Integration Project Systems and Control 2013 Group 1

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{|
{|
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! align="left"| Week:
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! align="left"| Week: ||
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! Activities:
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|Feb 18 - Feb 24 || ...Week_1
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|'''Feb 18 - Feb 24'''||
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|-
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Our first aim is to brush up on the technical knowledge required in the related areas.
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|''Literature Study regarding the following aspects:''||
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*Literature study regarding the following aspects:
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|-
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1. Robot: input-output variables of the given system, to study the matlab files provided, non-linearities, friction model, coupled phenomena in the system.
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| - matlab files provided
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2. System Identification: to derive system model using two point method, to derive system model using three point method
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|-
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*Lab activity_System identification/Frequency response measurements : Thursday 21/02, we will be in the lab collecting data.More precisely, given that no information is available to formulate a model from first principles, we have to revert to methods of System Identification using input/output behavior of the system. Hence, both open loop and closed loop frequency response measurements will be conducted on the robot. Initially we will take measurements using one input at a time, and then all four inputs together.As inputs we will use white noise and chirp signals.
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| - inputs/output variables regarding the given model
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In the open loop method, we will input a white noise of small power into the four controlled motors of the system and measure the corresponding output response. Using the frf of the input and output, we will find out the cross power spectral density on the input and the output using Matlab command cpsd.m. Then, we will calculate the auto power spectral density of the input using Matlab command psd.m (or spectrum.m). Then by using the formula H(f)=Syu(f)/Suu(f) we will arrive at the frf of the plant.This can be easily converted into a transfer function by using the Matlab command tfestimate.m.
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|-
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In the closed loop method, first a stabilizing controller will be build using SHAPE-IT for the calculated transfer function from the direct open loop method. Then closed loop measurements will be made where we inject white noise into the system and calculate the sensitivity and the process sensitivity. The plant frf is then calculated by using the above formula.
+
| - different types of controller
-
 
+
|-
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Once we have derived the model, response of the system in terms of bandwidth, time response, and stability margins will be noted. The same controller will then be plugged into the hardware to check if the response is same. A similar response would mean that the derived model of the plant is accurate.
+
| - design Criteria
-
 
+
|-
 +
|''Lab activity:''||
 +
|-
 +
| - meeting the robot
 +
|-
 +
| - how the robot moves
 +
|-
 +
|'''Feb 25 - Mar 3'''||
 +
|-
 +
|''Literature study regarding the following aspects:''||
 +
|-
 +
| - non-linearities
 +
|-
 +
| - friction model
 +
|-
 +
| - coupled phenomena
 +
|-
 +
| - design a PID feedback controller(preparation for the lab)
 +
|-
 +
| - system identification procedures
 +
|-
 +
| - FRF measurement(preparation for the lab)
 +
|-
 +
|''Lab activity:''||
 +
|-
 +
| - design the stabilizing(PID) controller
 +
|-
 +
| - get the FRF measurements
 +
|-
 +
|'''Mar 4 - Mar 10'''||
 +
|-
 +
|''Literature study regarding the following aspects:''||
 +
|-
 +
| - feedforward Controller
 +
|-
 +
| - design criteria (bandwidth, margins, sensitivity, steady state error, time response)
 +
|-
 +
| - work with shapeit to design optimal controllers for each axis (prepare the controllers to test them during the lab)
 +
|-
 +
| - reference trajectory(preparation for the lab)
 +
|-
 +
|''Lab activity:''||
 +
|-
 +
| - get the FRF measurements for the left robot
 +
|-
 +
| - test the controllers that we construct on shapeit
 +
|-
 +
| - get the reference trajectory
 +
|-
 +
|'''Mar 11 - Mar 17'''||
 +
|-
 +
| - feedforward Controller
 +
|-
 +
| - design criteria (bandwidth, margins, sensitivity, steady state error, time response)
 +
|-
 +
| - work with shapeit to design optimal controllers for each axis (prepare the controllers to test them during the lab, for the left robot this time)
 +
|-
 +
| - reference trajectory
 +
|-
 +
| - test the controllers for the left robot
 +
|-
 +
|'''Mar 18 - Mar 24'''||
 +
|-
 +
| - tune the feedforwrd and feedback controllers for the left robot
 +
|-
 +
| - improve the trajectory regarding the design criteria for the left robot
 +
|-
 +
|'''Mar 25 - Mar 31'''||
 +
|-
 +
| - FRF measurements for the right robot
 +
|-
 +
| - work on shapeit to improve the feedback controllers for the right robot
 +
|-
 +
| - tune feedforward/feedback controllers for the left robot
 +
|-
 +
| - improve the trajectory
 +
|-
 +
| - work on the presentation
 +
|-
 +
|'''Apr 1 - Apr 7'''||
 +
|-
 +
| - optimazation of the trajectory
 +
|-
 +
| - work on the presentation
|-
|-
-
|Feb 25 - Mar 3 || ...Week_2
 
-
 
-
* More literature study regarding the following aspects:
 
-
1.Design Criteria/Specifications: bandwidth, steady state error, time response, sensitivity, modulus/phase margin
 
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2. study about different types of controllers: feasibility of using a PID controller, feasibility of using an LQR controller, feasibility of using an H-inf. Controller , feasibility of using Adaptive control, feasibility of using Feedforward control, refresh memory on the use of ref3 and shapeit in matlab.
 
-
*Lab activity_Reference trajectory: The first task is to find out the desired and optimal reference trajectory. That is, the desired motion of the end effector where the Pizza will be placed. The reference trajectory is a plot of position, velocity and acceleration of end effector in x, y and z directions against time. The ref3 tool provided to us in the motion control course is an excellent way to plot a Matlab compatible reference trajectory. With the help of this tool we are able to generate a 3rd order polynomial for the reference trajectory.  As the motor inputs directly correspond to the end effector horizontal, vertical and rotational displacements, we don’t need to find out the inverse kinematics. As the final time is undecided, a targeted time will be used, which will then be minimized after the controller has been designed, by using iterations. This minimum time will depend on the motor saturation voltages.
 
-
 
|}
|}
== Progress ==
== Progress ==
-
==== Week 1 ====
+
==== Feb 18 - Feb 24 ====
-
 
+
Regarding our planning for the first week we came up with the following conclusions:
-
==== Week 2 ====
+
{|
 +
|-
 +
| -making a literature research, we realized that PID controller is used in most industrial robots, and since it is the type of controller with which we are more familiar with, we will use PID.
 +
|-
 +
| -we can control the move of each axis (and so each motor) separately cause they are independent. So it is needed to construct 4 PID controller, since we can handle the problem as a 4 SISO systems problem.
 +
|-
 +
| -the design of PID controller is based on the fact that is should follow the setpoint trajectory.
 +
|-
 +
| -we will use as inputs for each motor sine wave, and to represent the disturbance we will use white noise.
 +
|-
 +
| -regarding the design criteria we need total time to transfer the pizzas at about 15 secs(less than that if it is possible), as high as possible velocity and acceleration (we have to take care that the pizzas should not fall down from the fork during the movement of the robot) and also the fork should not touch the holding brackets of the pizzas. Also, the controller should work with sampling frequency of 500 Hz, because this is the sampling frequency of the system.
 +
|}
 +
==== Feb 25 - Mar 3 ====
 +
Regarding our planning for the second week we came up with the following conclusions:
 +
{|
 +
|-
 +
|-the system can be approached as 4 SISO systems so we do not have problems regarding coupled phenomena.
 +
|-
 +
| -we designed the stabilizing controller for our plant (PID controller)
 +
|-
 +
| -regarding the system identification procedures, we decided to perform FRF measurements.We took the FRF measurements applying both sine waves and white noise in order to avoid the non-linearities since FRF method refers to linear models-systems.
 +
|-
 +
| -about the friction model, we will check it on a later stage together with the feedforward control.
 +
|}
 +
==== Mar 4 - Mar 10 ====
 +
{|
 +
|-
 +
| -we have to move the pizzas with maximum acceleration and velocity without pizzas fall down
 +
|-
 +
| -the robot should follow the trajectory in less than 20 sec
 +
|-
 +
| -testing the controllers we realized that only PID controllers need to be used (no notch filters etc)
 +
|-
 +
| -Design of feedforward controller
 +
|-
 +
| -measurements which will help us with the reference rtajectory
 +
|}
 +
==== Mar 11 - Mar 17 ====
 +
{|
 +
|-
 +
| -we improved the feedforward controller for vertical and horizontal axis
 +
|-
 +
| -we tried to tune the error on these axis
 +
|-
 +
| -we found the reference trajectory
 +
|-
 +
|}
 +
==== Mar 18 - Mar 24 ====
 +
{|
 +
|-
 +
| -we improved the feedforward controllers for all axis, achieving a very small error ( about 10^-3 - 10^-4 )
 +
|-
 +
| -tune again the feedback controllers
 +
|-
 +
| -improve more the trajectory
 +
|-
 +
|}
 +
==== Mar 25 - Mar 31 ====
 +
{|
 +
|-
 +
| -based on the results of FRF measurements for the right robot we built the feedback controllers
 +
|-
 +
| -Built feedforward controllers for the right robot - tuning the gains to achieve very small errors
 +
|-
 +
| -we worked in the left robot to improve it more
 +
|-
 +
| -we worked on the report
 +
|-
 +
|}

Current revision as of 21:10, 4 April 2013

Contents

Group Members

Name: Student id: Email:
Abhishek Bareja 0825433 a.bareja@student.tue.nl
Ioannis Kokkinakis 0832282 i.kokkinakis@student.tue.nl
Evangelos Stamatopoulous 0827743 e.stamatopoulos@student.tue.nl
Donatella De Cesare 0821444 d.de.cesare@student.tue.nl

Planning

Week:
Feb 18 - Feb 24
Literature Study regarding the following aspects:
- matlab files provided
- inputs/output variables regarding the given model
- different types of controller
- design Criteria
Lab activity:
- meeting the robot
- how the robot moves
Feb 25 - Mar 3
Literature study regarding the following aspects:
- non-linearities
- friction model
- coupled phenomena
- design a PID feedback controller(preparation for the lab)
- system identification procedures
- FRF measurement(preparation for the lab)
Lab activity:
- design the stabilizing(PID) controller
- get the FRF measurements
Mar 4 - Mar 10
Literature study regarding the following aspects:
- feedforward Controller
- design criteria (bandwidth, margins, sensitivity, steady state error, time response)
- work with shapeit to design optimal controllers for each axis (prepare the controllers to test them during the lab)
- reference trajectory(preparation for the lab)
Lab activity:
- get the FRF measurements for the left robot
- test the controllers that we construct on shapeit
- get the reference trajectory
Mar 11 - Mar 17
- feedforward Controller
- design criteria (bandwidth, margins, sensitivity, steady state error, time response)
- work with shapeit to design optimal controllers for each axis (prepare the controllers to test them during the lab, for the left robot this time)
- reference trajectory
- test the controllers for the left robot
Mar 18 - Mar 24
- tune the feedforwrd and feedback controllers for the left robot
- improve the trajectory regarding the design criteria for the left robot
Mar 25 - Mar 31
- FRF measurements for the right robot
- work on shapeit to improve the feedback controllers for the right robot
- tune feedforward/feedback controllers for the left robot
- improve the trajectory
- work on the presentation
Apr 1 - Apr 7
- optimazation of the trajectory
- work on the presentation

Progress

Feb 18 - Feb 24

Regarding our planning for the first week we came up with the following conclusions:

-making a literature research, we realized that PID controller is used in most industrial robots, and since it is the type of controller with which we are more familiar with, we will use PID.
-we can control the move of each axis (and so each motor) separately cause they are independent. So it is needed to construct 4 PID controller, since we can handle the problem as a 4 SISO systems problem.
-the design of PID controller is based on the fact that is should follow the setpoint trajectory.
-we will use as inputs for each motor sine wave, and to represent the disturbance we will use white noise.
-regarding the design criteria we need total time to transfer the pizzas at about 15 secs(less than that if it is possible), as high as possible velocity and acceleration (we have to take care that the pizzas should not fall down from the fork during the movement of the robot) and also the fork should not touch the holding brackets of the pizzas. Also, the controller should work with sampling frequency of 500 Hz, because this is the sampling frequency of the system.

Feb 25 - Mar 3

Regarding our planning for the second week we came up with the following conclusions:

-we designed the stabilizing controller for our plant (PID controller)
-regarding the system identification procedures, we decided to perform FRF measurements.We took the FRF measurements applying both sine waves and white noise in order to avoid the non-linearities since FRF method refers to linear models-systems.
-about the friction model, we will check it on a later stage together with the feedforward control.

Mar 4 - Mar 10

-we have to move the pizzas with maximum acceleration and velocity without pizzas fall down
-the robot should follow the trajectory in less than 20 sec
-testing the controllers we realized that only PID controllers need to be used (no notch filters etc)
-Design of feedforward controller
-measurements which will help us with the reference rtajectory

Mar 11 - Mar 17

-we improved the feedforward controller for vertical and horizontal axis
-we tried to tune the error on these axis
-we found the reference trajectory

Mar 18 - Mar 24

-we improved the feedforward controllers for all axis, achieving a very small error ( about 10^-3 - 10^-4 )
-tune again the feedback controllers
-improve more the trajectory

Mar 25 - Mar 31

-based on the results of FRF measurements for the right robot we built the feedback controllers
-Built feedforward controllers for the right robot - tuning the gains to achieve very small errors
-we worked in the left robot to improve it more
-we worked on the report
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