## 1 Introduction  
The output power produced by a solar plant is proportional to the amount of solar energy absorbed by the system. Therefore, a sun tracking system (STS) with a high degree of accuracy is necessary to avoid losses in the output power of solar plants. STSs are usually classified into two categories [1]: passive sun tracking systems, which use the expansion of a gas caused by the solar radiation to move the mechanical structure of the tracker, and active sun tracking systems, which use motors, gears and electric controllers to drive concentration and absorption devices in a solar plant. There are two types of active sun tracking systems based on their controlling methods [2]: sensor driver systems (SDSs) and microprocessor driver systems (MDSs). SDSs use photosensors in order to detect a change in light sources and convert it into an electrical signal, which is used to obtain the position of the sun. However, there are tracking errors when the sensors cannot produce an electrical signal due to low solar radiation levels produced by the presence of passing clouds or contamination in the air. MDSs use microprocessors and computer systems to execute mathematical equations based on solar position algorithms and the current date and time to determine the exact position of the sun. MDSs are cheaper than SDSs; however, there is no feedback to verify the position of the sun, and tracking errors may appear due to the precision of the solar position algorithm. Several algorithms with different levels of complexity and accuracy can be found in the literature [3], where the use of a more precise solar position algorithm increases both the accuracy and the computational effort of the system. Central tower power plants use two-axis sun reflectors called heliostats, which reflect the solar irradiance into a collector tower. Every heliostat has a local control which drives two motors connected to reduction gears, where the trend is to give greater autonomy to the central control by increasing the intelligence of the local control of each heliostat. Additionally, in solar plants, there are changing dynamics due to non-linearities and uncertainties that traditional PID controllers cannot handle. This is because a PID controller may produce high oscillations when it is tightly tuned, and the dynamics of the process varies due to changes in the operating conditions. Hence, the use of more efficient control strategies results in better responses [1]. An FLC is a good alternative to traditional PID controllers, because it can deal with non-linear systems and can be designed by using the knowledge of a human operator without knowing the mathematical model of the system. Although the FLC does not have a better response in time domain than a PID controller, this later one cannot be applied to systems which have a quick change of parameters because it would require to adjust the value of its control gains [4]. FLCs [5; 6; 7; 8; 9; 10] and hybrid PID-FLCs [4; 11; 12; 13; 14; 15; 16] have been applied to control the position of DC motors, showing a good output response and better performance against traditional PID controllers. Furthermore, FLCs have been implemented to control two-axis sun trackers for photovoltaic systems. [PERSON] [17] was the first to develop a PC-based FLC algorithm to control a two-axis photo-voltaic (PV) solar panel. Afterwards, the FLC has been implemented in different platforms and devices such as microcontrollers [18; 19; 20], DSPs [21], personal computers (PCs) [22; 23; 24; 25] and field-programmable gate arrays (FPGAs) [26]. Finally, the FLC has also been applied in orientation control of heliostats. [PERSON] and [PERSON] [27] performed a comparison between a classical PI and PID controller, a PI-FLC and a PID-FLC for the orientation control of a laboratory-scale heliostat with two mirrors of 0.9 m \(\times\) 0.7 m, two 15 W DC motors, and a data acquisition system with 20 ms sampling time. The FLC uses three membership functions in order to adjust the PID controller gains.