92 research outputs found
Intelligent Control Strategies for an Autonomous Underwater Vehicle
The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control
problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics
are highly non-linear, and the relative similarity between the linear and angular velocities about
each degree of freedom means that control schemes employed within other flight vehicles are not
always applicable. In such instances, intelligent control strategies offer a more sophisticated
approach to the design of the control algorithm. Neurofuzzy control is one such technique, which
fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture.
Such an approach is highly suited to development of an autopilot for an AUV.
Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in
Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots.
However, the limitation of this technique is that it cannot be used for developing multivariable
fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and
employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control
of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is
extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design
that can accommodate changing vehicle pay loads and environmental disturbances.
Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system
design, the well known properties of radial basis function networks (RBFN) offer a more flexible
controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both
ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form.
This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the
hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector,
Defence Evaluation and Research Agency, Winfrit
Design of robust control for uncertain fuzzy quadruple-tank systems with time-varying delays
Producción CientíficaThe robust H∞ observer-based control design is addressed here for non-linear Takagi-Sugeno (T-S) fuzzy systems with time-varying delays, subject to uncertainties and external disturbances. This is motivated by the quadruple-tank with time delay control problem. The observer design methodology is based on constructing an appropriate Lyapunov–Krasovskii functional (LKF) for an augmented system formed from the original and the delayed states. The bilinear terms are transferred to the linear matrix inequalities, thanks to a change of variables which can be solved in one step. Furthermore, by employing the L2 performance index, the adverse effects of persistent bounded disturbances is largely avoided. The proposed method has the advantage of relating the controller and Lyapunov function to both the original and delayed states. Then, the controller and observer gains are obtained simultaneously by solving these inequalities with off-the-shelf software (Yalmip/MATLAB toolbox). Finally, an application to a simulated quadruple-tank system with time delay is carried out to demonstrate the benefits of the proposed technique, showing a compromise between controller simplicity and robustness that outperforms previous approaches.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL
Numerical Treatment of Non-Linear singular pertubation problems
Magister Scientiae - MScThis thesis deals with the design and implementation of some novel numerical methods for non-linear singular pertubations problems (NSPPs). It provide a survey of asymptotic and numerical methods for some NSPPs in the past decade. By considering two test problems, rigorous asymptotic analysis is carried out. Based on this analysis, suitable numerical methods are designed, analyzed and implemented in order to have some relevant results of physical importance. Since the asymptotic analysis provides only qualitative information, the focus is more on the numerical analysis of the problem which provides the quantitative information.South Afric
Advances in PID Control
Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications
Advances in fuzzy rule-based system for pattern classification
Ph.DDOCTOR OF PHILOSOPH
Data Science: Measuring Uncertainties
With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
Novel strategies for process control based on hybrid semi-parametric mathematical systems
Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201
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