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This outstanding PhD thesis describes a range of optimal and predictive control solutions to improve the efficiency of solar thermal systems. The proposed methodologies encompass a variety of approaches, including Q-Learning model-free optimal control, optimal control with feedforward, hybrid nonlinear predictive control, stochastic nonlinear predictive control, stabilizing predictive control, learning-based predictive control, and adaptive predictive control. With a rigorous scientific approach, and reporting on modeling and simulation analyses, control theory development, and practical implementation in existing facilities, this work shows how to model uncertainties, compensate for system disturbances, and account for the plant operating configuration. All in all, this book offers extensive information on control algorithms applied to solar thermal systems, and validation methodologies to test them in real-world contexts.