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Robot control systems
Transcript of Robot control systems
4.2.13 7.1.13 14.1.13 21.1.13 Hand in
28.1.13 Closed loop control 0 + - = 9 8 7 1 2 3 4 5 6 c Extension activities Outcomes:
To Do List On-Off Control Systems Many heating systems work like this PID Control Proportional control
Integral control Servo motors
Joint values are calculated
Displacement, velocity, acceleration, applied force, torque Values sent to controller
Controller sends signals to actuators
Actuators run joints to their destination in controlled manner
Sensors measure outputs and feed back signals to controller
Controller uses feedback signals to determine actuating signals closed loop control
aka negative feedback NB: Only a week between hand in dates Want to know more? https://www.roboticslab.ti.bfh.ch/media/content/roboticslab/papers/2012/54/IOJ12_carpi.pdf Robot Control Systems multi axis control Activity Activity All learners will be able to
Describe on-off control system principles
Describe PID control system principles
Illustrate an application of a closed loop servo controlled system
Describe a system for control of three axes of a robot Read more:
The Robotics Primer
By Maja J. Matari Extension activity:
Research stepper motors and compare them with servo motors Imagine you are designing a controller for a wall-following robot The desired state of the system, also called the
is where the system wants to be So, if the system’s
current and desired states
are the same, it does not need to do
But if they are not,
which is the case most of the time,
how does the system decide what to do?
That is what the
design of the controller
is all about. Achievement goals Maintenance goals How would you describe its goal state?
How would you illustrate its goal state Recent conference research paper describing wall-following robot for plant inspections Achievement
goals Maintenance goals, on the other hand, require ongoing active work on the part of the
system. Keeping a biped robot balanced and walking, for example, is a maintenance goal.
Similarly, following a wall is a maintenance goal. Control theory has traditionally (but not exclusively) concerned itself with maintenance goals. Maintenance
goals Achievement goals are states the system tries to reach, such as a particular location,
perhaps the end of a maze. Once the system is there, it is done, and need not do any more
AI has traditionally (but not exclusively) concerned itself with achievement goals. In wall-following, the goal state is a particular distance, or range of distances, from a wall.
This is a maintenance goal, since wall-following involves keeping that distance over time.
Given the goal, it is simple to work out the error.
In the case of wall-following, the error is the difference between the desired distance from the wall and the actual distance at any point in time.
Whenever the robot is at the desired distance (or range of distances), it is in the goal state. Otherwise, it is not. Now imagine that the robot is equipped with a sensor that can sense the distance-to-wall. Eg sonar or laser.
The controller algorithm might look like this:-
If distance-to-wall is in the right range, then keep going.
If distance-to-wall is larger than desired, then turn toward the wall, else turn away from the wall.
Following these rules, what would the actual robot path look like? In general, the behavior of any simple feedback system oscillates around the desired
state. Therefore, the robot oscillates around the
desired distance from the wall; most of the time it is either too close or too far away. adjust the turning angle find just the right range of distances that
defines the robot’s goal compute the error often How can we decrease this oscillation? so the robot can turn often rather than rarely so the robot turns by small rather than large angles Decisions on the above will depend on the specific parameters of the robot system: the robot’s speed of movement, the range of the sensor(s), and the sampling rate.
The calibration of the control parameters is a necessary, very important, and
time-consuming part of designing robot controllers.
We can introduce PID control.... The basic idea of proportional control is to have the system respond in proportion to the error, using both the direction and the magnitude of the error.
A proportional controller produces an output o proportional to its input i, and is formally written as:
o = Kpi
Kp is a proportionality constant, usually a constant that makes things work, and is specific to a particular control system.
Such parameters are abundant in control; typically you have to figure them out by trial and error and calibration. sample point Damping refers to the process of systematically decreasing oscillations. A system is
properly damped if it does not oscillate out of control, meaning its oscillations are either completely avoided (which is very rare) or, more practically, the oscillations gradually decrease toward the desired state within a reasonable time period. Derivative control takes into account the rate of change of the error.
Proportional control only takes into account the size of the error.
The stability and overshoot problems that arise when a proportional controller is used at high gain can be mitigated by adding a term proportional to the time-derivative of the error signal Do you remember when you differentiate or find the derivative in maths, you are finding rate of change or gradient? 1. Investigate the response of the ladybird robot, if it moves at constant speed, and has a proportional controller that uses the error in distance to desired path.
For a larger error in distance, the ladybird turns through a larger angle towards the desired path.
You could try investigating the effect of different sampling rates, speeds, proportional constants and/or gains 2. Go to the website below and use the 2nd order simulator
http://www.facstaff.bucknell.edu/mastascu/eControlHTML/Intro/Intro1.html Desired path Remember the droop, or steady state error, that we are left with under proportional control?
Integral control will correct this.
For further info, see
http://www.facstaff.bucknell.edu/mastascu/eControlHTML/Intro/Intro3.html actuator role control system role servomechanism to move a joint or a link and change its position System doing both these roles to move a joint or a link and change its position to ensure that movement is achieved in a manner that is satisfactory, as planned Servo motor output shaft does not rotate freely - it is commanded to move to a particular angular position.
The electronic sensing and control circuitry - the servo feedback control loop- drives the motor to move the shaft to the commanded position. http://www.bing.com/videos/search?q=servomechanism+how+it+works&view=detail&mid=CCD65D557D9754F80D2DCCD65D557D9754F80D2D&first=0 How servo motors work http://www.jameco.com/Jameco/workshop/howitworks/how-servo-motors-work.html Feedback control Block diagrams Signal sequence to move a robot joint Feedback control is a means of getting a system (a robot) to achieve and maintain a
desired state, usually called the set point, by continuously comparing its current state with
its desired state.
Feedback refers to the information that is sent back, literally “fed back,” into the
system’s controller. In most systems there will be an input and an output. Signals flow from the input, through the system and produce an output. The input will usually be an ideal form of the output. In other words the input is really what we want the output to be. It's the desired output.
The controller acts on the error signal and uses that information to produce the signal that actually affects the system we are trying to control. The output of the system is measured with a sensor.
The input to the plant is usually called the control effort, and the output of the sensor is usually called the measured output. To control the system we need to use the information provided by the sensor.
Usually, the output, as measured by the sensor is subtracted from the input (which is the desired output) as shown below. That forms an error signal that the controller can use to control the plant. Thus, the controller has two things that it has to achieve.
The controller has to compute what the control errort should be.
The controller has to apply the computed control effort. The sophistication of the control algorithm determines whether a servo robot can operate
no control of the trajectory followed between each position
Eg, drilling robot used in the auto industry
precise tracking of a reference trajectory, including velocity and acceleration profile
Eg, welding robots can produce complicated 3D welding seams.