Intelligent Control Using Metaheuristic Optimization for Buck-Boost Converter

)is research aims to introduce an intelligent controlling system of windmill-generated voltage connected to a load of 12V. As natural wind speed lacks consistency, the resultant irregular voltage can lead to system damage. In the experiment, a buck-boost converter is not only designed to control such voltage but also tuned by intelligent methods. It is very challenging to control the system. PI controller is developed usingmetaheuristic optimization, an artificial fish-swarm algorithm (AFSA). In testing, the buck-boost converter is controlled by the PI controller at a reference voltage of 14V and supplied with an input voltage (Vin) in the range of 5–100V.)e result shows that, even with inconstant (Vin), the system can effectively control the reference voltage at 14V.


Introduction
Energy is quite essential for modern life: almost every human activity nowadays needs energy, most commonly in the forms of heat and electricity, with the main source currently being fossil fuel formed by burying biomass, e.g., natural gas and coal. Not only are these sources of energy limited and nonrenewable, but such energy production also causes pollution to the environment, including the atmosphere, i.e., the greenhouse effect [1]. erefore, it is necessary to change the energy usage behavior and to seek alternative energy.
One of the alternatives attracting attention is wind power, as the wind is clean, pollutionless, and ubiquitous. It is also continually available both day and night. Production of wind energy involves conversion of kinetic energy into electrical energy: the turbine turns the airflow's kinetic energy into mechanical power and drives the permanent magnet synchronous generator (PMSG). e obtained alternating current later goes through a rectifier and becomes direct current. However, the natural wind speed is not constant, causing the electric pressure gained from PMSG to exhibit high variation, which leads to inconstant electrical supply and discontinuous operation [2]; the same has been stated in a previous study by Chandan and Chayapathy. Furthermore, in the event of low wind speed, it is insufficient to recharge batteries. For this reason, a buck-boost converter is introduced for maintaining voltage [3].
In a precedent work, Mittal and Arora have simulated windmills in MATLAB/Simulink [4] using PMSG and employed a buck-boost converter to control the electric pressure through such circuits as a chopper and inverter.
is conforms with Porselvi and Muthu's designed wind energy conversion system with boost converter and CHBMLI with single DC input, which retains the voltage at the DC link suitable for the fluctuating wind pressure [5]. Moreover, another study by Kamalakkannan and Arunkumar utilizes a buck-boost converter in upholding the voltage output at the DC-DC link [6]. Both suggest the necessity of controlling the changing electric tension at the DC-DC link onto an appropriate level for the gridconnected system.
In this study, a buck-boost converter is thus chosen as a switching device to adjust the unstable electric pressure at the DC-DC link to a reference voltage of the system [7], since the buck-boost converter possesses the function of increasing and decreasing electric pressure, which resembles the natural wind speed. Valenzuela and Alarcon proposed the control of a boost DC-DC power converter using the new controller under constrained input, which is the duty cycle, the physically admissible values. Moreover, the uncertain supply voltage and unmeasurable inductor current are used as an observer for the proposed control law [8]. Ortigoza et al. used a two-stage control design which performs the sensorless angular velocity trajectory tracking task for the buck power converter/DC motor system. Not only is a firststage controller based on the differential flatness property of the DC-motor model, but also a second-stage controller is based on flatness property on the buck power converter model to provide the input voltage to the DC motor [9]. Licea et al. presented a reconfigurable topology which consists of a reconfigurable buck, boost, and buck-boost DC-DC converter. Furthermore, a robust controller is designed by polytopic representation, and a Lyapunov based switched stability analysis of the closed-loop system is presented. e experimental results show that the robust stability under arbitrarily fast parameter variations and reconfiguration changes [10].
However, a proper standard value of PI controller was tuned by various approaches. One interesting approach is the optimization algorithm, a mathematical process to obtain an optimal value. A supporting study by Siano and Citro suggests using multiobjective particle swarm optimization to design a fuzzy-logic controller for buckboost converters with inconstant change of voltage [11]. e result obtained the quantity or numeric value of the problems set, to be further used as the suitable standard value. Similarly, Jalilvand et al. apply advanced particle swarm optimization, a sampling evolutionary algorithm efficient in finding optimal solutions in adjustment of PID controller's parameters [12]. is is in accordance with Tehrani et al., who propose a theory and an adaptation of the multipurpose strength Pareto evolutionary algorithm (SPEA). is controlling method dynamically responds to the required PID sorting coefficient [13]. In controlling PI controller, thus, a controller is required to control the standard PI. Furthermore, Liu and Hsu adopt the particle swarm optimization (PSO) technique for a static synchronous compensator (STATCOM) in finding a suitable standard value of intelligent PI controller [14]. Ultimately, Çınar and Akarslan design an intelligent battery charger controller for PV panels due to the its various capabilities [15]. Hence, a PI controller is operated to control the farming load system, the battery charger system, and the system which can supply to grid-connected system. Yau et al. apply two-stage system which implements maximum power point tracking and optimal charge control of Li-ion battery. ey use PI to control charge controller parameters. In determining the optimum parameters, the intelligent algorithms, PSO, and genetic algorithms (GAs) are utilized. e optimal parameter results for the controller of PSO have better performance than GA [16]. e purpose of this research is to design a buck-boost converter for controlling the reference electric pressure at 14 V and to develop a PI controller enhanced with metaheuristic optimization, artificial fish-swarm algorithm (AFSA), as an intelligent voltage controlling system of an 18A load for continual usage.

eories
2.1.1. Wind Energy. Wind is one of the clean and renewable natural sources. Wind formation results from solar radiation upon Earth. Receiving and absorbing unequal amounts of heat leads to difference in temperature and atmospheric pressure between geolocations. Air then lifts up in areas with high temperature or low atmospheric pressure and is replaced by the air from cooler areas or higher atmospheric pressures. is movement of air mass causes the wind and its motion's kinetic energy is harvestable. e wind energy has been harnessed more and more, especially in producing electrical power. In ailand, when compared to other countries, such advantages of wind energy have been little brought into use [17].
Winds in ailand are seasonal, following rather precise annual pattern of occurrence. To exemplify, monsoons, e.g., Southeast Asian monsoon, occur with unchanged direction and last for the entire season. As Asia covers a large area, the temperature and atmospheric pressure between the inland and coastal regions vastly differ, leading to distinct wind direction in each season.

Buck-Boost Converter.
e buck-boost converter is a type of circuit which transforms one electric pressure to another, either higher or lower, within the same circuit. Its function is based on the principle of cutting the input direct voltage into parts using a switching device, controlled by signals of the pulse width modulation (PWM) at a particular frequency. e control frequency which yields the buckboost converter's highest efficiency is reached when the time period (T) is the lowest, at 100 times the switching rate, as shown in Figure 1 [18]. e operation of a buck-boost converter can be divided into two modes, according to the condition of the power switch device: (1) Buck-Boost Converter Switch, Closed Circuit Mode. From Figure 2, the current flows from the electrical power source through the switch and the inductor (L) cause the inductor to store power in the form of magnetic field [19], explained by the following equation: where V S is the voltage drops across the supply and V L is the voltage drops across the inductor. Hence, Δi L /Δt is the rate of change of the current in closed switch, where t on � DT s and D is the duty cycle at t on : Journal of Engineering such that (2) Buck-Boost Converter Switch, Open Circuit Mode. Figure 3 shows the operation mode after the closed circuit condition. When the circuit is closed, the energy accumulated in the inductor creates self-induction, reversing the direction of electric pressure across the inductor and transforming itself into an electrical supply. Nonetheless, the current still flows through the inductor in the same direction as in the closed circuit. e diode is forward-biased, as in the closed circuit condition [12,13], as described by the following equation: where V S is voltage drops across the supply and V L is voltage drops across the inductor. V 0 is an output of buck-boot converter Hence, Δi L /Δt is the rate of change of the current in open switch where t off � (1 − D)T S , and D is the duty cycle at t off .
Combining (3) and (6), a change rate of the current can be rewritten as

Designed Circuit of Buck-Boost
Converter. e equivalent circuit design of the buck-boost converter is shown in Figure 4.
Based on Figure 4, the parameters designed for buckboost converter are shown in Table 1.
According to Figure 4 the designed circuit of buck-boost converter can receive an input voltage (V in ) in the range 5-100 V with standard value V out at 14 V compatible with a load of 18 A. e load resistance is calculated as 0.78 Ω according to Ohm's law. All these parameters are revised, while the voltage input to the buck-boost converter ranges from 0 V to 100 V as the input should be lower or higher than the reference pressure V out � 14 V. e output response of the buck-boost converter uses an inductance of 33.6 mH, a capacitance of 110 μf, and PWM signals for driving IGBT constant switching frequency at 4 kHz. e state vector for the buck-boost converter is defined as where I L is the current through the inductor and V C is the voltage across the capacitor. For the given duty cycle of 78%, the system is represented by the following set of continuous time state spaces equations: where _ x is the state vector, V S is the source vector, and A, B, C, D are the state coefficient matrices. e state model of the buck-boost converter is provided by the two modes as follows: e closed circuit mode is defined as e open circuit mode is written as Load

Journal of Engineering
where e state space model is of the following form: where en d, L and C are substituted as shown in Hence, the transfer function of the buck-boost converter is shown as From Figure 5, the process gain is defined as In substituting variables in (21), the equation yields K S � 3.54. According to Figure 5, τ � 0.89, K S is a process gain, and τ is a time constant.

PI Control of First-Order Systems.
Suppose that the process can be described by the following first-order model [20]: In substitution, the transfer function of the controller for buck-boost converter is rewritten as Figure 6 illustrates the first-order model buck-boost converter design.   Voltage Duty cycle 78% 4 Switching frequency (f) 4 kHz 5 Load Inductance (L) 33.6 mH 7 Capacitance ( e two parameters are presented, the process gains K s and the time constant (T i s). By controlling this with the PI controller [21] provided as a buck-boost converter design with PI controller is achieved.
According to Figure 7, a second-order closed-loop system is obtained as e two closed-loop poles can be chosen arbitrarily for a suitable choice of the gain K p and the integral time T i of the controller. e poles are given by the characteristic equation [13] 1 + Gp(s)Gc(s) � 0. (26) Suppose that the desired closed-loop poles are characterized by their relative damping ς and frequencyω; the desired characteristic equation then becomes Substitution yields e coefficients of these two characteristic equations for determining K p and T i are written as where ζ � 0.707; note that in order to have positive controller gains, it is necessary that the chosen bandwidth (ω) be larger than 1/2ξτ. If ω is very large, the integration time T i is given by and ω is hereby calculated as From the design, ζ � 0.707 and then ω � (1/(2 × 0.707 × 0.89)) so that ω � 0.795.

Journal of Engineering
By substituting the parameters into (29) and (30), the calculation of K P and T i is shown as e simulation of closed-loop control using PI controller designed by pole placement method via input step command 0-14 V is shown in Figure 8. (AFSA). AFSA, metaheuristic optimization, is a decision-making process which employs mathematical and logical reasoning in selecting steps reason principle for choosing method or operation until the last step of separating and ordering the operation processes to increase the efficiency of finding and solving problems. Formulation of AFSA is inspired by the behavior of a school of fish, imitating their survival and feeding in nature. e characteristics can be categorized by behavior for finding solutions as follows: [22].

Artificial Fish-Swarm Algorithm
(1) Random behavior: in order to find companion and food, a fish swims randomly in the water (2) Chasing behavior: if food is discovered by a fish, the others in the neighbourhood go quickly after it (3) Swarming behavior: in order to guarantee survival of the swarm and avoid dangers from predators, fish move together in schools

Journal of Engineering
(4) Searching behavior: fish go directly and quickly to a region, when more food is discovered, by instinct or vision (5) Leaping behavior: fish leap to look for food in other regions, when they stagnate in a region e binary version of AFSA can be given as Figure 9. Table 2 shows the parameters set for AFSA in tuning the PI controller. Table 3 show the gains K p and T i in AFSA for the intelligent tuning method for PI controller. From Table 3, the gains K p and K i are found optimal, using AFSA, at K p � 1.37 and T i � 0.812 with the minimum cost function of 0.1949. us, the aforementioned values are chosen. e simulation of closed-loop control using PI controller tuned by AFSA via input step command 0-14 V is shown in Figure 10. Figure 11 shows the comparison of the closed-loop control, with the output voltage via input step command 0-14 V, using PI controller designed by pole placement method and tuning by AFSA. Figure 12 shows the block diagram of an intelligent voltage control system with embedded buckboost converter controller with input from the wind turbine and standard value at 14 V.

Proposed System.
is is under the following conditions: in case that V ≥ 5, supply control circuit on buckboost converter; if V < 5, supply control circuit on battery; when V ≥ 30, farming system on buck-boost converter; in case that V < 30, farming system on battery; if V > 4, battery charger on buck-boost converter; and when V > 60, gridconnected system on buck-boost converter. Figure 13 portrays an electricity generating system powered by a wind turbine. e kinetic energy obtained from the wind flowing through the blades of windmill transforms into mechanical energy driving the shaft connected to the PMSG. e obtained alternating voltage is then rectified. Nonetheless, the voltage input to PMSG depends significantly on the fluctuating wind speed. A buck-boost converter, therefore, is used as a control device to ensure a suitable and constant voltage via a PI controller. Furthermore, suitable values of gains K p and T i are achievable via metaheuristic optimization using AFSA, taking into consideration the output voltage from the buck-boost converter (V out ) and the reference voltage (V ref ). V ref is used in setting V out via an embedded system using Arduino Mega board.
Arduino Mega is a microcontroller board with ATmega2560 chip, which has 54 digital input/output pins. ese include 15 pins used as PWM, 16 pins as analogy inputs, and 4 sets of the UART. e board's crystal frequency is 16 MHz. Arduino Mega also enables direct data transfer with computers via the board's USB port. Its design is compatible with various types of shield. is facilitates and fully supports program development on the Arduino platform. e load conditions are coded as shown in Figure 14. Figure 15, the experimental result is compared with the simulation result of the closed-loop control, with the output voltage via input step command 0-14 V, using PI controller designed by pole placement method and tuning by AFSA.

Simulation and Experimental Results. In
According to Figure 15, the experimental result of the output voltage is tested. In the simulation, the output voltage of proposed system, both tuned gains the PI controller by AFSA and the PI controller using pole placement method, are compared. Figure 16 shows the experimental setup of the intelligent voltage control system with Arduino Mega board connected to a buck-boost converter using a PI controller adjusted by   Journal of Engineering AFSA at objective output voltage of 14 V using PI controller designed by pole placement method and tuning by AFSA. It can control operations of a supply control circuit system, farming load system, battery charger system, and gridconnected system. In the experiment where the reference output voltage value is set to 14V, the input supply voltage (V in ) inconstantly varies from 5 to 100 V, with no load present; the results for closed-loop control using PI controller designed by pole placement method are shown in the following figures. Figure 17 shows the results of the voltage control system using a buck-boost converter tuning gains of PI controller by pole placement method of closed-loop system at reference voltage of 14 V with no load, supplying input voltage (V in ) of 5-100 V. In Figure 17(a), the input supply voltage (V in ) starts from 5 V to 100 V. Even under step input of V in changing rapidly, the output response is overshooting with no steady state error. In Figure 17(b), when V in is constant, V out is close to the voltage reference at 14 V. Moreover, the experiment testing of input voltage increased from 5 V to 100 V and decreased from 100V to 5 V, and the output response is close to the voltage reference if the input voltage is constant.
Next, in the experiment where the reference output voltage value is set to 14 V, the input supply voltage (V in ) inconstantly varies from 5 to 100 V , with no load present; closed-loop control by PI controller tuned by metaheuristic optimization using artificial fish-swarm algorithm (AFSA) is shown in the following figures. Figure 18 shows the results of the intelligent voltage controlling system via a buck-boost converter by a PI controller tuned by metaheuristic optimization using artificial fish-swarm algorithm (AFSA) at reference voltage of 14 V with no load, supplying input voltage (V in ) of 5-100 V. In Figure 18(a), the input supply voltage (V in ) starts at 5 V and gradually increases to 100 V. It is observed that the output voltage V out remains constant at the reference voltage (V ref ), 14V, throughout. Moreover, another test is implemented on the intelligent voltage control system when the input supply voltage (V in ) is not constant. Tuning is performed to create continuity. V in is made equal to 5 V, rising to 100 V, and reduced back to 5 V. e intelligent electrical control system can effectively maintain the voltage V out at a constant value of 14 Vas shown in Figure 18(b).
According to Figures 17 and 18, showing the output voltage of the experimental result of proposed system, with both tuned gains of the PI controller by a pole placement method and PI controller by AFSA, the results show that the simulation corresponds with the experiment result. In experiment result in a real plant, the comparison results revealed that the response of the output voltage with tuned gains by AFSA of PI controller is more approachable to the reference voltage at 14 V. Figure 19 shows the experimental results of the intelligent voltage control system when on load. An experiment on the intelligent voltage control system via a buck-boost converter using a PI controller tuned by AFSA at a reference voltage of 14 V when on load is done. e test is performed with a supply load to the control circuit of 1 A, a farming load system of 10 A, a battery charger system of 12 Vat a current of 5 A, and a gridconnected system of 5 A, under the following conditions: in case that V in ≥ 5 V, supply control circuit on buck-boost converter; if V in < 5 V, supply control circuit on battery; when V in ≥ 30 V, farming system on buck-boost converter; in case that V in < 30 V, farming system on battery; if V in > 40 V, battery charger on buck-boost converter; and when V in > 60 V, grid-connected system on buck-boost converter. Journal of Engineering e experimental result is shown in Figure 19(a). When the supplied input voltage V in stair changes at 5 Vand increases to 100V, it is found that the intelligent electrical control system is able to maintain the reference voltage at 14 Vin any condition. I Battery is activated to the charging current of 5 A when V in > 45 V, and I Grid rises to 2 A if V in > 60 V. When the circuit is at full load, the total current (I Total ) measures approximately 18A. On the other hand, I Control Circuit and I Farming Load remain constant as per the condition set. Figure 19(b) shows experimental results of the intelligent voltage control system where the input voltage V in is not constant. Continuity is acquired by adjusting V in to 5 V increasing to 100 V and decreasing to 5 V. e intelligent voltage control system can efficiently maintain the output voltage V out at a constant value at the reference voltage of 14 V as shown in Figure 17(b).

Conclusion
is research aims to design a buck-boost converter for controlling the output voltage at 14 V and to develop a PI controller tuned by AFSA, metaheuristic optimization, which is able to use intelligent voltage system of 18 A load.
e result of the experiment shows that the buck-boost converter can withstand the input voltage at 5-100 V using switching frequency f of 4 kHz with inductance L of 33.6 mH, capacitance C of 110 uf, load R of 0.78 Ω, and duty cycle of 78%. e PI controller tuned by AFSA, metaheuristic optimization, has provided the optimal gains at K p � 1.37 and T i � 0.812. It is an effectively intelligent system for controlling system at voltage reference of 14 V when provided with input voltage in the range 5-100 V and can supply the load of 18 A.

Data Availability
No data were used to support this study.

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.