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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.

Advanced Control of Solar Plants

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.- 1.4.1 Adaptive Control Structures.- 1.5 Model-based Predictive Control (MPC).- 1.5.1 Nonlinear MPC control techniques.- 1.6 Robust control, frequency domain control and optimal control.- 1.6.1 The robust control problem.- 1.6.2 Control methods in the frequency domain.- 1.6.3 Optimal control methods.- 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.- 2.4.1 Concentrated parameter model.- 2.4.2 Distributed parameter model.- 2.4.3 Model validation.- 2.5 Analysis of the dynamic response of the plant.- 2.5.1 Analysis of the time response.- 2.5.2 Analysis of the frequency response.- 2.6 Linear plant models.- 2.6.1 Low order linear plant models.- 2.6.2 High order linear plant models.- 3.Basic control schema.- 3.1 Feedforward control.- 3.1.1 Parallel feedforward compensation.- 3.1.2 Series feedforward compensation.- 3.1.3 General comments about 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.- 4.1.1 Parametric identification.- 4.1.2 Recursive least squares identification algorithm (RLS).- 4.1.3 Stability and robustness in the identification.- 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.- 5.3.1 Introduction.- 5.3.2 Application to the distributed solar collector field.- 5.3.3 Simulation studies.- 5.3.4 Plant results.- 5.4 Robust adaptive model predictive control with bounded uncertainties.- 5.4.1 Introduction.- 5.4.2 Robust identification mechanism.- 5.4.3 Robust adaptive model predictive control.- 5.4.4 Simulation studies.- 5.4.5 Plant results.- 5.5 Gain scheduling generalized predictive control.- 5.5.1 Introduction.- 5.5.2 Plant models and fixed parameter controllers.- 5.5.3 Gain scheduling control of the distributed solar collector field.- 5.5.4 Plant results.- 5.6 GPC scheme with nonlinear prediction of the free response.- 5.6.1 Nonlinear GPC scheme.- 5.6.2 Incremental formulation of predicted disturbances.- 5.6.3 Application to the distributed solar collector field.- 5.6.4 Simulation studies.- 5.6.5 Plant results.- 6.Frequency domain control and robust optimal control.- 6.1 Adaptive frequency domain internal model control.- 6.1.1 Introduction.- 6.1.2 The IMC control structure.- 6.1.3 Stability and performance of IMC.- 6.1.4 Frequency domain interpolation.- 6.1.5 Adaptive IMC in the frequency domain.- 6.1.6 A case study: linear system with one antiresonance mode.- 6.1.7 Application to the distributed solar collector field.- 6.2 Linear Quadratic Gaussian Optimal Control (LQG).- 6.2.1 Introduction.- 6.2.2 The LQR and LQG regulators.- 6.2.3 Establishment of the LQG method in the frequency domain.- 6.2.4 Loop transfer recovery (LTR).- 6.2.5 LQG/LTR design method.- 6.2.6 Output recovery.- 6.2.7 Application to the distributed solar collector field.- 7.Heuristic fuzzy logic control.- 7.1 Fuzzy logic inference scheme.- 7.2 Incremental fuzzy PI control (IFPIC).- 7.2.1 Application to the distributed solar collector field.- 7.2.2 Plant results.- 7.3 Fuzzy logic controller (FLC).- 7.3.1 FLC design procedure.- 7.3.2 Plant results.- 8.Summary and concluding remarks.- 8.1 Performance indexes.- 8.1.1 Robustness analysis.- 8.1.2 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.