**ECE-520 Control Systems I: Discrete-Time Control Systems **

Since the predominant goal of this grant is to have students understand and appreciate the distinction between a model of a system and the real system, most of the Matlab routines used in the labs plot both the predicted response (based on the model) and the measured response (from the ECP system). A model of the system is necessary for the initial design of a controller, but the predicted response of the system may not match the true system response due to the simplified models being used.

We utilized ECP's Simulink drivers as our plants, and all of the labs were done in Simulink. The Simulink drivers were configured so that the ECP 210 systems used units of cm for both input and output, while the ECP 205 system used units of radians for both input an output

**Lab 1: ***In this laboratory, the students developed
models for two one degree of freedom systems: a rectilinear system (ECP 210) and
a torisonal system (ECP 205). The sampling interval was fixed at 0.05 seconds
for all systems.*

*
Lab 1,
log_dec.m,
log_dec.fig,
process_data_1dof.m,
model_1dof.m *

**Lab 2: ***In this laboratory, the students the students
developed models for two two degree of freedom systems: a rectilinear system (ECP
210) and a torsional system (ECP 205). The sampling inverval was fixed at 0.05
seconds for all systems.*

*
Lab 2,
process_data_2dof.m,
model_2dof.m*

**Lab 3: ***In this laboratory, the students utilized
Matlab's System Identification Toolbox to identify their two one degree of
freedom systems.The sampling inverval was fixed at 0.05 seconds for all systems.*

**Lab 4: ***In this laboratory, the
students utilized Matlab's System Identification Toolbox to identify their two
two degree of freedom systems.The sampling inverval was fixed at 0.05 seconds
for all systems.*

**Lab 5:*** In this laboratory, the students utilized
state variable feedback with a constant prefilter to control their four systems.
The different models, created either by sampling a continuous time model or
created directly by using the System Identification Toolbox were compared. The
sampling inverval was fixed at 0.05 seconds for all systems.*

**Lab 6: *** In this laboratory, the students utilized
state variable feedback with integral control to control their four systems. The
different models, created either by sampling a continuous time model or created
directly by using the System Identification Toolbox were compared. The sampling
inverval was fixed at 0.05 seconds for all systems.*

**Lab 7: *** In this laboratory, the students utilized
full order observers with state variable feedback with integral control to
control their four systems. Only the those models which most accurately
predicted the systems response were utilized. The sampling inverval was fixed at
0.05 seconds for all systems.*

**Lab 8: *** In this laboratory, the students utilized
full order observers with state variable feedback with integral control to
control their four systems. The continuous time models were discretized using
three different sampling interval to examine the effects of different sampling
rates.*

**Lab 9: *** In this laboratory, the students utilized
minimum order observers with state variable feedback with integral control to
control their four systems. The continuous time models were discretized using
three different sampling interval to examine the effects of different sampling
rates.*