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ELEC6228 Applied Control Systems
创建日期：2024-06-21 17:11:08
标签： ELEC6228
Applied Control Systems

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ELEC6228 Coursework Design Project P1

Applied Control Systems: Design Project
Please read through this document and complete the
coursework as required. The coursework submitted
(including the relevant program source files) should be all
your own work and that reference to, quotation from, and
discussion of other work has been correctly acknowledged.
Note: This coursework will count towards 50% of your
final mark for this module. Please spend no more than 50
hours on this assignment.
Applied Control Systems: Design Project
Part A – Lego Mindstorms NXT Robot
In Part A, you will work by yourself to use PID and model predictive control to balance a twowheel Lego robot (shown below) and verify the performance of your design in Simulation using
Matlab and Simulink. Please note you can use any system configuration (e.g. constructions, use
of different sensors/measurements) as long as it achieves the design objectives, but you may
want to weigh up achievability versus challenge in making your decision.
Preparation
Read about Lego Mindstorms NXT robot to have a good understanding of the platform. You
should also get yourself familiar with Matlab and Simulink. You should be able to find many
references on these topics – here are a few examples:
Task A.1 Mathematical Modelling of the Robot Dynamics
In order to use different control design methods to balance the robot, a mathematic model of the
robot dynamics is needed. Read the Appendix and make sure you understand how to establish a
mathematic model of the robot dynamics.
Task A.2 Modelling of the Robot Dynamics in Simulink
Build the model obtained in Task A.1 in Matlab Simulink. For different initial conditions of the
robot, simulate the system model and observe the system behaviours. Are they consistent with
your expectations?
Task A.3 A Simple PID Controller
Design a PID controller to balance your robot. Clearly describe your design procedure and the
measurements you use. Evaluate the performance of your PID controller.
ELEC6228 Coursework Design Project P1

Task A.4 Model Predictive Control
Use model predictive control to balance your robot. Clearly describe your design procedure and
the measurements you use. Evaluate the performance of your controller. How would you tune
your MPC controller in practice? Compare your MPC design with the PID design in Task A.3 –
what do you find? Can you explain why?
Task A.5 Discussion
Critically evaluate your work. Have you achieved the objectives? How would you improve your
design? What problems did you find during this work? What conclusions can you draw from
your design? From the experience of this work, what do you need to consider when solving a
practical problem?
Part B – Quanser QUBE-Servo with NI myRIO
In this part, you will work in group to apply the three control design methods, i.e. linear
quadratic control, model predictive control and iterative learning control to the Quanser QUBEServo system with NI myRIO. Note that the Quanser QUBE-Servo can be configured into either
an inertial disc (the design objective is for the output angle to follow a periodic square wave
(amplitude ��� and period � seconds) or an inverted pendulum (the design objective is to
balance the pendulum).
Please choose the configuration according to your own preference. Note you may want to weigh
up achievability versus challenge in making your decision.
Preparation
After attending the NI training session, you should now be familiar with myRIO, LabVIEW and
the Quanser QUBE. If not, please read more about the training session notes or/and talk to the
module lecturers.
Task B.1 Configuring the Quanser QUBE-Servo
Choose a configuration and connect your system appropriately. Check you configuration and
make sure the system works properly.
ELEC6228 Coursework Design Project P1

Task B.2 Modelling of the Robot Dynamics
In order to control the system, a mathematical model is needed. Establish a mathematical model
of the system. Clearly specify the input and output of the model, your methods in obtaining the
model and any assumptions you have made. Is the model obtained a good description of the
physical plant? Evidence this.
Task B.3 Linear Quadratic Optimal Control
Design a linear quadratic optimal controller to achieve the design objective. Clearly describe
your design procedure. Evaluate the performance of your designed controller. How would you
tune the parameters of the controller?
Task B.4 Model Predictive Control
Use model predictive control to design the controller. Clearly describe your design procedure.
Evaluate the performance of your controller. How would you tune the MPC controller
parameters and what criteria would you choose?
Task B.5 Iterative Learning Control
Design an iterative learning control law to achieve the design objective. Clearly describe your
design procedure and any assumptions you have made. Evaluate the performance of your
controller.
Task B.6 Comparison and Discussions
Compare the performance of the three control design methods. What do you find? Can you
explain your observations?
Critically evaluate your work. Have you achieved the objectives? How would you improve your
design? What problems did you find during this work? What conclusions can you draw from
your design? From the experience of this work, what do you need to consider when solving a
practical problem?
Assessment of Your Work: The project accounts for 50% of your final mark for this module. You will
be asked to upload a report and relevant program source files (with clear instructions on how to run the
program) before the deadline (16:00 26/04/2020) using the ECS handin system.
The report should consist of two parts, each carrying equal marks:
Part A should describe your implementation of Model Predictive Control on the LEGO Mindstorm
platform. This should be written as an individual activity, and all tasks involving LEGO Mindstorm
should likewise be completed as an individual activity.
Part B is should detail the implementation of Optimal Control, Model Predictive Control and Iterative
Learning Control on the Quanser QUBE-Servo platform, and fully evaluate and compare their respective
performance. Part B should be written as a group activity, as should all tasks involving the QUBE
platform. Note that the QUBE platform can be configured as either a servo-inertia, or a servo-pendulum.
ELEC6228 Coursework Design Project P1

It is up to you what choice you make, but you may want to weigh up achievability versus challenge in
making your decision.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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