Provide a PDF file giving your responses (plots and responses when necessary) to the following prompts. Include all of your MATLAB “work” (aka-command window printout) as an appendix for 1-2. In your responses, provide 2-3 sentences discussing each prompt. If something does not make sense, why does it not make sense? What does this tell us?

10 25𝑠𝑠+1

1. calculate the inverse LaPlace Transform
2. plot the response to a unit step input and identify the behavior
3. plot the Pole-Zero Map and discuss the system’s stability & behavior
4. plot the Bode Plot and determine the gain margin and phase margin
5. plot the Nyquist Plot and discuss the system’s stability & behavior7𝑒𝑒−4𝑠𝑠

9𝑠𝑠+5

1. calculate the inverse LaPlace Transform
2. plot the response to a unit step input and identify the behavior
3. plot the Pole-Zero Map and discuss the system’s stability & behavior
4. plot the Bode Plot and determine the gain margin and phase margin
5. plot the Nyquist Plot and discuss the system’s stability & behavior

3. In MATLAB be sure that SIMULINK TOOLBOX, the CONTROL SYSTEM TOOLBOX, and SIMULINK DESIGN OPTIMIZATION APP are installed. Run through the following tutorial and respond to h.

1. Open the heatex_demo model using the command: open_system(‘heatex_demo’) in the command window and run the simulation. The simulation produces an unoptimized temperature variation of the heat exchanger and the initial data for optimization.
2. Double-click the Scope block to view the unoptimized temperature response, the disturbance signal and the control signal.
3. Double-click the Heat Exchanger Model block to view the model details.
4. Double-click the Max Temperature Variation block to view constraints on thetemperature variation of the heat exchanger. This constraint is used to tune the controllerparameters.
5. You can launch Response Optimization Tool using the Analysis menu in Simulink, or thesdotool command in MATLAB. You can launch a pre-configured optimization task in Response Optimization Tool by first opening the model and by double-clicking on the orange block at the bottom of the model. From the Response Optimization Tool, press the Plot Model Response button to simulate the model and show how well the initial design satisfies the design requirements.
6. The solid line represents the current response with the mean Disturbance Delay as specified in the constraint block. The dashed lines represent the response with the maximum and minimum Disturbance Delay. We start the optimization by pressing the Optimize button from the Response Optimization Tool. The plots are updated to indicate that the design requirements are now satisfied.
7. The solid curve shows the final optimized temperature variation of the heat exchanger.
8. Comment on the

1. Input the process transfer function (𝐺𝐺(𝑠𝑠) =

) in the MATLAB command window, and:

2. Input the process transfer function (𝐺𝐺(𝑠𝑠) =

) in the MATLAB command window, and:

i. ii. iii. iv. v. vi. vii. viii.

Process model
Process gain
Process time constant
Disturbance model
Disturbance gain
Disturbance time constant
Type of controller modes used
Types of control loops in the control block diagram (What are these for? How do they help control the temperature?)

4. In MATLAB be sure that SIMULINK TOOLBOX, the CONTROL SYSTEM TOOLBOX, and SIMULINK DESIGN OPTIMIZATION APP are installed. Run through the following tutorial and respond to h.

1. Open the distillation_demo model using the command: open_system(‘distillation_demo’) in the command window and run the simulation. The simulation produces the unoptimized composition of methanol in the column and the initial data for optimization.
2. Double-click the Scope block to view the unoptimized methanol composition in the top and bottom of the column.
3. Double-click the Linearized Model of Distillation Column block. Note that this is a subsystem and shows the model for variation of methanol in the top and bottom of the distillation column.
4. Double-click the Desired Step Response block to view constraints on the step response of the distillation column. These constraints are used to simultaneously tune both of the single-loop controller parameters.
5. You can launch Response Optimization Tool using the Analysis menu in Simulink, or the sdotool command in MATLAB. You can launch a pre-configured optimization task in Response Optimization Tool by first opening the model and by double-clicking on the orange block at the bottom of the model. From the Response Optimization Tool, press the Plot Model Response button to simulate the model and show how well the initial design satisfies the design requirements.
6. There are two curves in the plot representing the methanol composition in the top and bottom of the column. We start the optimization by pressing the Optimize button from the Response Optimization Tool. The plots are updated to indicate that the design requirements are now satisfied.
7. The two solid curves show the final optimized methanol composition in the top and bottom of the distillation column.
8. Comment on the
   i. Process modelii. Process gain
   iii. Process time constant
   iv. Type of controller modes usedv. Types of control loops in the control block diagram (What are these for? How do they help control the temperature?)

Learning SIMULINK requires a lot of effort. These models are a great starting point to begin changing model and control parameters (gains, time constants, setpoints, TFs, etc.) and seeing the effects. You could take a semester-long course on just SIMULINK!

3 days ago