Integration Project Systems and Control 2013 Group 3

Group Members

Name:	Student id:	Email:
Joep Alleleijn	0760626	j.h.h.m.alleleijn@student.tue.nl
E. Romero Sahagun	0827538	e.romero.sahagun@student.tue.nl
M. Kabacinski	0789360	m.j.kabacinski@student.tue.nl
N. Kontaras	0827208	n.kontaras@student.tue.nl
A. Simon	0676675	a.s.simon@student.tue.nl

Planning

Week:	Activities:
Feb 18 - Feb 24
	- Make a list for the requirements of the controller
	- Prepare and conduct tests for coupling/decoupling(JA,NK,MK) 3.5 hours
	- Prepare and conduct tests for non linearity (JA,NK,MK) 3.5 hours
	- Prepare and conduct tests for joint identification and create FRF models of the joints (JA,NK,MK) 5 hours
	- Set up DH transfer matrices (AS) 5 hours
	- Generate Matlab Simulation of the kinematic chain (ER) 7 Hours
Feb 25 - Mar 3
	- Finish tests for moving direction and create FRF models of the moving direction, moving direction is (JA,NK,MK) 5 hours
	- Design PID feedback controller, add feedforward and test on the robot for each axis of freedom (JA,NK,MK) 11 hours
	- Prepare testing procedure, for following of the trajectory and checking of the requirements (JA,NK,MK) 1.5 hours
	- Matlab code for trajectory generation (TG) (ER) 5 hours
Mar 4 - Mar 11
	- Test controllers on the robot (JA,NK,MK) 2 hours
	- Test trajectory generation on the simulation (ER) 3 hours
	- Integrate Inverse Kinematics with Controllers (AS,ER) 2 hours
Mar 12 - Mar 19
	- Consider different controller design methods (JA,NK,MK,AS,ER) 10 hours
	- Work on report (JA,NK,MK,AS,ER) 2 hours
Mar 20 - Mar 27
	- Test routine generation on the robot (JA,NK,MK,AS,ER) 2 hours
	- Select optimal routine (JA,NK,MK,AS,ER) 4 hours
	- Problem solving (JA,NK,MK,AS,ER) 10 hours
	- Consider different controller design methods (JA,NK,MK,AS,ER) 10 hours
	- Test different controllers (JA,NK,MK,AS,ER) 5 hours
	- Work on report (JA,NK,MK,AS,ER) 2 hours
Mar 28 - Apr 4
	- Work on report (JA,NK,MK,AS,ER) 15 hours
	- Prepare presentation (JA,NK,MK,AS,ER) 5 hours

Progress

Week 1: Feb 18 - Feb 24

1 Make a list of the requirements and design assumptions for the controller

Controller requirements:

- Each of the four inputs of the robot is required to follow the third order trajectory, thereby the resulting trajectory of the fork will be appropriate to safely move the three pizzas form there initial positions to the final positions.

- The error between the reference trajectory and the actual trajectory of the fork has to be smaller then 0.5 cm during the whole time of operation.

- The settling time of the controller needs to be less 0.5 seconds.

- The acceleration applied to the platform has to be as high as possible without a pizza falling of the platform while following the trajectory.

- The total time of transport of the three pizzas should be less 15 seconds.

Design assumptions:

- Position will be used as input reference for the pizza robot. The pizza robot provides its position as output, this information can be used to compare to the desired position in closed loop control.

- The controller works with the sampling frequency of 500 Hz, because the sampling frequency of the system is 500 Hz.

- FRF will be used to obtain a model of the system. This method can be used to obtain a model of a system without knowing all the parameters of the system.

- Initially a PID controller will be used to follow the setpoint trajectory. A PID controller will be used because it is a simple controller and every team member is familiar with the concept. To increase performance this PID controller can be extended with feedforward block. When this controller functions according to the set requirements, different control methods will be explored to investigate if it would be possible to increase performance.

2 Prepare and conduct tests for coupling/decoupling (JA,NK,MK)

Have not done yet, task is postponed to the next week. However so far we have not encountered problems with decoupling. It seems that the complete system can be approached as several SISO systems. This makes it possible to use decoupled (diagonal) controller. More detailed test are to be carried out next week.

3 Prepare and conduct tests for non linearity (JA,NK,MK)

Have not done yet, task is postponed to the next week.

4 Prepare and conduct tests for joint identification and create FRF models of the direction of motion (JA,NK,MK)

Started with FRF measurements, takes longer then expected. Especially for the vertical movement it is challenge to conduct a measurement without hitting the airbag limits of the machine. Most recent FRF models of the vertival fork displacement:

5 Set up DH transfer matrices (AS)

Inverse kinematics do not have to be determined, this has been already done, only trajectory and controllers have to be developed.

6 Generate Matlab Simulation of the kinematic chain (ER)

Kinematic chain is already provided.

7 Trajectory and Path Generation (AS,ER)

LSBP (Linear Segments with Parabolic Blends) and Quintic polynomial trajectory functions have been programmed, as well as a generic script for path generation using these functions. The functions allow to create the fastest trajectory between two points based on acceleration restrictions.

Week 2: Feb 25 - Mar 3

1 Make a list for the requirements of the controller

Controller requirements:

- Position will be used as input reference for the pizza robot. The pizza robot provides its position as output, this information can be used to compare to the desired position in closed loop control.

- Each of the four inputs of the robot is required to follow the third order trajectory, thereby the resulting trajectory of the fork will be appropriate to safely move the three pizzas form there initial positions to the final positions.

For the controllers the following margins will be applied:

- Phase margin: >45 degrees

- Gain margin: >6dB

- Modulus margin: <6dB

Rotation axis:

- The error between the reference trajectory for the rotation and the actual trajectory for the rotation of the robot has to be smaller then 0.25 degree.

- The amount of overshoot should be less than 5%.

- Bandwidth of the system (plant + control) should be high enough to minimize the non-linear friction static error, but low enough in order to not amplify noise. A bandwidth of between 5Hz and 10Hz seems appropriate.

- The rotation of the robot from one rack to the other rack should take less then 1.5 seconds.

Vertical axis:

- The error between the reference trajectory for the vertical movement and the actual trajectory for the vertical movement of the fork has to be smaller then 0.5 cm.

- The amount of overshoot should be less than 5%.

- Bandwidth of the system (plant + control) should be high enough to minimize the non-linear friction static error, but low enough in order to not amplify noise. A bandwidth of between 5Hz and 10Hz seems appropriate.

Horizontal axis:

- The error between the reference trajectory for the horizontal movement and the actual trajectory for the horizontal movement of the fork has to be smaller then 2 cm.

- The amount of overshoot should be less than 5%.

- Bandwidth of the system (plant + control) should be high enough to minimize the non-linear friction static error, but low enough in order to not amplify noise. A bandwidth of between 5Hz and 10Hz seems appropriate.

Translation axis:

- The error between the reference trajectory for the translation of the platform and the actual trajectory for the translation of the robot has to be smaller then 1 cm.

- The amount of overshoot should be less than 5%.

- Bandwidth of the controller should be high enough to minimize the non-linear friction static error, but low enough in order to not amplify noise. A bandwidth of between 5Hz and 10Hz seems appropriate.

- The controller works with the sample frequency of 500 Hz, the sampling frequency of the system is 500 Hz.

- The acceleration exerted on to the platform has to be as high as possible without a pizza falling of the platform while following the trajectory.

- The total time of transport of the three pizza's should be less 15 seconds.

- FRF will be used to obtain a model of the system. This method can be used to obtain a model of a system without knowing all the parameters of the system.

- The acceleration of the reference trajectories should be at least second order or higher to reduce the amount of overshoot

Assumptions:

- Initially a PID controller will be used to follow the set trajectory. A PID controller will be used because it is a simple controller and every team member is familiar with the concept. To increase performance this PID controller can be extended with feedforward. When this controller functions according to the set requirements, different control methods will be explored to investigate if it would be possible to increase performance.

2 Set up DH transfer matrices (AS) 5 hours

- Not necessary, already provided.

3 Generate Matlab Simulation of the kinematic chain (ER) 7 Hours

- Not necessary, already provided. Instead, the trajectory was built with the LSBP path generator. The trajectory was structured in such a way that LSBP function could at any moment be changed with the quintic polynomial path generator. The trajectory was built using the the shortest possible path. The lowest pizza will be transported to the lowest position, the middle pizza to the middle position and the highest pizza to the highest position. The lowest pizza will be first transported, then the middle and then the highest pizza. The robot arm will move from point to point (see figure below), moving only in one direction at a time, except when rotating without pizza. It will also change height during that motion.

- Improve the results of FRF measurements by applying new identification methods of course 5MB40-system identification.

- Implement PID controller and test together with trajectory of investigate if the trajectory can be followed or the controllers need to be improved or the trajectory can be made faster.

- As our initial plan states, we scheduled to do our FRF measurements by closing the loop with negative feedback on each axis, thus stabilizing it (with a low bandwidth controller), and then estimating the frequency response by using the three-point FRF method. We were able to get an acceptable FRF on the first axis (the vertical movement), however on the other three axes our results were unsatisfactory, with the main problem being the high noise making the gain and phase estimations unclear. In order to rectify these unclear readings, we attempted to perform open-loop FRF measurements. These experiments were done with special care, as the axes should not be allowed to touch the airbags. The results were satisfactory to a high degree. Then we proceeded to fit our frequency response data to discrete transfer functions using the System Identification Toolbox (using mainly the ARX and Box-Jenkins models). These high-order models were used for building our controllers. The advantage of using models is being able to observe our estimated performance (such as step responses) as well and not just stability margins (gain margin, phase margin, modulus margin etc.). High-order models were used in order to catch all the dynamics and phase loss due to sampling the system shows. The first axis was fitted into a Box-Jenkins model of 30th order (30 coefficients for model numerator and denominator each, and another 30 coefficients for the noise model). The 2nd and 3rd axes were fitted into ARX models of 120th order (120 coefficients for A(q) and another 120 coefficients for B(q)). The 4th axis was fitted into a Box-Jenkins model of 60th order (60 coefficients for model numerator and denominator each, and another 60 coefficients for the noise model). Below we present our results of our parametric model estimation, on top of the non-parametric frequency response estimation. From the latter we see the very good quality of our data. Axis1.jpg Axis2.jpg Axis3.jpg Axis4.jpg

- The trajectory built in the previous week was changed. During initial testing it could be seen that the rotation of the robot took more time than changing height or translating. Thus changing the height during the rotation from the left to the right position did not result in a slower trajectory. The order of placing the pizza's was then changed, to create more space when putting down the pizza's. The trajectory was adjusted to place the first pizza on the heighest position, then the middle pizza on the middle position and then the heighest pizza on the lowest position. This resulted in more movement space when putting down the pizza's. Week 4: Mar 11 - Mar 17 - Change requirement for the controllers depending on the findings of last week tests. Improved accuracy on rotational axes. - Test different PID controller that match better the requirement for each axes. - Controller Synthesis Using our previous system identification findings, we proceeded to our controller synthesis procedure. The parametric models allowed us to nicely observe the step response of our plant(s), and thus determine various performance and stability margins for our controllers (like Gain Margin, Phase Margin, Modulus Margin, Bandwidth, Settling time, Rise Time and Overshoot). The main tools we used were the loopshaping tool Shapeit and the PID tuning tool of Matlab. The complexity of the controllers was kept low, since our plants were not extremely complicated (resembled the expected double integrator transfer function quite substantially). Week 5: Mar 18 - Mar 24 - Improve controllers by adding feed forward. Construct low bandwidth controller, challenging trajectory to get a usable error profile to tune feed forward. - After testing the new trajectory, the results were satisfactory. We were now able to pick and place pizza's succesfully at a low speed. The accelerations of the different movements were increased until the error would become to big. To further improve the trajectory, unnecessary motions were deleted. The retracting of the arm after putting down a pizza was deleted, because there was enough movement space inbetween the two shelves. This resulted in a considerable time gain. - Consideration of different controller synthesis methods: Out of the various options we had to work with, PID tuning was the one that everyone had experience with, and thus contribute to the team. Additionally, other controller synthesis methods were discarded for various reasons, like unnecessary (H infinity robust control), hard to implement (Model Predictive Control), or focusing on different aspects instead of accuracy (LQR state space control). Week 6: Mar 25 - Mar 31 The next improvement for the trajectory was the overlap of movements. Until now, the movement in different directions was seperated as much as possible. However, it was noticed that this was not necesarry. For example, when putting down a pizza, instead of moving the fork out, then going down and then retracting the fork, these movements were combined. We achieved as much as 50% overlap while still placing the pizza's succesfully. This resulted in considerable trajection time reduction. Week 7: Apr 1 - Apr 5