Manuel Berenguel, Francisco R. Rubio

Advanced Control of Solar Plants (eBook, PDF)

• Format: PDF

Produktbeschreibung

There is some degree of separation between the development of advanced control algorithms within the research community and their use in industrial practice. Several strategies developed from experimental research into improving the efficiency of solar thermal power plants are here examined in the context of their industrial application.

The techniques described and applied are:

• modeling and simulation;
• adaptive control;
• model-based predictive control;
• frequency domain control and robust optimal control; and
• fuzzy logic control.

Their effectiveness in this control process is assessed and the various techniques' advantages and drawbacks are analyzed and compared. The results obtained can be readily extended to other industrial processes; in this context, the solar control process examined provides an ideal test-bed since it exhibits many of the problems found in other processes, such as nonlinearities, changing dynamics and strong external disturbances.

This is a comprehensive analysis of the practical application of different control strategies that will be of interest to control engineers working in solar power systems and throughout other process industries, and to researchers, scientists and graduate students in this field.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

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Produktdetails

• Verlag: Springer London
• Seitenzahl: 268
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781447109815
• Artikelnr.: 44062943

Inhaltsangabe

1.Introduction.- 1.1 The control of solar collector fields.- 1.2 Trends in process control.- 1.3 Modelling and Identification.- 1.4 Adaptive Control.- 1.5 Model-based Predictive Control (MPC).- 1.6 Robust control, frequency domain control and optimal control.- 1.7 Artificial Intelligence Techniques.- 2.Description and dynamic models of the plant.- 2.1 Plant description.- 2.2 Objective of the control system.- 2.3 Data acquisition system.- 2.4 Dynamic simulation models of the field.- 2.5 Analysis of the dynamic response of the plant.- 2.6 Linear plant models.- 3.Basic control schema.- 3.1 Feedforward control.- 3.2 Fixed Ziegler-Nichols rule based PID controllers.- 3.3 Backup controller.- 3.4 Fine-tuned PID controller.- 4.Basic structures of adaptive control.- 4.1 Parameter estimation algorithm.- 4.2 Supervisory levels.- 4.3 Adaptive Ziegler-Nichols rule based PID controllers.- 4.4 Pole-placement adaptive PI controller.- 4.5 Simulation analysis of PID controllers.- 4.6 Plant results with adaptive PI controllers.- 5.Model-based predictive control strategies.- 5.1 Generalized predictive control (GPC).- 5.2 Constrained generalized predictive control.- 5.3 Adaptive generalized predictive control.- 5.4 Robust adaptive model predictive control with bounded uncertainties.- 5.5 Gain scheduling generalized predictive control.- 5.6 GPC scheme with nonlinear prediction of the free response.- 6.Frequency domain control and robust optimal control.- 6.1 Adaptive frequency domain internal model control.- 6.2 Linear Quadratic Gaussian Optimal Control (LQG).- 7.Heuristic fuzzy logic control.- 7.1 Fuzzy logic inference scheme.- 7.2 Incremental fuzzy PI control (IFPIC).- 7.3 Fuzzy logic controller (FLC).- 8.Summary and concluding remarks.- 8.1 Performance indexes.- 8.2 Fixed PID controller.-8.3 Adaptive GPC controller.- 8.4 Robust adaptive GPC controller.- 8.5 Gain scheduling GPC controller.- 8.6 Nonlinear GPC controller.- 8.7 Frequency domain adaptive IMC controller.- 8.8 Robust LQG/LTR controller.- 8.9 Heuristic incremental fuzzy PI controller (IFPIC).- 8.10 Heuristic fuzzy logic controller (FLC).- 8.11 Conclusions.- References.