Advanced Control of Solar Plants
Published on 6. December 2012
XXVIII, 268 pages
978-1-4471-0981-5 (ISBN)
Description
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. This volume by Professor Eduardo F. Camacho and his colleagues Manuel Berenguel and Francisco R. Rubio is an exemplar of what an Advances in Industrial Control monograph should be. In it the control of a thermal solar facility is used to study the performance obtainable from an interesting range of control algorithms. These methods range from the conventional PID controller, through to model-based predictive and robust optimal control methods and finishing with two fuzzy logic based control techniques. The scientific methodology applied is modelling, simulation and plant implementation. In the last chapter, a rigorous approach for a comparative study is described involving a careful selection of performance metrics. The text is rich in relevant up-to-date source material, and contains many thought-provoking comments. The presentation is well-balanced, impartial and very readable.
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Manuel Berenguel | Francisco R. Rubio
Advanced Control of Solar Plants
Book
09/2011
Springer
€106.99
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Manuel Berenguel | Francisco R. Rubio
Advanced Control of Solar Plants
Book
07/1997
Springer
€85.55
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Content
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.
