International Journal of Modern Research in Engineering & Management (IJMREM) ||Volume|| 2 ||Issue|| 3 ||Pages|| 01-12 || March 2019 || ISSN: 2581-4540
Neural Network Control of Switch Mode Dc Dc Converter 1, 1,
Anees U Rahman , 2, Dr. Mukhtiar Ahmed Mahar , 3,Dr Abdul Sattar Larik
Student of Masters of Engineering at Mehran University of Engineering and Technology Jamshoro Pakistan 2, Professor Department of Electrical Engineering, Mehran University of Engineering and Technology Jamshoro Pakistan 3, Professor Department of Electrical Engineering, Mehran University of Engineering and Technology Jamshoro Pakistan
----------------------------------------------------ABSTRACT----------------------------------------------------Power electronic converters are periodical variable structure system owing to their switching operations, being a simple in structure, lot of researchers are working on power electronic converters. As they have been found practically more efficient and cheapest. That’s why they are being used vastly. The Switch Mode DC DC Converters being a highly underdamped produces oscillations and nonlinearities in the output voltage. Lot of controlling techniques have been used to mitigate the oscillations but those controllers/ controlling techniques have certain drawbacks. In this research paper PI controller is being compared with Artificial Neural network controller based on Switch Mode DC DC converter at higher switching frequency under Steady state as well as dynamic state (line and load variations). Although Artificial Neural network controller is being used in problems involving nonlinearities and uncertainties but here it is used/applied to Step down chopper converter to control voltage under steady state as well as dynamic state. Here Neural Network is trained by using Levenberg Marquardt (trainlm) Algorithm. It is executed and operated in Matlab/Simulink, and Simulation results are shown here.
KEYWORDS: switch mode dc dc converter, PI Controller, Neural Network Controller, Steady state analysis and dynamic state analysis ------------------------------------------------------------------------------------------------------------------------------------------Date of Submission: Date, 18 February 2019 Date of Accepted: 24. February 2019 -------------------------------------------------------------------------------------------------------------------------------------------
I. INTRODUCTION The modern power electronic converters which have become more effective and efficient because of latest improvements in power electronic converters, as they are being used for the high Voltage (HV) and high-Power applications. Nowadays numbers of converter topologies are being manufactured by manufacturers and researchers. They are still researching in the further development of Switch Mode DC DC Converter topologies. Owing to their switching operation [1-4], these Power Electronic/ Semiconductor devices are totally non-linear alterable/ variable in structure. The Switched Mode DC DC Converters alter one magnitude of electrical voltage into another magnitude. These are simplest power electronic circuits operated in switching action. Due to simplest topology, these converters have been successfully adopted in various areas. Due to their vast applications like constant current source for LEDs, Personal Computers (PC) Power supplier, Institute appliances, Power Electrical based equipment control and in communication technology etc. Main factors which are widely accepted of switching converters are control and stabilization of system. Different techniques and methods [6-8-17] have been used of switched mode converter, but for the industrial demand and other purposes the simplest and economical controller’s configuration is required for better efficiency. Switch mode converters are highly under damped system. According to the various authors [10-11-12-16-17] oscillations have been produced in the output of converters owing to nonlinearities [9-12]. Also, they are the sources of harmonics. In addition of higher switching frequencies and with lower conversion factor converter efficiency decreases [1416]. And a high frequency noise is also generated by the switching of these converters [3-4]. Methodology : In this research paper PI based simulation model of Buck Converter is developed, analyzed and compared to Neural Network Controller when applied on it. The main aim is to overcome the oscillations of step-down chopper circuit.
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Neural Network Control of Switch Mode‌ II. STATE SPACE REPRESENTATION OF STEP-DOWN CHOPPER CIRCUIT. Step Down DC DC Buck Converter is shown in figure.1, consists of single-phase voltage source (Vin) through the rectifier diode and uses a control switch to power flow from input to output, it contains two energy storing elements like inductor L and capacitor C to transfer energy from input to output and a load resistor RL [1-2]. There are two basic operating principles of Step-down DC DC Switching Converter. Continues Conduction Mode (CCM) and Dis-continues Conduction Mode (DCCM). During Continues Conduction Mode (CCM) converters continuously conducts and inductor current is not zero at any time, while in Discontinues Conduction Mode (DCCM) inductor current becomes zero [1-6], in his paper continues conduction operating mode is considered.
Figure 1: DC –DC buck Converter When source is connected current starts flowing due to forward biasing switch is on, but diode becomes in reverse biasing and does not conducts as shown in fig.1.1(a). During reverse biasing switch becomes off and free wheel diode D passes the energy stored by the storing element as shown in fig.1.1 (b) [2-3].
Fig.1.1 (a), Turn on switch
Figure 1.1(b). Turn off switch
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Neural Network Control of Switch Mode… On the basis of these model state space model is developed for the state variable.
Equation.1 describe the space average modeling of Switched mode Buck Converter. From the equation (1) state variable for inductor current and its 1st derivate is given as
.
x1 =
D 1 Vin − x2 L L
(2)
D is the duty ratio.
d iL dt
=
( D Vin ) − VO L
(3)
Similarly the state variable for the voltage across capacitor can be expressed from equation (1) as
dVO i − iO = L dt C
(4)
Io is the current through load.
III. MODELING OF STEP-DOWN DC DC CONVERTER BY USING MATLAB SIMULATION WITH PI CONTROLLER Matlab Simulation model for the subject Converter is represented in the fig.2. Simply by looking in this Model, it is found that it contains major three portions that are supposed to be fixed by every user. These are input voltage denoted by (Vin), the reference voltage known as the output voltage denoted by (Vo) and the resistance as load denoted by (Ro) [2-4]. Furthermore it is added that this simulation model contains another two sub portion/ system, these are Pulse Width Modulation (PWM) generator and step down Buck converter. PWM Waveform Generator Subsystem: This subsystem generates the pulses in shape of wave but at different Duty Cycles and the input to this sub system is the Duty Cycle of the controller. Simply talking about the circuit that it has two operating principles/ conditions. First one is the Duty interval when the switch is ON is known as (Ton) and second is freewheeling diode mode, when switch is off known as the (Toff). These both conditions are simulated in the same model. However it takes two inputs the first one which is in decimal form is the Duty Cycle (D) From the existing controller and second one is the switching Frequency represented by the (Fs) as an input parameters as shown in fig.2.1 [2].
Fig.2.MainSimulinkmodel.
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Neural Network Control of Switch Mode‌
Fig. 2.1 PWM Generator
Fig.2.2: -PWMwaveforms The output wave forms of the pulse width modulation subsystem is shown in fig. having constant 0.6 duty cycle. A Repeating Sequence block is placed to generate output saw tooth wave forms as shown the waveform 1 in figure 2.2, the time values are for switching purpose are (0 and 1/Fs) and output values are (0 and 1). The decimal value/ magnitude of this waveform is subtracted from the duty cycle to produce the mirrored version of saw tooth waveforms and finally amplitude will be from D to D-1, and this is shown in second waveform of figure 2.2, now suppose we want positive amplitude then the required PWM signal is high in magnitude, and for the negative amplitude PWM pulse is low in magnitude, and it is achieved simply by using switching action ON and Off. Output is one when switch is ON and output is zero when switch is off by using Relay Block to generate the PWM pulses during the zero crossing. Finally, the last waveform in figure 2.2 is resulting PWM signal of the required duty cycle [2][4]. Switch Mode DC DC Step Down Chopper sub-system: This sub-system contains all the configuration ad parameters which generates the out-put voltage and load current. It requires the input signal as well like the DC unregulated input Voltage (Vin), Duty Cycle (D) and the PWM generated signal. Furthermore it contains other elements declared as the mask parameters of the sub-system just like Inductor inductance (L), effective inductive series resistance (RL), capacitor capacitance (C), effective capacitive series resistance (Rc). All these mask
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Neural Network Control of Switch Mode… parameters are configured in such a way that the subsystem generates the required out puts as displayed in the fig. 2.3. This sub-system essentially consist of the two loops. And are represented in the differential equation forms and provide the inductor current and the reference output voltage. As it is already discussed the principle operations of switch mode converter, that it has two modes of operations like a. Time period during the switch is ON and it passes the input voltage towards the load. b. Time period during the switch is open means off, the freewheeling diode passes the stored energy by the inductor to the load and the input voltage is opened from the circuit, this period is called t off period [1-4]. In figure 2.3, the very first close loop is computing and is the responsible for the inductor current, and the second one close loop is computing and generating the out-put voltage. This second loop is the combination of the voltage across the capacitor and voltage drop across the effective capacitive series resistance (Rc). We are already familiar that the out-put voltage and the inductor current is the out-put of this sub-system as displayed in fig.2. The load current is the ratio of the output voltage to the load resistance, and it is returned back as the feedback to the subject sub-system [2]. The performance parameters are given in Table.1. Parameter name
Symbol
value
Parameter name
Symbol
value
Input voltage
VDC
24V
Switching frequency
Fs
50KHz
Output voltage
Vo
18V
Duty cycle
D
0.6
Inductor
L
3mH
Effective series resistance Of Inductor
RL
0.08Ω
Capacitor
C
0.95µF Effective series resistance Of Capacitor
Rc
0.03Ω
Load resistance
R
100Ω
Fig.2.3 buck converter subsystem
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Neural Network Control of Switch Mode‌ IV. EXISTING CONTROLLER (PI). Matlab Simulink model of PI Controller is here used to control the oscillations of the out-put voltage during steady state and dynamic state analysis of the step-down chopper circuit. The main function of this controller is to generate the required Duty Cycle (D) which is in Decimal form based on error signal [1-2]. This Controller parameters or values can be placed either manually or by Using Matlab tuning tools for some extent. For further analysis it provides bode plot. As we know that PI is a linear controller cannot handle the nonlinearities, so by changing input voltage called line variation, and load variation (Resistance) we found the oscillations in the output voltages [2-4-6] as sown in figure 6 &7. The main aim/ objective of this research work/ paper is to build up/ design the best controller for the step-down chopper circuit in order to enhance performance of this converter by controlling uncertainties and nonlinearities. It is deemed that Neural Network Controller can easily overcome the shortfalls of Switch Mode DC DC Buck Converter.
V. NEURAL NETWORK CONTROLLER. Various other controllers or control techniques just like cascaded controller method, Fuzzy Logic Controller, PID Controller, Adaptive Controller, Sliding Mode Controller etc [6-8-9-10-11] were used in order to keep the output voltage (terminal voltage) constant under line and load variations but control strategy design is fraught with some problems and is not giving desired and suitable results. However, this requires for the desirable results under the line and load variation, there is extremely need of control development. In contrast to above controllers, modern and latest Artificial Neural Network has been introduced due to its quick response, fast behavior and robustness. The Neural Network controller is a basically a nonlinear control technique and widely increases system efficiency with more effectiveness [7]. It also increases system performance and lowers the system complications. System parameters like variations, uncertainties and nonlinearities are adjusted by Neural Network due to its self-adjusting capabilities. Owing to its simplest parallel structure it enables to operate easily parallel and distributed processes [11-17]. As the Neural Network is much similar to the brain of human in two ways, at first through learning algorithms it acquires knowledge, secondly data is being stored within synaptic weights. Its construction is simply consists of the junctions that are connected with the links called processing elements and weights that are used for the storing information purpose in the Neural Network. These weights are modified so that the network becomes in hormone with plant that provides the input data. The processing elements Neuron in the Multilayer Feed Forward are connected/ adjusted in the layer forms. The input data is received by the Neuron from the previous layers and then it shifts/ feeds to the coming/ preceding layers. That’s why this structure/ category of Neural Network is known as the Multi-layer Fees Forward Neural Network and contains three layers called Input, Hidden and Out-put Layers [7] as shown in fig.4.
Fig.4.Multi-layer-Feed-forward-Nueral-Network.
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Neural Network Control of Switch Mode‌ The Feed Forward Neural Network has lot of usages in many branches of the control technology. It has been used in system control strategies, function approximation, time and series prediction and in power electronics as a controlling technique. One of the most beneficial advantage of this network is of the fast learning speed and removes complexities in system control field. And recently it has been widely used for the tuning of PI, PID controller parameters in the control strategy [13-14]. As this Network learns from the historical ways/ patterns and are used for the associative memories that’s why it is recommended as a best to use in control system. Due to this it gives good generalization results. Even this can be effectively applied when the data or information is not complete, due to information/ data is divided/ distributed over the weights of Neural Network. It is also easy to train the Neural Network because lot of simple learning algorithms can be used to any type of hurdle in the system [7-11-12-15-16]. Learning Algorithm : In this research paper I have used Levenberg Marquardt learning algorithm. As this algorithm appears to be fastest method for the training of feed forward networks up to 700 weights and efficient implementation in Matlab Simulink. It is also known as the damped least squares method, it always takes the form of sum of squared errors and works with the Jacobin matrix and gradient vector. Levenberg Marquardt learning algorithm uses the idea of back propagation for the calculation of the Jacobin matrix. To develop the neural network controller, a data is required about the Switch Mode DC DC Step Down Converter. Basically the no of input and output neurons at each layer are equal to the no of input and output signals of the system respectively. The parameters of PI controller are being modified based on Feed Forward Neural Network. This network has ability to tune the PI parameters like Kp and Ki in order to optimize the error and finally the system comes in to steady state [13-14]. In this paper the Simulink is carried out in Matlab. The training process optimizes the error of the network by the optimization method. A sufficient training data input output mapping data of plant is required while training/ leaning mode of the neural network controller [15-16]. Since the parameters of the switch mode dc dc converter are given in table 1, and Neural Network Controller being a nonlinear controller controls the oscillations of the existing controller. And structure of developed Neural Network controller in Matlab simulation is shown in figure 5, consists of two inputs, one hidden layer consists of 5 neurons and one output.
Fig.5. Neural network simulation model for switch mode dc dc converter.
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Neural Network Control of Switch Mode‌
Fig.5.1
Fig.5.2 Fig.5.1 represents Regression plots to check linearity between the input and output, whereas Dashes represents perfect result, and perfect results-output=target [14]. Here R indicates the relationship between outputs and targets, it almost approaches one (1), it means there is exact linear relationship between outputs and targets, and result is perfect. Whereas figure 5.2 show the best training results and error reduced to its optimization value.
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Neural Network Control of Switch Mode‌ Performance Parameters of Neural Network controller Table 2. Parameters of neural Network controller Number of input
2
Number of output
1
Number of hidden layer
1
Number of hidden neuron
5
Number of Epochs
1000
VI. SIMULATION RESULTS To find out the effectiveness of the proposed controller, a Matlab based simulation model of Switch Mode Buck Converter has been developed/ designed with the existing PI and Neural Network controller as shown in figures given above. In this paper the conventional PI controller is being compared with the Neural Network Controller and Simulation results are displayed and shown with brief explanation. The steady state analyses : During steady state analyses the output voltage rises to 18 volts after 2 milliseconds in case of PI Controller and ripple is also present in the output wave as shown in figure 6 (a), on the other hand after training of NNC the simulation results of Switch Mode DC DC Converter rises to 18 volts after 1milli seconds. The waveform obtained for this model are identical to a buck converter as displayed in fig.6 (b).
Fig.6 Dynamic analysis: Under this section I have compared the simulation results under the line (Input) variations and load variation. In order to know the performance of controllers the dynamic characteristics are significant. a) Line Variations : In this technique the input voltages of source is varied from the 24 volts to 28 volts, just to check the impact on out-put voltage and the overshoot in the out-put of the Simulink when PI and NNC is used respectively. When the simulation model of the step-down Switch Mode Converter is executed in the presence of PI Controller then there is 3.66 peak to peak overshoot in output voltage and it takes one millisecond for the settling time as shown in figure 7, similarly when NNC is connected with model then the simulation results are
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Neural Network Control of Switch Mode‌ shown in figure 7. It is clearly shown that there is not any overshoot in the output voltage wave form when model is controlled through NNC and found efficient one.
Fig.7. a) Load variations : Now in this case the load resistance of the model is increased or decreased then we will be able to analyze and compare the out-put simulation results of the converter. a) In the first case the load resistance is varied from the 100 ohms to 150 ohms. The output Voltage of the switch mode dc dc converter is varied when PI controller is used, the peak to peak overshoot in the voltage is 4.8 volts and it takes 1.8 milliseconds to return back to 18 volts due to increase of load, as shown in figure 8. On the other hand, when NNC is used with the switch mode dc dc converter and load is increased then the peak to peak overshoot in voltage is just 2.2volts and it takes 0.6 milliseconds to return back to 18volts as shown in figure 8. b) In the second case the load resistance is decreased from 100 ohms to 80 ohms, the output Voltage of the switch mode dc dc converter is varied, when PI controller is used the peak to peak overshoot in the voltage is 2.6 volts and it takes 0.9 milliseconds to return back to 18 volts due to load decreased as shown in figure 8.1. On the other hand when NNC is used with the switch mode dc dc converter and load is decreased then the peak to peak overshoot in voltage is 0.6 volts and it takes 0.6 milliseconds to return back to 18volts as shown in figure 8.1.
Fig. 8. When the load is varied from 100ohm to 150 ohm.
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Figure 8.1, when the load is varied from 100 ohm to 80 ohm.
V. CONCLUSION In this paper the NNC is executed/ implemented in Matlab simulation to control the out-put Voltage of Step Down Chopper Circuit. The subject converter is simulated under steady state and dynamic states under line and load variation in Mat lab. The NNC is trained through levenberg-Marquardt algorithm. It has been proved from the above simulation results that the NNC have the fast response in tracking the desired output voltage under the steady and dynamic states. The proposed Neural Network Controller produces the better performance than PI controller, it removes the overshoots and oscillations to get the desired out-put Voltage as shown in fig. 4 and 5. It is added furthermore that the settling time of NNC is faster than PI Controller as it returns back quickly to desired voltage.
ACKNOWLEDGMENT I would like to thank Almighty Allah who blessed me to complete this research work successfully. Secondly I would like to express sincere and heartfelt gratitude towards prof: Dr. Mukhtiar Ahmed Mahar and Prof: Dr. A. Sattar larik Mehran University Of Engineering Technology Jamshoro, Pakistan and I am deeply indebted to them for valuable and remarkable supports during conducting and preparing this research paper.
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