Research Article  Open Access
P. Chanjira, S. Tunyasrirut, "Intelligent Control Using Metaheuristic Optimization for BuckBoost Converter", Journal of Engineering, vol. 2020, Article ID 5462871, 12 pages, 2020. https://doi.org/10.1155/2020/5462871
Intelligent Control Using Metaheuristic Optimization for BuckBoost Converter
Abstract
This research aims to introduce an intelligent controlling system of windmillgenerated voltage connected to a load of . As natural wind speed lacks consistency, the resultant irregular voltage can lead to system damage. In the experiment, a buckboost 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 using metaheuristic optimization, an artificial fishswarm algorithm (AFSA). In testing, the buckboost converter is controlled by the PI controller at a reference voltage of and supplied with an input voltage in the range of 5–100. The result shows that, even with inconstant , the system can effectively control the reference voltage at .
1. 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]. Therefore, 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). The 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 buckboost 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 buckboost converter to control the electric pressure through such circuits as a chopper and inverter. This 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 buckboost converter in upholding the voltage output at the DCDC link [6]. Both suggest the necessity of controlling the changing electric tension at the DCDC link onto an appropriate level for the gridconnected system.
In this study, a buckboost converter is thus chosen as a switching device to adjust the unstable electric pressure at the DCDC link to a reference voltage of the system [7], since the buckboost 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 DCDC 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 twostage 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 DCmotor model, but also a secondstage 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 buckboost DCDC converter. Furthermore, a robust controller is designed by polytopic representation, and a Lyapunov based switched stability analysis of the closedloop system is presented. The 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 fuzzylogic controller for buckboost converters with inconstant change of voltage [11]. The 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]. This is in accordance with Tehrani et al., who propose a theory and an adaptation of the multipurpose strength Pareto evolutionary algorithm (SPEA). This 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 gridconnected system. Yau et al. apply twostage system which implements maximum power point tracking and optimal charge control of Liion battery. They use PI to control charge controller parameters. In determining the optimum parameters, the intelligent algorithms, PSO, and genetic algorithms (GAs) are utilized. The optimal parameter results for the controller of PSO have better performance than GA [16].
The purpose of this research is to design a buckboost converter for controlling the reference electric pressure at and to develop a PI controller enhanced with metaheuristic optimization, artificial fishswarm algorithm (AFSA), as an intelligent voltage controlling system of an load for continual usage.
2. Materials and Methods
2.1. Theories
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. This movement of air mass causes the wind and its motion’s kinetic energy is harvestable. The wind energy has been harnessed more and more, especially in producing electrical power. In Thailand, when compared to other countries, such advantages of wind energy have been little brought into use [17].
Winds in Thailand 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.
2.1.2. BuckBoost Converter
The buckboost 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. The control frequency which yields the buckboost converter’s highest efficiency is reached when the time period is the lowest, at 100 times the switching rate, as shown in Figure 1 [18].
The operation of a buckboost converter can be divided into two modes, according to the condition of the power switch device:
(1) BuckBoost Converter Switch, Closed Circuit Mode. From Figure 2, the current flows from the electrical power source through the switch and the inductor cause the inductor to store power in the form of magnetic field [19], explained by the following equation:where is the voltage drops across the supply and is the voltage drops across the inductor.
Hence, is the rate of change of the current in closed switch, where and is the duty cycle at :such that
(2) BuckBoost 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 selfinduction, 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. The diode is forwardbiased, as in the closed circuit condition [12, 13], as described by the following equation:where is voltage drops across the supply and is voltage drops across the inductor. is an output of buckboot converter
Hence, is the rate of change of the current in open switch where , and is the duty cycle at .
Combining (3) and (6), a change rate of the current can be rewritten as
Rearranging (8), is denoted by
2.1.3. Designed Circuit of BuckBoost Converter
The equivalent circuit design of the buckboost 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 buckboost converter can receive an input voltage in the range 5–100 with standard value at compatible with a load of . The load resistance is calculated as according to Ohm’s law. All these parameters are revised, while the voltage input to the buckboost converter ranges from to as the input should be lower or higher than the reference pressure . The output response of the buckboost converter uses an inductance of , a capacitance of , and PWM signals for driving IGBT constant switching frequency at . The state vector for the buckboost converter is defined aswhere is the current through the inductor and 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 is the state vector, is the source vector, and are the state coefficient matrices. The state model of the buckboost converter is provided by the two modes as follows:
The closed circuit mode is defined as
The open circuit mode is written aswhere
The state space model is of the following form:where
Then are substituted as shown in
Hence, the transfer function of the buckboost converter is shown as
From Figure 5, the process gain is defined as
In substituting variables in (21), the equation yields . According to Figure 5, , is a process gain, and is a time constant.
2.1.4. PI Control of FirstOrder Systems
Suppose that the process can be described by the following firstorder model [20]:
In substitution, the transfer function of the controller for buckboost converter is rewritten as
Figure 6 illustrates the firstorder model buckboost converter design.
The two parameters are presented, the process gains and the time constant . By controlling this with the PI controller [21] provided as
a buckboost converter design with PI controller is achieved.
According to Figure 7, a secondorder closedloop system is obtained as
The two closedloop poles can be chosen arbitrarily for a suitable choice of the gain and the integral time of the controller. The poles are given by the characteristic equation [13]
Suppose that the desired closedloop poles are characterized by their relative damping and frequency; the desired characteristic equation then becomes
Substitution yields
The coefficients of these two characteristic equations for determining and are written aswhere ; note that in order to have positive controller gains, it is necessary that the chosen bandwidth be larger than . If is very large, the integration time is given byand is hereby calculated as
From the design, and then so that .
In defining ,
By substituting the parameters into (29) and (30), the calculation of and is shown as
Consequently, and . By substitution into (25),
Hence, a buckboost converter design with PI controller is defined as
The simulation of closedloop control using PI controller designed by pole placement method via input step command 0–14 V is shown in Figure 8.
2.1.5. Artificial FishSwarm Algorithm (AFSA)
AFSA, metaheuristic optimization, is a decisionmaking 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. The characteristics can be categorized by behavior for finding solutions as follows: [22].(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(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
The 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 and in AFSA for the intelligent tuning method for PI controller. From Table 3, the gains and are found optimal, using AFSA, at and with the minimum cost function of 0.1949. Thus, the aforementioned values are chosen. The simulation of closedloop 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 closedloop control, with the output voltage via input step command 0–14 V, using PI controller designed by pole placement method and tuning by AFSA.
3. Results and Discussion
3.1. Proposed System
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 . This is under the following conditions: in case that , supply control circuit on buckboost converter; if , supply control circuit on battery; when , farming system on buckboost converter; in case that , farming system on battery; if , battery charger on buckboost converter; and when , gridconnected system on buckboost converter.
Figure 13 portrays an electricity generating system powered by a wind turbine. The kinetic energy obtained from the wind flowing through the blades of windmill transforms into mechanical energy driving the shaft connected to the PMSG. The obtained alternating voltage is then rectified. Nonetheless, the voltage input to PMSG depends significantly on the fluctuating wind speed. A buckboost converter, therefore, is used as a control device to ensure a suitable and constant voltage via a PI controller. Furthermore, suitable values of gains and are achievable via metaheuristic optimization using AFSA, taking into consideration the output voltage from the buckboost converter and the reference voltage . is used in setting via an embedded system using Arduino Mega board.
Arduino Mega is a microcontroller board with ATmega2560 chip, which has 54 digital input/output pins. These include 15 pins used as PWM, 16 pins as analogy inputs, and 4 sets of the UART. The board’s crystal frequency is . Arduino Mega also enables direct data transfer with computers via the board’s USB port. Its design is compatible with various types of shield. This facilitates and fully supports program development on the Arduino platform. The load conditions are coded as shown in Figure 14.
3.2. Simulation and Experimental Results
In Figure 15, the experimental result is compared with the simulation result of the closedloop control, with the output voltage via input step command 0–14 V, using PI controller designed by pole placement method and tuning by AFSA.
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 buckboost converter using a PI controller adjusted by AFSA at objective output voltage of 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 , the input supply voltage inconstantly varies from 5 to 100 , with no load present; the results for closedloop 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 buckboost converter tuning gains of PI controller by pole placement method of closedloop system at reference voltage of with no load, supplying input voltage of 5–100 . In Figure 17(a), the input supply voltage starts from to . Even under step input of changing rapidly, the output response is overshooting with no steady state error. In Figure 17(b), when is constant, is close to the voltage reference at 14 V. Moreover, the experiment testing of input voltage increased from to and decreased from to , and the output response is close to the voltage reference if the input voltage is constant.
(a)
(b)
Next, in the experiment where the reference output voltage value is set to , the input supply voltage inconstantly varies from 5 to 100 _{,} with no load present; closedloop control by PI controller tuned by metaheuristic optimization using artificial fishswarm algorithm (AFSA) is shown in the following figures.
Figure 18 shows the results of the intelligent voltage controlling system via a buckboost converter by a PI controller tuned by metaheuristic optimization using artificial fishswarm algorithm (AFSA) at reference voltage of with no load, supplying input voltage of 5–100 . In Figure 18(a), the input supply voltage starts at and gradually increases to 100 It is observed that the output voltage remains constant at the reference voltage , , throughout. Moreover, another test is implemented on the intelligent voltage control system when the input supply voltage is not constant. Tuning is performed to create continuity. is made equal to 5 , rising to 100 , and reduced back to 5 . The intelligent electrical control system can effectively maintain the voltage at a constant value of as shown in Figure 18(b).
(a)
(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 .
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 buckboost converter using a PI controller tuned by AFSA at a reference voltage of when on load is done. The test is performed with a supply load to the control circuit of 1 A, a farming load system of , a battery charger system of at a current of , and a gridconnected system of , under the following conditions: in case that , supply control circuit on buckboost converter; if , supply control circuit on battery; when , farming system on buckboost converter; in case that , farming system on battery; if , battery charger on buckboost converter; and when , gridconnected system on buckboost converter.
(a)
(b)
The experimental result is shown in Figure 19(a). When the supplied input voltage stair changes at and increases to 100 it is found that the intelligent electrical control system is able to maintain the reference voltage at in any condition. I_{Battery} is activated to the charging current of when , and I_{Grid} rises to if . When the circuit is at full load, the total current (I_{Total}) measures approximately . 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 is not constant. Continuity is acquired by adjusting to increasing to and decreasing to . The intelligent voltage control system can efficiently maintain the output voltage at a constant value at the reference voltage of as shown in Figure 17(b).
4. Conclusion
This research aims to design a buckboost converter for controlling the output voltage at and to develop a PI controller tuned by AFSA, metaheuristic optimization, which is able to use intelligent voltage system of load. The result of the experiment shows that the buckboost converter can withstand the input voltage at 5–100 using switching frequency of with inductance of , capacitance of , load of , and duty cycle of 78%. The PI controller tuned by AFSA, metaheuristic optimization, has provided the optimal gains at and It is an effectively intelligent system for controlling system at voltage reference of when provided with input voltage in the range 5–100 and can supply the load of .
Data Availability
No data were used to support this study.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Acknowledgments
The authors would like to thank Dr. Wachirapond Permpoonsinsup, Assistant Professor, Department of Science and Technology, Pathumwan Institute of Technology, for spending her valuable time reading the manuscript and providing valuable suggestions. This study has been supported by the Faculty of Engineering, Pathumwan Institute of Technology, Bangkok, Thailand.
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Copyright
Copyright © 2020 P. Chanjira and S. Tunyasrirut. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.