Bøger i Advanced Textbooks in Control and Signal Processing serien

The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners.

MATLAB (R) files supplied to assist in working through exercises and examples. Solutions manual pdf.

This textbook presents theory and practice in the context of automatic control education. It presents the relevant theory in the first eight chapters,applying them later on to the control of several real plants. Each plant is studied following a uniform procedure: a) the plant¿s functionis described, b) a mathematical model is obtained, c) plant construction is explained in such a way that the reader can build his or her own plant to conduct experiments, d) experiments are conducted to determine the plant¿s parameters, e) a controller is designed using the theory discussed in the first eight chapters, f) practical controller implementation is performed in such a way that the reader can build the controller in practice, and g) the experimental results are presented. Moreover, the book provides a wealth of exercises and appendices reviewing the foundations of several concepts and techniques in automatic control. The control system construction proposed is based on inexpensive, easy-to-use hardware. An explicit procedure for obtaining formulas for the oscillation condition and the oscillation frequency of electronic oscillator circuits is demonstrated as well.

In order to deepen readers' understanding, simpler single-input-single-output systems generally precede treatment of more complex multi-input-multi-output (MIMO) systems and linear systems precede nonlinear systems.

MATLAB (R) files supplied to assist in working through exercises and examples. Solutions manual pdf.

Dead-time-process-control problems are studied using classical proportional-integral-differential (PID) control for the simpler examples and dead-time-compensator (DTC) and model predictive control (MPC) methods for progressively more complex ones.

Following an introduction on system theory, this book shows the reader how to approach the system identification problem in a systematic fashion. It aims to teach students the fundamentals of systems identification without unduly complicated mathematics.

This comprehensive and up-to-date book focuses on an algebraic approach to the analysis and design of discrete-time signal processors, including material applicable to numeric and symbolic computation programs such as MATLAB. Written with clarity, it contains the latest detailed research results.

The only book on the subject at this level, this is a well written formalised and concise presentation of the basis of statistical signal processing. It teaches a wide variety of techniques, demonstrating how they can be applied to many different situations.

Robotics provides the know-how on the foundations of robotics: modelling, planning and control. It covers mobile robots, visual control and motion planning. A variety of problems are worked through, and the tools to find engineering solutions are explained.

Industrial examples illustrate the concepts and methods discussed, making this book an invaluable aid for students embarking on further courses in control, manufacturing engineering or computer studies.

How can a signal be processed for which there are few or no a priori data? This text covers Kalman and Wiener filters, neural networks, genetic algorithms and fuzzy logic systems together in a unified treatment. It is useful for one-semester introductory graduate or senior undergraduate courses.

A comprehensive introduction to the most popular class of neural network, the multilayer perceptron, showing how it can be used for system identification and control.

This book introduces the advantages of parallel processing and details how to use it to deal with common signal processing and control algorithms. The text includes examples and end-of-chapter exercises, and case studies to put theoretical concepts into a practical context.

The book presents a clear understanding of a new type of computation system, the Cellular Neural Network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations.

Introduces the proper tools to find engineering-oriented solutions. This book includes fundamental coverage of kinematics, statics and dynamics of manipulators, and trajectory planning and motion control in free space. It offers technological aspects that include hardware- and software-control architectures and industrial robot-control algorithms.

Presents you with a course in self-tuning control, beginning with a survey of adaptive control and the formulation of adaptive control problems. This book also deals with modelling and identification before passing on to algebraic design methods and particular PID and linear-quadratic forms of self-tuning control.

Robust Control Design with MATLAB (R) (second edition) helps the student to learn how to use well-developed advanced robust control design methods in practical cases.

This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.

Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs.

This comprehensive book gives a overview of the latest discussions in the application of genetic algorithms to solve engineering problems. Featuring real-world applications and an accompanying disk, giving the reader the opportunity to use an interactive genetic algorithms demonstration program.

Addresses robot control in depth, treating a range of model-based controllers in detail: proportional derivative; proportional integral derivative; computed torque and some adaptive variants. This book includes other areas of study important to robotics, such as kinematics, and presents case studies. It also offers auxiliary resources.

This MATLAB exercise book accompanies the textbook Control Engineering, providing a platform for students to practice problem solving in the analysis and design of continuous and discrete control problems reflected in the main textbook.

This book shows how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. It details how to convert theoretically-based control algorithms into realistic real-world aerospace applications.