control algorithms for an autonomous underwater vehicle

Research on Autonomous Underwater Vehicle (AUV) has been increasing in the recent years. These robotics have the ability to revolutionize access to the oceans to address critical problems facing the marine community such as underwater search and mapping, water column observations climate change assessment, marine habitat monitoring, and underwater mine detection. In this thesis an adaptive nonlinear controller for steering the dynamic model of an autonomous underwater vehicle (AUV) onto a predefined path at a constant forward speed along a desired path is being presented. In this we have used two different controllers for dive plane control of an AUV. In one case, the diving dynamics of an AUV is derived under the assumptions that the pitch angle of AUV is small in the diving plane motion of the vehicle. Autonomous Underwater Vehicles’ dynamics are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to determine due to the variations of the hydrodynamic coefficients with different operating conditions of the environment. These kinds of difficulties cause modeling inaccuracies of AUV’s dynamics. So in order to achieve robustness against parameter uncertainty, system identification technique based on Model Reference Adaptive Controller (MRAC) using MIT rule and Fuzzy Logic Controller (FLC) is employed for modeling the AUV dynamics. In this thesis MRAC technique is being proposed for the dynamics control and for the kinematics control of an Autonomous Underwater Vehicle we have used the FLC .Simulation results are being shown which shows effective dive-plane control in spite of the dynamic uncertainties. However in the second case we have proposed a dive plane controller based on Lyapunov theory and Backstepping techniques, where the dynamics of the AUV is being considered without keeping any restricting assumptions on AUV’s pitch angle

Pemahaman pelajar tingkatan lima katering terhadap bab kaedah memasak dalam mata pelajaran teknologi katering

Bab Kaedah Memasak merupakan salah satu bab yang penting dalam mata pelajaran Teknologi Katering. Faktor terpenting adalah memastikan pelajar menguasai serta memahami konsepnya adalah melalui proses pengajaran dan pembelajaran yang betul. Tinjauan awal di Sekolah Menengah Teknik yang menawarkan Kursus Katering, menunjukkan bahawa kebanyakan pelajar sukar untuk menguasai dan memahami bab tersebut. Berdasarkan hasil tinjauan , pengkaji ingin mengenalpasti pemasalahan dalam memahami bab tersebut. Di samping itu juga, pengkaji ingin mengenalpasti adakah pencapaian pelajar dalam PMR, minat, motivasi dan gaya pembelajaran mempengaruhi pemahaman pelajar, Kajian rintis telah dilakukan terhadap 10 orang responden dengan nilai alpha 0.91. Ini menunjukkan kebolehpercayaan terhadap kajian di jalankan adalah tinggi. Responden adalah terdiri daripada 30 orang pelajar Tingkatan Lima (ERT) Sekolah Menengah Teknik Muar, Johor. Keputusan skor min keseluruhan menunjukkan pelajar berminat dan mempunyai motivasi ynag baik dalam bidang ini. Namun begitu, gaya pembelajaran yang diamalkan tidak sesuai dan antara pemyebab wujudnya pemasalahan dalam memahami bab Kaedah Memasak. Ujian kolerasi menunjukkan bahawa tidak terdapat sebarang hubungan signifikan antara pencapaian PMR pelajar dengan pemahaman bab tersebut. Sementara minat, motivasi dan gaya pembelajaran membuktikan ada hubungan signifikan dengan pemahaman pelajar dalam bab Kaedah Memasak

Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control

Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller.;This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored.;In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to the explored derivatives. Biases were considered in the range -500% to 500% and delays in the range 0.5 to 40 seconds. The stability and control derivatives considered in this research effort are a combination of decoupled derivatives in the three channels, longitudinal, lateral, and directional. Numerous simulation scenarios and flight conditions are considered to provide more credibility to the obtained results. In addition, a statistical analysis has been conducted to assess the results. The performance of the control laws has been evaluated in terms of the integral of the error in tracking the three desired angular rates, pitch, roll, and yaw. In addition, the effort of the neural networks exerted to compensate for tracking errors is considered in the analysis as well.;The results show that in order to obtain reliable estimates for the investigated derivatives, the estimator needs to generate values with less than five seconds delay. In addition, derivatives estimates are within 50% or -15% off the exact values. Moreover, the importance of updating derivatives depends on the maneuver scenario and the flight condition. The estimation process at quasi-steady state conditions provides reliable estimates as opposed to estimation during fast dynamic changes; also, the estimation process has better performance at large rate of change of derivatives values

Design and Experimental Realization of Adaptive Control Schemes for an Autonomous Underwater Vehicle

Research on Autonomous Underwater Vehicle(AUV) has attracted increased attention of control engineering community in the recent years due to its many interesting applications such as in Defense organisations for underwater mine detection, region surveillance, oceanography studies, oil/gas industries for inspection of underwater pipelines and other marine related industries. However, for the realization of these applications, effective motion control algorithms need to be developed. These motion control algorithms require mathematical representation of AUV which comprises of hydrodynamic damping, Coriolis terms, mass and inertia terms etc. To obtain dynamics of an AUV, different analytical and empirical methods are reported in the literature such as tow tank test, Computational Fluid Dynamics (CFD) analysis and on-line system identification. Among these methods, tow-tank test and CFD analysis provide white-box identified model of the AUV dynamics. Thus, the control design using these methods are found to be ineffective in situation of change in payloads of an AUV or parametric variations in AUV dynamics. On the other hand, control design using on-line identification, the dynamics of AUV can be obtained at every sampling time and thus the aforesaid parametric variations in AUV dynamics can be handled effectively. In this thesis, adaptive control strategies are developed using the parameters of AUV obtained through on-line system identification. The proposed algorithms are verified first through simulation and then through experimentation on the prototype AUV. Among various motion control algorithms, waypoint tracking has more practical significance for oceanographic surveys and many other applications. In order to implement, waypoint motion control schemes, Line-of-Sight (LoS) guidance law can be used which is computationally less expensive. In this thesis, adaptive control schemes are developed to implement LoS guidance for an AUV for practical realization of the control algorithm. Further, in order to realize the proposed control algorithms, a prototype AUV is developed in the laboratory. The developed AUV is a torpedo-shaped in order to experience low drag force, underactuated AUV with a single thruster for forward motion and control planes for angular motion. Firstly, the AUV structure such as nose profile, tail profile, hull section and control planes are designed and developed. Secondly, the hardware configuration of the AUV such as sensors, actuators, computational unit, communication module etc. are appropriately selected. Finally, a software framework called Robot Operating System (ROS) is used for seamless integration of various sensors, actuators with the computational unit. ROS is a software platform which provides right platform for the implementation of the control algorithms using the sensor data to achieve autonomous capability of the AUV. In order to develop adaptive control strategies, the unknown dynamics of the AUV is identified using polynomial-based Nonlinear Autoregressive Moving Average eXogenous (NARMAX) model structure. The parameters of this NARMAX model structure are identified online using Recursive Extended Least Square (RELS) method. Then an adaptive controller is developed for realization of the LoS guidance law for an AUV. Using the kinematic equation and the desired path parameters, a Lyapunov based backstepping controller is designed to obtain the reference velocities for the dynamics. Subsequently, a self-tuning PID controller is designed for the AUV to track these reference velocities. Using an inverse optimal control technique, the gains of the selftuning PID controller are tuned on-line. Although, this algorithm is computationally less expensive but there lie issues such as actuator constraints and state constraints which need to be resolved in view of practical realization of the control law. It is also observed that the proposed NARMAX structure of the AUV consists of redundant regressor terms. To alleviate the aforesaid limitations of the Inverse optimal self-tuning control scheme, a constrained adaptive control scheme is proposed that employs a minimum representation of the NARMAX structure (MR-NARMAX) for capturing AUV dynamics. The regressors of the MR-NARMAX structure are identified using Forward Regressor Orthogonal Least Square algorithm. Further, the parameters of this MRNARMAX model structure of the AUV are identified at every sampling time using RELS algorithm. Using the desired path parameters and the identified dynamics, an error objective function is defined which is to be minimized. The minimization problem where the objective function with the state and actuator constraints is formulated as a convex optimization problem. This optimization problem is solved using quadratic programming technique. The proposed MR-NARMAX based adaptive control is verified in the simulation and then on the prototype AUV. From the obtained results it is observed that this algorithm provides successful tracking of the desired heading. But, the proposed control algorithm is computational expensive, as an optimization problem is to be solved at each sampling instant. In order to reduce the computational time, an explicit model predictive control strategy is developed using the concept of multi-parametric programming. A Lyapunov based backstepping controller is designed to generate desired yaw velocity in order to steer the AUV towards the desired path. This explicit model predictive controller is designed using the identified NARMAX model for tracking the desired yaw velocity. The proposed explicit MPC algorithm is implemented first in simulation and then in the prototype AUV. From the simulation and experimental results, it is found that this controller has less computation time and also it considers both the state and actuator constraints whilst exhibiting good tracking performance

3D reconstruction and motion estimation using forward looking sonar

Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains including archaeology, oil and gas industry, coral reef monitoring, harbour’s security, and mine countermeasure missions. As electromagnetic signals do not penetrate underwater environment, GPS signals cannot be used for AUV navigation, and optical cameras have very short range underwater which limits their use in most underwater environments. Motion estimation for AUVs is a critical requirement for successful vehicle recovery and meaningful data collection. Classical inertial sensors, usually used for AUV motion estimation, suffer from large drift error. On the other hand, accurate inertial sensors are very expensive which limits their deployment to costly AUVs. Furthermore, acoustic positioning systems (APS) used for AUV navigation require costly installation and calibration. Moreover, they have poor performance in terms of the inferred resolution. Underwater 3D imaging is another challenge in AUV industry as 3D information is increasingly demanded to accomplish different AUV missions. Different systems have been proposed for underwater 3D imaging, such as planar-array sonar and T-configured 3D sonar. While the former features good resolution in general, it is very expensive and requires huge computational power, the later is cheaper implementation but requires long time for full 3D scan even in short ranges. In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by proposing relatively affordable methodologies and study different parameters affecting their performance. We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on forward looking sonars; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars