Have you ever tried balancing a ball on a racket? If you have ever played table tennis, then you should have tried balancing the tennis ball on your racket. How long did you manage to hold the ball against the gravity? 30 seconds? 2 minutes? My personal best time is around 5 minutes. You know that it is all related with gravity. I think it is not surprising to find out that human beings&#39; one of the most important invention is a circular object: Wheel. That might be the reason why it attracts our attention the most and we would like to experiment on a design that is engraved in our DNAs!

What about balancing the ball on a moving robotic-ish plate using some electronics, 2x servo motors and mechanics with a little bit of math and some voodoo! Not the last one hopefully. But something called PID (proportional&ndash;integral&ndash;derivative) controller would do the trick (and it has been doing many tricks for more than 100 years). Actually, a ball balancer, or a ball and plate experiment or a <a href="ball-balancing-table-3-2.html">ball balancing table</a> or many other fancy names point to the same classical control experiment that has been solved over and over. The idea is simple. Control the servomotors to change the orientation or inclination of a surface where a ball is moving freely with the aid of the gravity. Get some feedback either from some touch sensor or maybe through a camera. Use a closed-loop control system with a low-cost <a href="https://www.arduino.cc/">Arduino</a> or maybe with a high-end <a href="https://www.ni.com/myrio">NI myRIO</a> by implementing a linear or non-linear control algorithm and track the ball and its position error.

Some implementations use simple algebraic techniques such as error reduction, on-off controllers. Others utilize advanced control theory topics like fuzzy control, adaptive algorithms and so on, for which we have provided a tasty glimpse of the concepts in our blog page, &quot;<a href="overview-of-control-systems-in-the-universe.html">Overview of Control Systems in the Universe</a>&quot;

&nbsp;<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/figure-1.jpg" style="height:341px; width:400px" /> 
Figure&nbsp; 1 - Acrome Ball Balancing Table keeping the ball right into the spot, well done calibration!

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If we make a deeper dive into the system, we can find some much fancier use cases of our fellow ball balancer. Here is a good list that encapsulates what could be done with a decent quality ball balancer system:

 
1) Plate Orientation Control (in 1D or 2D surface)

Let&#39;s start with the simplest one. Definitely one can control each of the motors (one motor for 1D, 2 motors at the same time for 2D) to change the orientation of the plate and for what? Well you may work on the 2nd law of Sir Newton for physics experiments on inclined surfaces to analyze the effects of friction and acceleration and speed. Although a round ball is not the best item to analyze this problem, using any comparable size object is acceptable if you have a touch sensor for the feedback.

&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/figure-2a.jpg" style="height:258px; width:200px" /><img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/figure-2b.png" style="height:197px; width:300px" />

Figure 2 - Sir Isaac Newton and the infamous friction and acceleration exercise. Do you know which law one of his law&#39;s related with this problem?&nbsp; [Image is in courtesy of <a href="https://www.khanacademy.org/science/physics/forces-newtons-laws/inclined-planes-friction/a/what-are-inclines">Khan Academy</a>]

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2) Frequency Domain Analysis

If you have studied a little bit of control theory or maybe signals and systems, then you should be familiar with the term called frequency domain analysis. Any physical (and electronic as well) system has a limited response to the rate of frequency that they are used with. And as with the previous sentence, it is always hard to visualize and fully understand the concept of frequency response. Not even mentioning about drawing a Bode Diagram to analyze the response.

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/yeni-figure-3.png" style="height:229px; width:680px" /> 
Figure 3 - Ready to run Bode Analysis Example of ACROME Ball Balancing Table draws a bode plot with the system model. Detailed courseware available at <a href="ball-balancing-table-3-2.html">Ball Balancing Table</a> page.

 
A beautiful experiment that can be applied with a ball balancing table is the frequency response of the system and there should be no worries to make an error. It is a fully error free system. If you follow a circular trajectory with the ball on the table, then you will have a very safe and a quite easy to control system suitable for frequency domain analysis. By changing the speed of revolution, you will change the frequency of the system. Faster the ball revolute, higher the frequency of a system to control the rotation. This experiment is not widely known but it is very easy to implement. If you own a ball balancer, go ahead and give it a try. If you don&#39;t have a system currently, then you may consider enrolling to our <a href="acrome-webinar-series-introducing-new-hands-on-experiments.html">webinars </a>for a live demonstration.

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3) Real time Proportional&ndash;Integral&ndash;Derivative Control (PID Control)

Assuming that we all know the PID (proportional&ndash;integral&ndash;derivative) controllers ... Or do we? Definitely, PID is not a rainbow that you can see in your daily life (or depending on where you live -hopefully not in Kalahari Desert- in weekly, monthly or at least a yearly time-frame).

Although PID is a mature[1] fundamental topic, most of the engineering students cannot experiment PID basics in the real world like moving, juggling, boiling or using a similar control equipment. With many other community references, we believe that success in education increases with something called applied learning.

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/figure-4.jpg" style="height:428px; width:900px" /> 
Figure 4 - Results from a Google search of &quot;Applied Learning&quot;

 
According to an <a href="https://uncw.edu/eteal/overview/appliedlearning.html">UNCW article</a>, applied learning is a pedagogical model that places students in experiences requiring them to integrate theories, ideas, and skills they have learned in new contexts, thereby extending their learning. And it is always hard to reinforce engineering concepts -such as PID- with practical lab experiences. Sometimes difficulty comes from the installation and operational costs. Even if there are existing resources, sometimes students are getting buried under non-conceptual details due to the inefficient pieces. Common problems are loose cables, mechanical deficiencies, missing tools, missing software pieces, missing instructions or even lost user manuals. Virtual labs are not ready to provide quality service and coherentcontent.&nbsp;

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<div id="_com_1">An optimum solution is to have a user-familiar, open platform made with precision crafted mechanics yet with an acceptable price tag.</div>
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Closing the loop of the PID control; a ball balancing table is a good example of a real time PID controller to control the plate orientation for experimenting different PID parameters and controller types. Important concepts such as damping factor, overshoot-undershoot, integral windup; are all easy to use and easy to experiment by the most fundamental ball position control experiment (and with the help of a step input, which could be your hand).

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4) Trajectory Tracking

If we have a real-time control system with some good response speed to control the ball&#39;s position then we can use the same system to arbitrarily place our ball into any location on our platform, well in this case on the table. Then what? Although there are many good usage examples such as reducing the vibration of CD-DVD players as explained in nice academic papers [2][3], we can use these systems to easily track some predetermined trajectories such as rectangular or circular paths, or arbitrarily drawn paths. You may check our ACROME Ball and Balancing Table below&nbsp;to see a good &quot;infinite path&quot; example.&nbsp;

<iframe frameborder="0" height="315" src="https://www.youtube.com/embed/tPr6-Rh0m-8" width="560"></iframe>

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5) Linear and Nonlinear Control

Choosing the type of the controller depends on system objectives and needs. Conventional control methods (such as PID described above) assume that the behavior of a system is linear, and the system transfer function is known. Hence, the controller parameters can be found mathematically by using the transfer function. What if the system is a non-linear one? Then a different approach should be used for controlling the system. One good method to control the non-linear system is with something called fuzzy logic controller, which was presented by Professor Lotfi Zadeh in 1965 but not as a control methodology in the beginning. Luckily later in 1972, he presented a new paper[4] and introduced the term fuzzy rules and linguistic variables in the control theory.

In contrast to conventional control methods, fuzzy logic does not require a system transfer function or a linear system. Fuzzy logic uses linguistic variables such as negative, zero, positive, small, medium and large. These linguistic variables which form fuzzy sets-inputs and outputs of the fuzzy controller- are formed by combining those fuzzy sets.Next, rule-base (aka linguistic rules) is obtained by using linguistic variables.Fuzzy inference process is used for determining which rules are relevant to the current situation by using rule-base. Finally, a single output is produced by using one of the defuzzification methods.

Wow! Somany academic definitions. But how can we use this information in real life? Naturally with a solidexperiment and some graphical user interface to reinforce the experiment. Below you may find a sketch of the fuzzy control system and the way we implement this system in our ball-balancing product. By the way, all the source code of the ACROME ball-balancing table is open source and you can read and modify the source code however you want.&nbsp;

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/yeni-figure-5.png" style="height:243px; width:900px" /> 
Figure 5 - Fuzzy Logic&#39;s Block Diagramand User Interface to Implement

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6) System Model Derivation and Linearization

If you are familiar with the control theory then you should have heard that, &quot;give me the model of any system then I can control it with anything!&quot;or maybe not. In reality, deriving the mathematical model of any physical system is a tedious and mostly a hard job involving mathematical operations, many observations and assumptions. Even though it is a simple experiment, a ball-balancing table is not an exception and there are multiple variables to deal with. The system model must take into consideration about 11 parameters to faithfully simulate the physical realm.

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/figure-6.png" style="height:445px; width:900px" /> 
Figure 6 - Physical model of the Ball Balancing Table and its parameters involved in mathematical model

 
The first step of the modeling is to derive the equation of motion. The equation of motion describes the relationship between motion of the ball in (𝑥𝑏, 𝑦𝑏) plane and the two angles of the table (&alpha;, &beta;). The equation of motion is available in our courseware and derived with two different methods, Lagrangian Method and Newton&#39;s Law of Motion (as described in above section). The aim of modeling is to derive the physical relationship between the outputs and inputs of a system. In our case, the outputs of the system are the coordinates of the ball in x and y-direction (𝑥𝑏, 𝑦𝑏). The inputs of the system are the motor angles (𝜗𝑥, 𝜗𝑦). To obtain the transfer function of the system, the differential equations should be linearized about the operating point (x=0, y=0). We are giving more insight into the linearization process in the courseware.

At the end of all above efforts (and many mathematical operations, formulas that we have fast-forwarded), we are able to end up with a validated transfer function and a block diagram of the ball balancing system as given in thefigure underlined below:

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/figure-7.png" style="height:260px; width:900px" /> 
Figure 7 - Block diagram of the Ball Balancing Table

 
That&#39;s all from this blog post. Let us know which topics you want us to cover in our future posts. Also you can subscribe to our newsletter.

To learn more about our ball balancing table, mechanic tips and tricks, and application codes etc. join us in an upcoming webinar&nbsp; or feel free to send a message through <a href="cdn-cgi/l/email-protection.html#98f1f6fef7d8f9fbeaf7f5fdb6f6fdec">[email&#160;protected]</a>.

 
Related Readings: 
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<a href="https://www.emersonautomationexperts.com/2013/process-optimization/pid-control-history-and-advancements/">[1]: PID Control History and&nbsp;Advancements blog written by Jim Cahill</a>

<a href="http://www.dct.tue.nl/New/Wouw/IJBC2005.pdf">[2]: Performance&nbsp;of&nbsp;an Automatic&nbsp;Ball&nbsp;Balancer&nbsp;With&nbsp;Dry&nbsp;Friction&nbsp;</a>

<a href="https://ieeexplore.ieee.org/document/7944590">[3]: Automatic Ball Balancer Using Permanent Magnets to Reduce Transient Vibration&nbsp;</a>

<a href="http://dx.doi.org/10.1080/01969727208542910">[4]: L. A. Zadeh (1972) A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges, Journal of Cybernetics, 2:3, 4-34, DOI: 10.1080/01969727208542910 -&nbsp;</a>

 
Disclaimer:&nbsp; 
 
Arduino is the trademark of Arduino AG and NI myRIO is under trademark of National Instruments. Other product and company names listed are trademarks and trade names of their respective companies.

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Here is a good list that encapsulates what could be done with a decent quality ball balancer system: 6 different control lab experiments.

6 Different Control Engineering Lab Experiments That Can Be Related With A 2-DOF Ball Balancer

2018 was such a productive year for robotic developments. Different kind of industries like medical, bio, domestic and toy segments have all made brilliant advancements with the help of artificial intelligence. Naturally, with the upcoming hardware into the market, all the old techs are falling down and replaced by new versions and 2018 has seen many of upgrades in myriad ways. In this blog, we will take a look at a few net robots and robotic developments for the last year.

<h2>1) Boston Dynamics&#39; Atlas Performs Parkour</h2>

<div class="gmail-_1mf gmail-_1mj" style="direction: ltr; font-family: inherit;">The humanoid robot of Boston Dynamics&#39; , Atlas had an amazing journey since 2013. Atlas is well experienced with several revisions and upgrades with the ability of battery powered and additional sensors to increase stability. Now, Atlas can run, jump over obstacles and even perform backflips. </div>

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<div class="gmail-_1mf gmail-_1mj" style="direction: ltr; font-family: inherit;"><iframe frameborder="0" height="315" src="https://www.youtube.com/embed/fRj34o4hN4I" width="560"></iframe></div>

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<h2>2) JAXA&#39;s MINERVA - II Robotic Rover Captures Video from Asteroid Ryugu</h2>

Months ago, The Japan Aerospace Exploration Agency (JAXA) announced their robotic rover MINERVA-II deployed from their unmanned spacecraft then landed on the asteroid Ryugu. The rovers move by hopping in the asteroid&#39;s low gravitational field, using torque generated by a pair of rotating masses housed inside the vehicle. The rover has stero and wide angle cameras, also thermometers and solar cells &amp; double-layer capacitors to power prodution.

<iframe frameborder="0" height="315" src="https://www.youtube.com/embed/cbwZUvXBfI4" width="560"></iframe>

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<h2>3) Sony&#39;s Limited First Litter Edition Aibo Becomes Available in the U.S.</h2>

The first version of Aibo &quot;the pet dog&quot; released in 1999 and nowadays the 4th generation is available in the market. With the last version, the product becomes more compact about packaging. And with the new design, Aibo gets more realistic look and more natural behaviours including tail &amp; ear wagging and mouth and paw motions.&nbsp;Aibo just recently became available in the US for $2,899, a great holiday gift for those looking for a pet that doesn&#39;t require too much care.

<iframe frameborder="0" height="315" src="https://www.youtube.com/embed/oGo0TwNXXuo" width="560"></iframe>

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2018 was such a productive year for robotic developments. Different kind of industries like medical, bio, domestic and toy segments have all made brilliant advancements with the help of artificial intelligence.

Robotic News of 2018 (Part 1)

Stepper motors are the motors that operate with very precise signals. They change the angular position step by step with the pulse signals applied to the motor input. The stepper motors, which convert digital inputs into analog rotational motion, are also known as &quot;digital machines&quot;

To drive the stepper motor, the bobbins of the stepper motor are energized with electronic switching. The rotor moves and stops across the energized bobbin. This is called the full step. In half steps, two bobbins are energized together, and the rotor moves half a step by standing between two bobbins. If you want the motor to turn for a long time, you have to energize the bobbins one by one during the cycle. Figure 1 shows the stepper motor structure.

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/01.png" style="height:223px; width:500px" />

Figure 1: internal structure of stepper motors

We use stepper motor-driven systems in many areas such as printers, 3D printers, CNC machines, hard disk drives. Stepper motors in various sizes are shown in Figure 2.

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/02.png" style="height:317px; width:500px" />

Figure 2: Types of stepper motor

The stepper motor is a motor that provides position and speed control at high accuracy. They are ideal for applications that require fast positioning at short distances, high torque and low vibration at low speeds.

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This application explains how to drive stepper motor using NI myRIO. The input signal required to run the stepper motor is generated using NI myRIO, and the code generated in the LabVIEW block diagram can also be monitored with the front panel of LabVIEW. Figure 3 shows the block diagram of the application.

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<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/03.png" style="height:287px; width:500px" />

Figure 3 : Block diagram of the application.

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Used materials:

&bull; NI-myRIO 
&bull; ACROME Power Box 
&bull; NEMA17-HS5005 
&bull; 1000-line incremental rotary encoder 
&bull; TB6560 stepper motor driver 
&bull; 24V Power Supply

In many applications stepper motors are controlled with open loop. There is no need for a feedback. Position calculations are based on the number of pulses in the input signal. However, since stepper motors are not ideal structures, the calculated position in stepping situations can be wrong. In order to avoid such a situation in this application, an incremental encoder was used together with a stepper motor, so the feedback was received via the motor.

Stepper motors have different types of structures. Permanent magnet stepper motors, variable reluctance stepper motors, hybrid stepper motors, linear stepper motors, etc. In this application, hybrid NEMA 17 stepper motor is preferred.

<img alt="" src="https://acrome-net.s3.us-east-2.amazonaws.com/04.png" />

Figure 4: NEMA17 stepper motors

Hybrid stepper motors have permanent magnets in their rotors. These motors are called hybrids because the working principle is the combination of permanent magnet stepper motors and variable reluctance stepper motors. Stator&#39;s core structures are very similar to variable reluctance stepper motors, but winding connections are different. These motors perform better than permanent magnet stepper motors when they are used for features such as step stability, speed and torque.

In the application, the TB6560 was used as a stepper motor driver. TB6560 can limit the motor power to the switches on it and it can change the number of steps per pulse.

<img alt="05" src="https://acrome-net.s3.us-east-2.amazonaws.com/05.png" style="height:405px; width:500px" />

Figure 5: TB6560 stepper motor driver

The pulse signal generated by NI-myRIO to drive the stepper motor was connected to the CLK + pin of the TB6560 driver. To use the NI-myRIO&#39;s FPGA, you must use the LabVIEW FPGA Module. For this, a program was designed in the FPGA section of the LAbVIEW project. By generating the pulse signal from myRIO; the speed of the motor has been changed by turning the DIO8 pin ON / OFF during variable periods. CW pin of the TB6560 driver was connected to the DIO9 pin of myRIO to be able to determine the direction of the stepper motor. The motor changes direction according to the ON-OFF state of the DIO9 pin. The ENA+ pin on the TB6560 driver is not used to stop the motor. The Stop button in the LabVIEW program is used to hold itself at a certain torque when the motor is stopped, and the CLK signal pin is turned OFF when the Stop button is activated.

<img alt="06" src="https://acrome-net.s3.us-east-2.amazonaws.com/06.png" style="height:186px; width:500px" />

Figure 6: Motor drive section of the LabVIEW FPGA program

The encoder on the back of the motor outputs a square wave signal from 2 separate channels. The encoder signal sends 1000 lines of data in a full turn of the motor. The LabVIEW-FPGA code, which detects the rising and falling edges of the square wave, reads the data in detail. The full rotation of the motor is equivalent to 4000 encoder steps. In this way, it is possible to make very precise positioning.

<img alt="07" src="https://acrome-net.s3.us-east-2.amazonaws.com/07.png" style="height:228px; width:500px" />

Figure 7: Encoder signal read section of the LabVIEW FPGA program

There are control inputs and buttons on the front side of the LabVIEW FPGA program. OnTimeX indicates the ON duration of the generated square wave. TotalTime is the period of the square wave. Motor speed can be adjusted by changing TotalTime

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Note that TotalTime must be greater than OnTimeX.

<img alt="08" src="https://acrome-net.s3.us-east-2.amazonaws.com/08.png" />

Figure 8: OnTime &amp; TotalTime

Stop button stops the motor. The CW button changes the direction of the motor. Encoder Count (Base) shows the encoder value instantly. The &quot;stop&quot; button stops the program.

<img alt="09" src="https://acrome-net.s3.us-east-2.amazonaws.com/09.png" />

Figure 9: Front panel of the LabVIEW FPGA program

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driving-a-step-motor-using-labview-fpga-module.jpg

Stepper motors are the motors that operate with very precise signals. They change the angular position step by step with the pulse signals applied to the motor input. The stepper motors, which convert digital inputs into analog rotational motion, are also known as "digital machines".

Driving a step motor using LabVIEW FPGA Module

Ball Balancing Table: 2 DOF Control Platform for Teaching and Research. Control System Experiment and Ball Plate Courseware.

ACROME Ball Balancing Table is an innovatively designed experiment system suitable for controls and mechatronics education and advanced research topics.

Ball Balancing Table

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2 DOF Control Platform for Teaching and Research

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ACROME Ball and Beam System is ideal to introduce students to the fundamental and intermediate principles of controls.

ACROME Ball and Beam System is ideal to introduce students to the fundamental and intermediate principles of controls. Let's teach automatic control with Ball and Beam!

Ball and Beam

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1 DOF Control Platform for Teaching and Research

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Delta Robot, 3 DOF Robotic Platform for Teaching and Research.

ACROME Delta Robot is best-in-class robotic platform to understand parallel kinematic robotic fundamentals without the barriers.

Delta Robot

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3 DOF Vision Guided Robotic Platform for Teaching and Research

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ACROBOT is a test bed for collaborative robotics, intelligent control systems and Industry 4.0.

ACROBOT is a robot arm system with custom joint options, to be used as a test bed for collaborative robotics, intelligent control systems and Industry 4.0.

Acrobot

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6 DOF Robotic Arm - Acrome Robotics

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6 DOF Robotic Arm for research and teaching

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6 DOF modular lab platform for robotics, mechatronics, and controls.

ACROME Stewart Platform is a parallel robotic system with custom size options, could be used for advanced robotics concepts in academia and various applications in industry.

Stewart Platform

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6 DOF sophisticated motion system for robotics and controls

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ACROME 1 DOF Copter is a perfect introductory plant for learning fundamentals of flight dynamics and control.

1-DOF Copter

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Flight simulation control unit for teaching and research

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Linear Inverted Pendulum: Designed for learning and testing advanced feedback-control algorithms using an unstable non-linear system.

ACROME Linear Inverted Pendulum is designed for learning and testing advanced feedback-control algorithms using an unstable non-linear system.

Linear Inverted Pendulum

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2 DOF Control Platform for Advanced Control Teaching and Research

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