## 1 Introduction
A control algorithm modifies the angular position of the motors, allowing the mechanical structure to remain a stable position despite changes in the environmental conditions [1]. Besides, a unique central control is used to communicate with the local controls through a wireless communication system. However, due to the limited bandwidth in wireless communication, it is difficult to control the orientation of each heliostat from the central control with a high update rate, which increases the possibility of errors. Therefore, every local control must calculate the sun position and the aim point in order to move the heliostat to the desired position, reducing the network traffic between the central and the local control, and increasing its autonomy [14]. A SCADA system integrates the information received from the local controllers (also called remote terminal units), by using a supervisory unit (SU) embedded into a computer, in order to analyze the data, supervise the process, and make decisions by using a human-machine interface [1]. Some heliostat control systems have been presented in the literature. [PERSON] and [PERSON] [15] developed a heliostat control by using Java language on a computer, which calculates the angles of the position of the sun and the heliostat, sending them via Bluetooth to a microcontroller-based card, in order to control a 0.16 m\({}^{2}\) heliostat by using two low-power DC motors. The heliostat sends its actual position to the computer, which compares it with the calculated values and corrects the position of the heliostat if there is a difference between the values. [PERSON] and [PERSON] [16] developed a heliostat control in an embedded system using an industrial computer model ARK 1388V, which performs all the tasks of the heliostat. The control system orients a 1 m\({}^{2}\) heliostat by using two stepper motors, a programmable controller, and a driver circuit. The system moves the heliostat every four minutes, by using the previous and new position of the heliostat in order to calculate the step number of the motors. [PERSON] et al. [17] developed a control system with a graphical user interface (GUI) to monitor a field of heliostats, allowing to control multiple receivers and types of heliostats in a single application. The system can be updated by adding new heliostats in the field and more than one receiver. Finally, an advanced control strategy, such as a fuzzy logic controller, is an excellent alternative to deal with changing dynamics and non-linearities present in solar plants that traditional controllers cannot handle [1]. Fuzzy logic controllers have been applied in order to control the orientation of a heliostat. [PERSON] and [PERSON] [18] applied a speed control of a DC motor with an FLC for the orientation of a heliostat. Two FLC configurations were compared with a neural controller in Matlab software. The first configuration (FLC1) uses two inputs (error and derivate of the error), one output, and a rule base of ten rules, whereas second configuration (FLC2) uses only one input (error), one output, and a rule base three rules. The results demonstrated a better output response for the FLC compared with the neural controller, which presents some fluctuations that can be harmful to the DC motors. [PERSON] et al. [19] applied an adaptive fuzzy-proportional-integral (F-PI) controller in order to orient a heliostat by controlling a DC motor in Matlab software. The F-PI controller was compared with two FLC configurations (FLC1 and FLC2) and a conventional Proportional-Integral (PI) controller. The F-PI controller modifies the controller gains of a PI controller, by using two inputs (error and derivate of the error), two outputs (proportional gain and integral gain) and a rule base of thirty-five elements for each output. The results showed that the F-PI controller gives the best results compared to other methods. However, these works have only been presented in simulations.