7.2.1 Introduction
This section presents automatic control system strategies for controlling the temperature of a solar furnace. As has been pointed out, solar furnaces are manually controlled by skilled operators due to the variety of sample materials and temperature profiles. In recent years, different control strategies have been developed to allow automatic control of solar furnaces, ranging from adaptive control [13, 43, 120, 121], fuzzy logic control [220, 221] and predictive control [48]. Many of these techniques are based on the physical model developed in [43].
This section shows the results obtained in the application of proportional-integral (PI) and fuzzy logic controllers (FLC) to a solar furnace. In the case of PI controllers, both fixed and adaptive versions of the controllers have been developed, incorporating feedforward (FF) action, anti-windup and slew-rate constraint handling mechanisms.
From the control viewpoint, a solar furnace is a system which presents several interesting characteristics making the control problem a difficult task:
• The characteristics of the samples are quite different depending on their nature (steel, alumina, etc.). Obtaining a fixed parameter controller which allows different samples to be controlled becomes a difficult task.
• The dynamic characteristics of each sample greatly depend on the temperature and introduce a high non-linearity which makes the behavior of the system controlled change with the operating conditions.
• The control specifications are quite severe (rate of temperature increase, rate of temperature decrease, variable step changes, etc.) and have to be achieved with small errors.
• The system suffers from strong disturbances caused by solar irradiance variations (slow variations due to the daily cycle or fast and strong variations due to passing clouds), which make the exact reproduction of the conditions of a determined test impossible.
• Limitations exist in the maximum temperature achievable by the materials and different constraints (non-linearities) in the actuator (amplitude, slew rate, etc.).
Recently, there have been extensions to solar-driven thermochemical processes [293], in which high-temperature process heat is supplied by concentrated solar energy, providing an efficient route for fuel and material production. In this case, a linear feedback controller was implemented using an optimal control design method (LQG/LTR).
