### Task:

System Identification

In this task, you are required to understand the dynamic behaviour of the DC MG system and identify mathematical model for it through analysing the actual recoded data and the equivalent Simulink model of the

device as shown in Figure 1. These data are recorded via an Oscilloscope (short-period state-state response

at a high resolution) and an Arduino (long-period dynamic and state-state response).

In this task, you are required to understand the dynamic behaviour of the DC MG system and identify mathematical model for it through analysing the actual recoded data and the equivalent Simulink model of the

device as shown in Figure 1. These data are recorded via an Oscilloscope (short-period state-state response

at a high resolution) and an Arduino (long-period dynamic and state-state response).

Four different sets of measured data are available for you to analyse the actual behaviour of the system. The

DC MG was hooked up to a motor driver (TB67H420FTG H-Bridge). Oscilloscope data were recorded for a

period of 50ms at a high resolution during steady-state response (50,000 samples) for duty cycles of 20% to

80%. This provides a detailed behaviour of the system including high frequency noise, low frequency vibration,

input current ripple, and issues with Arduino PWM generator. In reality, we would use a microcontroller to

implement a control system for real time control, so the measurements have to be recorded and processed

by, for example, an Arduino. Therefore, I have recorded the output voltage across a 10Ω high-power load

resistor (which you can use to estimate the output current) as well as reading the PWM signal back into

Arduino (which is useless due to a massive aliasing).

DC MG was hooked up to a motor driver (TB67H420FTG H-Bridge). Oscilloscope data were recorded for a

period of 50ms at a high resolution during steady-state response (50,000 samples) for duty cycles of 20% to

80%. This provides a detailed behaviour of the system including high frequency noise, low frequency vibration,

input current ripple, and issues with Arduino PWM generator. In reality, we would use a microcontroller to

implement a control system for real time control, so the measurements have to be recorded and processed

by, for example, an Arduino. Therefore, I have recorded the output voltage across a 10Ω high-power load

resistor (which you can use to estimate the output current) as well as reading the PWM signal back into

Arduino (which is useless due to a massive aliasing).

All the tests were carried out with 4 different PWM frequencies, 490Hz, 976.5Hz, 1.96kHz and 7.81kHz (some

other frequencies are also available, but I did not use them like 3.92kHz, 31.37kHz and 62.5kHz). The Arduino

real time operation was compiled in Simulink with a baud rate of 57600. As a result of many overhead codes

used by Simulink, a low-resolution sampling frequency of 100Hz had to be chosen to have a consistent

sampling rate in the recorded data via Arduino. This gives you a practical insight into the impact of heavy

aliasing in the measurements and the impact of quantization and down-sampling noise introduced into the

data, which you need to consider when using these data for dynamic modelling.

other frequencies are also available, but I did not use them like 3.92kHz, 31.37kHz and 62.5kHz). The Arduino

real time operation was compiled in Simulink with a baud rate of 57600. As a result of many overhead codes

used by Simulink, a low-resolution sampling frequency of 100Hz had to be chosen to have a consistent

sampling rate in the recorded data via Arduino. This gives you a practical insight into the impact of heavy

aliasing in the measurements and the impact of quantization and down-sampling noise introduced into the

data, which you need to consider when using these data for dynamic modelling.

Another practical aspect of this task is to investigate how the PWM control affects the operation of the DC

motor. It is known that a DC motor can be more efficiently controlled through changing the duty cycle of the

PWM (compared to using a linear voltage regulator) because the DC motor responds to the average value of

the PWM signal (think why that is the case!). However, this introduces new issues for the motor such creating

input current ripple and triggering some nonlinearity (think about what the impacts of the large current ripple

are or what happens to the H-Bridge switching MOSFETs if the frequency of PWM is too high). There is a

technique called motor current smoothing where a motor choke is added in series with the positive terminal

of the motor to reduce the ripples. But you have to consider a trade-off between the size of the current

smoothing inductance and the PWM frequency. You need to think about and make a choice on their values

for this task (there are other techniques like LC filtering which you might want to look into as well).

motor. It is known that a DC motor can be more efficiently controlled through changing the duty cycle of the

PWM (compared to using a linear voltage regulator) because the DC motor responds to the average value of

the PWM signal (think why that is the case!). However, this introduces new issues for the motor such creating

input current ripple and triggering some nonlinearity (think about what the impacts of the large current ripple

are or what happens to the H-Bridge switching MOSFETs if the frequency of PWM is too high). There is a

technique called motor current smoothing where a motor choke is added in series with the positive terminal

of the motor to reduce the ripples. But you have to consider a trade-off between the size of the current

smoothing inductance and the PWM frequency. You need to think about and make a choice on their values

for this task (there are other techniques like LC filtering which you might want to look into as well).

The Simulink model of the system is shown in Figure 2. You will be provided with nominal estimated

parameters of an actual DC MG to derive two transfer functions, one with the load resistance voltage (

parameters of an actual DC MG to derive two transfer functions, one with the load resistance voltage (

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