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