Build the Single stage RC circuit shown in Fig. 3, with .
Fig. 3: (left) schematic of the single stage RC circuit, (right) implementation on breadboard
Analysis
The claimed transfer function of this circuit is
Where is the imaginary unit.
- What is the magnitude of the transfer function?
- What is the phase response of the circuit?
- What class (low-pass, high-pass, band-pass, band-stop) of filter is this?
- What is the -3dB frequency?
Measurement
Using the Red Pitaya’s Bode Analyzer tool, measure the frequency response (|T(f)|) as described in section 3.1.2.
- Show the plot of the measurement below:
Comparison
Respond to the following questions:
- Does the shape of the frequency response match your expectation from the analysis? Is there any point that stands out as odd?
- Find the -3dB point in the circuit, and compare this value to the one you previously calculated.
Single stage RC circuit – Unknown parameter estimation
Build the Single stage RC circuit shown in Fig. 4, with the potentiometer and . Use another resistor to provide electrical contact. Ensure that the potentiometer pins used are the two furthest pins, as this will be the total resistance of the device.
Fig. 4: (left) schematic of the single stage RC circuit, (right) implementation on breadboard
Analysis
The claimed transfer function of this circuit is the same as in 3.1 (reprinted here for courtesy)
Where is the imaginary unit. However now the value of R is unknown. Since we already know the expected behavior of the system, we can estimate the value of R by measuring the transfer function again.
- Derive the expression for the -3dB frequency as a function of R.
Measurement
Using the Red Pitaya’s Bode Analyzer tool, measure the frequency response (|T(f)|) as described in section 3.1.2. Pay special attention to include the cutoff frequency in the sweep.
- Show the plot of the measurement below:
Comparison
Respond to the following questions:
- Use the expression you derived to calculate the value of R from the measured value of .
To calculate the value of from the measured value of , we can rearrange the transfer function equation as follows: . Solving for , we get . Substitute the measured value of and the known value of to obtain the calculated value of .
- The previous analysis all presumed we knew the value of perfectly. In reality, the values of there are only approximately known.
- If the capacitance value can vary ±20%, what is the bounds on the error of the calculated value of ?
- (Optional) In the same line of thought, assume that the values of and are described statistically by gaussian distributions with mean and variances provided below:
If the capacitance value varies by %, the bounds on the error of the calculated value of can be determined by considering the sensitivity of with respect to . Using the formula for sensitivity, the upper bound of the error in can be calculated as , where is the derivative of with respect to . Similarly, the lower bound can be obtained by considering the negative change (-20%) in capacitance.
b. If the frequency f value can vary ±0.1%, what is the bounds on the error of the calculated value of R?
If the frequency value varies by 0.1%, the bounds on the error of the calculated value of can be determined by considering the sensitivity of with respect to . Using the formula for sensitivity, the upper bound of the error in can be calculated as , where is the derivative of with respect to . Similarly, the lower bound can be obtained by considering the negative change (-0.1%) in frequency.
c. If the both as above simultaneously, what is the total bounding on the error of the calculated value of ? (Hint: This should be a rectangular area)
If both and vary simultaneously, the total bounding on the error of the calculated value of can be obtained by combining the individual bounds calculated in parts (a) and (b). The total bounding will form a rectangular area defined by the maximum and minimum values of the error in due to the variations in and .
If and are described by Gaussian distributions with means and variances, the resulting probability distribution of can be calculated by applying the rules of propagation of uncertainty. By considering the distributions of and as well as their respective derivatives with respect to , the probability distribution of can be obtained. This distribution will provide information about the likelihood of different values of based on the statistical characteristics of and .