Screen LibraryPredictive approaches to control of complex systems /
A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident...
Saved in:
| Main Authors: | |
|---|---|
| Corporate Authors: | |
| Group Author: | |
| Published: |
Springer,
|
| Publisher Address: | Berlin ; New York : |
| Publication Dates: | 2013. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Studies in computational intelligence,
454 |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-642-33947-9 |
| Summary: |
A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems. |
| Carrier Form: | 1 online resource. |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9783642339479 (electronic bk.) 3642339476 (electronic bk.) |
| Index Number: | TJ217 |
| CLC: | TP273 |
| Contents: |
Hybrid dynamics -- Piecewise affine and equivalent models -- Hybrid fuzzy model -- Unsupervised learning methods for identification of complex systems -- Batch reactor -- Modeling and identification of the batch reactor: the PWA approach -- Modeling and identification of the batch reactor: the HFM approach -- Introduction to predictive control of complex systems -- Solving mixed-Integer optimization problems -- Predictive control based on a reachability analysis -- Predictive control based on a genetic algorithm -- Self-adaptive predictive control with an online local-Linear-Model identification -- Control using an inverse hybrid fuzzy model -- Conclusion. |