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Nonlinear Backstepping Control of Underactuated AUV in Diving Plane
Abstract
This paper proposed a path-following algorithm for diving control of autonomous underwater vehicle (AUV) along a predefined depth, where surge force and stern plane are only available for vehicle's 3DOF diving motion. Controller design is based on robust backstepping technique together with Lyapunov function. The whole system can be guaranteed asymptotic stability with path-following error convergence to zero, and the states are all bounded. To demonstrate the effectiveness of the proposed method, the simulation results are illustrated in this paper.
In this paper, a novel finite-time fault-tolerant trajectory tracking controller is created to render a marine vehicle to enhance tracking performance in the presence of complex unknown variables, including model uncertainties, environmental disturbances, and actuator faults. An adaptive fault-tolerant finite-time sliding mode controller is designed by introducing adaptive control techniques, the non-singular fast terminal sliding mode (NSFTSM) function, and a radial base function (RBF) neural network. The radial base function neural network (RBFNN) is developed to eliminate the influences of model uncertainties and environmental disturbances. A fault compensation by integrating the adaption technique is designed to reduce the effects of actuator faults and approximation errors. Suffering from uncertainties and actuator faults, the proposed finite-time tracking controller can track the desired trajectory with high precision. Simulation results and compared simulations indicate the efficiency and superiority of the proposed controller.
The use of Learning Management Systems as a tool for designing, sharing, monitoring, and organizing various forms of teaching and training content marked a turning point in the development of E - Learning. Major technical advancements have converted the early LMS into a sophisticated program for organizing curriculum, giving rich-content course materials, assessment, and interactive collaboration since its beginnings. With numerous current research areas dealing with various technologies connected to the LMS, the structure, operations, and implementation of the LMS will undoubtedly alter in the future. The Internet of Things is the most crucial technology that is predicted to alter many parts of the future (IoT). We present a conceptual framework for a forthcoming E Learning with Intelligent automation in this article.
We discuss how IoT will influence many aspects of the E - Learning, as well as the projected improvements and adjustments that IoT will bring to E- learning functionality. In this paper, we emphasize on the use of IoT services in the context of e-learning. We outline numerous services now provided by existing learning management systems in our framework, and show how these services will transform if they are merged with IoT services and connected to IoT modules and gadgets. We also suggest some additional services which will be achieved as a result of the integration of IoT into E-learning. We describe how each proposed E-learning. service will be implemented inside the E-learning., how it will be linked to other E-learning. services, and how it will be utilized to improve teaching and learning processes on the university campus
Geçmişten günümüze kadar özellikle evlerimizde kullandığımız ve yaşam alanlarımızın
vazgeçilmez ürünü olan halıların hem tasarım hem de üretim yönünden köklü bir geçmişi
bulunmaktadır. Binlerce yıllık geçmişe sahip olan halı, dünyada giderek önem kazanmış ve
dünya ticareti içerisinde kayda değer ürün gruplarından birisi haline gelmiştir. Günümüzde
makine halıları ya dokuma tekniği kullanılarak ya da tufting metodu kullanılarak
üretilmektedir. Üretilen bu makine halılarının kalitesi için ürünün yapısal özellikleri önem arz
etmektedir. Müşterinin bir halıda bulunmasını istediği akustik yalıtım, konfor, termal yalıtım,
görünüm, dayanıklılık ve güvenlik gibi özelliklerin arka planında yer alan halının yapısal
özellikleri belirleyici niteliktedir. Makine halısının yapısal kalitesini belirleyen faktörlerin
başında ise büyük oranda hav ipliğinin yapısı gelmektedir. Yapı içerisindeki hav ipliği
incelendiğinde; sentetik liflerin sahip oldukları yüksek mukavemet ve aşınma direnci özelliği,
kolay temizlenebilme özelliği, kimyasallara karşı yüksek direnç gösterme özelliği gibi
avantajları sayesinde tekstil alanında yaygın olarak kullanıldıkları görülmektedir. Yapılan bu
çalışmada aynı üretim parametrelerinden oluşmuş farklı kesit şekillerindeki polyester iplik
kullanımının makine halıları üzerindeki etkisinin araştırılması amaçlanmıştır.
E-learning technology based on multimedia has had a tremendous impact on our daily
teaching and learning activities. It has centred the learning process on the student. E-learning
has become more fascinating than traditional education systems due to the usage of
multimedia technologies. It began in the 1980s and has continued to develop since then. This
innovative technology provides pupils with the benefit of studying in a fresh way. E-learning
approaches make education more engaging and fascinating. It has also had a bigger influence
on our culture and school system. It is now simple to study and teach without being fatigued.
E-learning creates virtual learning environments on the internet via teacher-student
interactions, online assessment, and interactive multimedia-based course material
dissemination. Interactive multimedia delivers various kinds of media and suitable content
delivery based on learners' learning styles, which improves learners' learning effectiveness.
This article discusses the rapidly evolving technology of "Multimedia and e-education."
Smart teaching approaches are increasingly being used in schools, as well as other institutions
and organisations. E-textbooks are electronic textbooks that are used in a learner's actual
class. The digital textbook makes advantage of the most recent smart gadget and technology.
Keywords: Multimedia, Education, E- Learning, digital classroom
The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.
In case of initial alignment process in AUV, the measurement accuracy of inertial measurement device is not high, as a result, the system has a low self-initial accuracy. To solve the problem above, a method of integrated initial with DVL is proposed in initial alignment, which can improve initial accuracy with velocity provided by DVL. As well, due to own characteristics and special nature of work environment of AUV, nonlinear filter methods are utilized in AUV initial alignment with large azimuth misalignment angle. Square root central difference Kalman filter (SRCDKF) takes square root of covariance in recursive processes. Compared with extended Kalman filter (EKF), SRCDKF not only augments the estimation accuracy, but also avoids calculating the Jacobian matrix, and compared with CDKF, SRCDKF can significantly reduce amount of calculation and improve the numerical stability. However, SRCDKF may be restricted by uncertainty of system dynamic model and inaccuracy of noise covariance. Gaussian process regression SRCDKF (GP-SRCDKF) is proposed in this paper to overcome the limitations above. Gaussian process regression is introduced in SRCDKF to get system regression model and noise covariance, state equation and observation equation are substituted by Gaussian process regression model, and corresponding noise covariance carries on real-time adaptive adjustment. According to simulation results, the effectiveness and superiority of GP-SRCDKF in AUV integrated alignment with large azimuth misalignment angle is fully verified.
High performance path following control, especially straight line path tracking, is one of the most difficult problems in autonomous control of under-actuated ship. In this paper, we propose a straight line path following control algorithm by combining adaptive yaw angle feedback, back-stepping and acceleration feedback technique together. The most absorbing advantage of the proposed controller is that it not only reserves the good performance of back-stepping controller and bring much faster convergent rate, but also eliminates the disturbance impacts, which is very important in real applications. The simulation results with respect to a training ship model have shown the feasibility and validity of the proposed method.
A robust controller based on sliding mode method is presented to force one ship of three degrees of freedom to track a desired trajectory. Neural-network is designed to mimic an equivalent control law in the sliding-mode control, and a robust controller is chosen to curb the system dynamics on the sliding surface, thus the asymptotic stability property of closed system can be guaranteed. Furthermore, an adaptive estimation law based on Lyapunov stability theorem is employed to estimate the nonlinear uncertainties hydrodynamics of closed system. Numerical simulations illustrate the excellent tracking control performance of ship which suitable for engineering application demand.
In case of the characteristics that SINS/GPS error model is nonlinear with large azimuth misalignment angle, cubature Kalman filter (CKF) is widely utilized these years. However, in actual alignment process, due to instability of satellite signal, gross errors will exist in observation vector, which will affect estimation effect of CKF. As well, CKF will be restrict by uncertain system dynamic model and inaccurate noise statistical characteristic. To solve the problems above, Robust estimation principle and adaptive estimation method is introduced in CKF, Robust Adaptive CKF is proposed in this paper to carry on initial alignment, and which improves the robust and adaptive capacity of CKF. According to simulation results, Robust Adaptive CKF not only overcomes the filter affection of observation gross errors and inaccuracy of noise statistical model, but also has a higher estimation accuracy than CKF, improves system accuracy and reliability further, and the effectiveness and superiority of Robust Adaptive CKF in SINS/GPS initial alignment with large azimuth misalignment angle is fully verified.
The problem of path following control for underactuated surface ship is addressed in this paper. Path tracking error dynamics are obtained using diffeomorphism transformation and virtual guidance in Serret-Frenet frame. The linear feedback term is designed in kinematic loop to stabilize the position tracking subsystem, which can avoid the complexity of recomputing the derivative of stabilization term, and then the path following error can be guarantee stable. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.
This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori because of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions characterized by bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.
We present a depth controller design for a torpedo-shaped autonomous underwater vehicle (AUV) known as STARFISH. It is common to design an AUV to be positively buoyant, so that it will float to the surface in case of power failure. However, most depth controllers are designed with a neutral buoyancy assumption by regarding the extra buoyancy as a disturbance. In this paper, we study the effect of buoyancy on both pitch and heave dynamics of an AUV, and propose a controller scheme that specifically compensates for the effect. We propose a simplified model for pitch dynamics that takes into account the buoyancy of the AUV. We identify the parameters of the model from field data from a closed loop depth maneuver. We adopt dual loop control methodology with inner pitch control loop and outer depth control loop. The inner pitch controller is designed using sliding mode control (SMC) with integrator effect to overcome a constant offset term due to positive buoyancy of the AUV. Then, a simple proportional controller is designed in the outer loop for depth control. Positive buoyancy of the vehicle will induce heave motion of the AUV. Thus, in order to maintain depth, the AUV need to be pitch down at certain angle. An adaptive feedforward controller is designed to compensate for this angle. The dual loop design with inner SMC and outer proportional control with feedforward loop was shown to be effective through experiments in both lake and sea.
To solve the fluctuations of temperature and humidity in artificial climate chest and to realize accurate control, a more accurate model considering backlash characteristics of sensors which may cause the fluctuations is firstly proposed in this paper. Next, based on the model and method of stability equations, the relation between controllers' parameters and the existence of limit cycle oscillation is deduced to explain the output fluctuations. Furthermore, a strategy of control parameter selection is presented to predict and remove limit cycles. Experiments results show that the strategy is effective and can improve the accuracy of artificial climate chest control.
In this paper, based on the dynamics model, taking into account the disturbance and control constraint, diving depth asymptotic stability controller of autonomous underwater vehicles is designed. Based on the establishment of the mathematic model of AUV and the linearization of processing , and assumed that the hydrodynamic parameters of the vehicle nonlinear model are precisely measured, the control question researched have much practical significance by introducing bending deflection of fin angle control which has rigid limit characteristics. By using linear matrix inequality processing method, a robust tracking controller is designed. Depth asymptotically tracking of AUV is realized under the condition of control constraint. The result of simulation shows that the depth controller is effectively accomplished in spite of control fin angle deflection constraint.
In this paper, adaptive control of low speed bio-robotic autonomous underwater vehicles (BAUVs) in the dive plane using dorsal fins is considered. It is assumed that the model parameters are completely unknown and only the depth of the vehicle is measured for feedback. Two dorsal fins are mounted in the horizontal plane on either side of the BAUV. The normal force produced by the fins, when cambered, is used for the maneuvering. The BAUV model considered here is non-minimum phase. An indirect adaptive control system is designed for the depth control using the dorsal fins. The control system consists of a gradient based identifier for online parameter estimation, an observer for state estimation, and an optimal controller. Simulation results are presented which show that the adaptive control system accomplishes precise depth control of the BAUV using dorsal fins in spite of large uncertainties in the system parameters.
This paper proposes a novel motion controller for autonomous underwater vehicle based on parallel neural network. The motion controller consists of a real-time part, a self-learning part and a desired state programming part, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. Finally, simulation is carried out on ZS-IV vehicle. The control performance is compared with that of neural network controller with different structures such as normal adaptive neural network. The results show that the AUV motion controller based on parallel neural network has a great possibility to solve the problems in the AUV control system design.