Magnetic Encoders VS Optical Encoders

Encoders, whether rotary or linear, absolute or incremental, typically use one of two measuring principles—optical or magnetic. While optical encoders were, in the past, the primary choice for high resolution applications, improvements in magnetic encoder technology now allow them to achieve resolutions down to one micron, competing with optical technology in many applications. Magnetic technology is also, in many ways, more robust than optical technology, making magnetic encoders a popular choice in industrial environments.

Parameter Optical Sensor Characteristics Magnetic Hall Sensor Characteristics
Principle  coded disc/scale, through beam arrangement  magnet/tape/polewheel opposed to sensor
Incremental accuracy of target  100 nm – 1 μm

(lithography process)

 5 μm – 30 μm

(magnetisation process)

Energising by external LED (20 mW)  by target (Br>220 mT)
Signal Frequency  > 1 MHz possible  < 50 kHz
Benefits  high code density, high code accuracy  robust
Disadvantages  sensitive to contamination, high alignment requirements  raw code density, medium code accuracy

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Complementary Encoder Signals to mitigate the electrical noise

Electrical noise is a common problem that occurs during the transmission of an incremental encoder’s signal to the receiving electronics, especially when the cable lengths are very long. Stray electromagnetic fields or currents induce unwanted voltages into the signal. These voltages can cause the receiver to make false counts, producing errors in the position or velocity feedback.

The primary way to alleviate electrical encoder noise is to use TTL output – also known as differential line driver output. This output format provides not only the standard A and B square wave signals and a Z reference signal, it also includes their complementary signals, /A, /B, /Z (sometimes written as A’, B’, and Z’). These complementary signals are produced by splitting the output of each channel (A, B, and Z) into two signals that are 180 degrees out of phase (complements) with each other. In other words, when the A signal is high (logic state 1) the A’ signal will be low (logic state 0). The receiving electronics take the state of that channel as the difference between the two signals.

encoder noise

In order for the complementary signals to be read, however, the receiving electronics must have a circuit that is designed for differential input – known as line receiver input. In addition, the wires for each channel (A and A’, for example) should be a twisted pair. In this twisted pair of wires, any electrical encoder noise that is induced will be the same on each signal. The receiving electronics recognise only the difference between the two signals, and because the signals are complements (equal in magnitude, with 180 degree phase lag) but the noise is common mode (equal on each signal, with no phase lag), the noise is cancelled out on the receiving end.

Image result for Incremental optical encoder quadrature operation

Incremental quadrature encoder for noise rejection

 

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Posted in Actuators, Robotics, Sensors

Optical Encoders in Brushless Servo Motors

The trend towards wide scale use of Brushless Motors is being driven by manufacturing
cost reductions, improved efficiencies, greater reliability, availability of improved drive
electronics, and the availability of improved sensors for motor control.

The brushless motors, independently whether they are PMSM or BLDC motors, require to know the relative position of the windings with respect the motor poles. To do this, a set of Hall Effect sensors are typically employed and placed between windings. An alternative option is to use the positional feedback from more accurate sensors to determine the presence of the magnetic field without using Hall Effect sensors. However, an intelligent drive is required to perform this count and issue the signal that conmutates the phases. This action is performed automatically with the bipolar Hall-Effect sensors.

Image result for hall effect sensors

Hall Effect sensor

For the majority of applications in the US and Japan, the trend in brushless motor sensor designs is moving away from Hall boards and feedback elements to integrated devices. For resolver applications, it can be handled by adding a dedicated set of 2, 3, or 4 speed windings for commutation, or it can be handled with a single speed winding and an intelligent drive.

The elimination of the Hall sensors from the BLDC motor eliminates many of the potential problems which can occur in a motor application. Hall devices are sensitive to acoustic noise, current spikes, temperature, EM fields, and can be difficult to align, which results in torque ripple. When a BLDC motor is used in a servo application with a high resolution feedback sensor, Hall sensors are redundant and consume space. They also add to motor length, assembly costs, cable harnessing complexity, and decrease overall reliability. The use of an encoder or resolver to eliminate Hall sensors in this situation is not only cost effective, but also improves the overall system performance.

1

Drive with Hall board, Encoder or Resolver for Commutation and Feedback a caption

2

Drive with Commutating Encoder

3

Encoder Types.

When an encoder is used as the feedback element, there are a variety of types to choose from. The following is a short summary of the predominant types currently available.

1. Incremental, (TTL)
Readily available from a wide variety of Suppliers. Almost unlimited line count availability up to 5000 cycles per revolution. Special line counts and output options
are easily obtained.

2. Incremental with Commutation, (TTL)
Becoming more common in the US and Japan, availability is somewhat constrained by lack of industry standards. Mounting configuration, signal conditioning, and power supplies vary widely. Available in line counts up to 8000 for 2, 4, 6, and 8 pole motors. They are being developed in both hollow-shaft and modular versions by a variety of encoder suppliers.

3. Incremental with Commutation, (Sine wave)
More common in Europe, this type of encoder generally has sinusoidal quadrature
outputs, with a 1 volt pk-pk amplitude. Commutation is accomplished using a quadrature one cycle per revolution output.

4. Absolute Single Turn, (TTL/Parallel)
Less common for drive applications, these are usually found in 10 to 12 bit versions. Larger word sizes are available, but costs become a real issue and make them unsuitable for all but the most specialized applications.

5. Absolute Multi-turn, (Sine wave Incremental, Serial Absolute)
These encoders are generally based upon a 12 or 13 bit single turn absolute encoder, with a 12 bit turn counter yielding 24 or 25 bits of position information. Although these have been available for some time, they have been too costly for widespread applications. Recent developments in Europe, however, are making these more available, and costs are starting to come down. These encoders contain an incremental output with A, B, and Reference pulse, a serial absolute interface, and commutation outputs. Commutation output is derived from the MSB of the single-turn absolute. The Incremental tracks are derived from the LSB of the absolute encoder, and generally result in a 2048 or 4096 cycles per revolution incremental signal that is suitable for use in high-speed servo controls.

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A good robotic blog

One of my mates is developing several projects related to robotics which are worth mentioning.  In his blog,  one of his last projects is the construction of an humanoid robot based on steppers.

Main Assembly v114 Spine

Posted in Robotics

Backdrivability

There is a trend in robotics towards the use of backdrivable actuators due to multiple reasons. One of them, is the need of sharing the environment with humans. Such a robot needs flexibility to adapt the differences between real environmental condition and assumed environmental condition. The robot´s joints may need to be articulated by a human and that would require high backdrivable actuators with low friction.
There are many other applications where a backdrivable actuator is needed, for example, in teleoperation, when Position-Position control is implemented. In teleoperation, a master device is controlled by a human operator and a slave device placed in a safe area mimics the movements of the master device. Position-Position uses the difference in position between master and slave to calculate the feedback forces to the operator. The more the positional error, the greater the forces. This positional error would be much more reduced with non-backdrivable actuators, and then, more difficult to provide feedback to the operator. At the end of the day, what you want in your slave device is to behave like a human, to react to the external forces as a human would do. If having a very stiff slave, the positional error would be minimum in normal tasks and the operator would not feel anything.

In any circumstance where the estimation of the external forces is needed, a backdrivable actuator is preferred. This could also be the case of walking robots, where it is important to feel the impact between the leg and the ground and react accordingly with a determined compliance.

Backdrivability is essential for safe robotic-arm operation around people; operating in unstructured environments; for stable control of contact forces; and for exploiting Jacobian-Transpose safely to enable Cartesian control of forces, haptic objects, and direct Cartesian control of trajectories.

Backdrivability is the ability for interactive transmission of force between input axis and output axis. To get high backdrivability, we have to reduce friction of power transmission considerably. Backdrivability provide actuators with high force sensitivity and high impact resistance which adapts to quick external force mechanically.

In rehabilitation robotics, particularly in upper limbrobotics, the drives must be able to deliver high torques at low velocity. Therefore, many rehabilitation robots are driven by motor-gearbox combinations. In contrast to direct-drive motors, the backdrivability of geared drives is poor due to friction in the gearbox. The back-driving torque sb can be defined as the amount of torque the human must apply to the robotic joint in order to perform a user-driven movement. Perfect backdrivability is achieved if sb = 0.

In [1] it is suggested that a reduction ratio over 60 in harmonic drives creates non-backdrivable actuators.

The backdrivability of a gearbox is highly correlated with the friction and the efficiency. Hence, let us have a look to the efficiency of different types of gears to get a initial impression of the backdrivaility of them.

In [2] a comparison between Harmonic drives and Cycloid drives is shown. In their research, Cycloid drives fitted into the same package diameter as harmonic drives with equal torque-generating capabilities. In their research, they demonstrated many advantages over harmonic drives, including substantially greater efficiency (especially at low torques) and lower reflected inertia, and often provided a thinner profile. These benefits were offset, however, by substantial disadvantages, including significant backlash and gear ratio ripple.

They conclude that, neither Cycloid nor harmonic drives are universally superior for all applications and conditions. However, Cycloid drives should be considered for applications in anthropomorphic robots and prostheses, especially those in which size, inertia, and efficiency take precedence over backlash and torque ripple.

Gear Efficiency Comparison Table

No Type Normal Ratio Range Efficiency Range
1 Spur 1:1 to 6:1 94-98%
2 Straight Bevel 3:2 to 5:1 93-97%
3 Spiral Bevel 3:2 to 4:1 95-99%
4 Worm 5:1 to 75:1 50-90%
5 Hypoid 10:1 to 200:1 80-95%
6 Helical 3:2 to 10:1 94-98%
7 Cycloid 10:1 to 100:1 75% to 85%

Double helical gear drives are considered to be more efficient than single helical gears.

spur gear

Spur Gears

Straight Bevel

Straight Bevel Gears

7_spiral-bevel-gears

Spiral Bevel Gears

hypoid-gear2

Hypoid Gears.

cycloid

Cycloid Gear.

Double_Helical_Gears_edit (1)

Double Helical Gears.

gear-helical2

Helical Gears

worm

Worm Gears.

[1] Instrumented Harmonic Drives for Robotic Compliant Maneouvres. H. Kazerooni.
[2] Cycloid vs. Harmonic Drives for use in High Ratio, Single Stage
Robotic Transmissions. Jonathon W. Sensinger, Member, IEEE and James H. Lipsey.2012 IEEE International Conference on Robotics and Automation
RiverCentre, Saint Paul, Minnesota, USA

 

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Notes on encoder resolution for velocity control of motors

The simplest velocity estimation method is the Euler approximation that takes the difference of two sampling positions divided by the sampling period. Typically the position measurements are taken with encoders or resolvers which contain stochastic errors which result in enormous noise during the velocity estimation by the Euler approximation when the sampling period is small and the velocity low [1] .

Image result

Encoder

Different alternatives have been tried which utilise more backwards steps to reduce the noise but introducing a small delay. On [3]  a first order adaptive method is shown which is able to vary the backward steps depending on the speed. Also, on [2] it has been found that 3 steps is the best for a sampling rate of 2500 Hz in their experiments with an encoder of 655360 pulses per revolution. They also implemented a Kalman observer and non-linear observers, obtaining the same results than an averaging of the Euler formula. On [4] a Kalman filter is tested assuming a normal distribution of the position error. On [1] a dynamic method which varies the samples used for averaging depending on the speed is developed with very good results. For example, given a desired relative accuracy r_j of the velocity calculation, with encoder measurements by the formula below taken from [1] , it is possible to derive the required amount of time for obtaining a velocity measurement. This is assuming that the velocity is not calculated with two consecutive samples, but with two samples separated a certain number of backwards steps s_j in order to increase the velocity resolution. For an incremental encoder with a resolution R, if the position q(t) is sampled with a sampling period T, and for k = 1, 2, …, the discrete sampled position at
time
kT is given by θ(k). The relative accuracy is given by:

Capture

Where v_j is the real velocity and \hat{v_j} is the estimated with the measurements. For example, in order to obtain a relative accuracy of r_j= 2%, s_j = 100, i.e. 100 past pulses have to be traced back on the velocity calculation. If we want to achieve this with an encoder of 10.000 lines/rev, the elapsed angular space for 100 pulses would result to be: 3.6 °. With a motor running at 1 rpm, the required amount of time for completing that angular slot is 10 ms. This amount of delay is detrimental for a good bilateral performance.

 

[1] G. Liu. “On velocity estimation using position measurements”. In: Proceedings
of the american control conference,
Anchorage, AK May 8-10. 2002, vol. 2, pp. 1115-
1120.

[2] A. Jaritz, M.W. Spong. “An experimental comparison of robust control algorithms on a direct drive manipulator”. In: IEEE Transactions on Control Systems
Technology. 1996, vol.4, no.6, pp.627-640. doi: 10.1109/87.541692

[3]  F. Janabi-Sharifi, V. Hayward, C-S.J. Chen. “Discrete-time adaptive windowing for velocity estimation”. In: IEEE Transactions on Control Systems Technology.2000, vol.8, no.6, pp.1003-1009. doi: 10.1109/87.880606

[4] P.R. Belanger, P.Dobrovolny, A. Helmy, and X. Zhang. “Estimation of angular
velocity and acceleration from shaft-encoder measurements.”
The International Journal
of Robotics Research.
1998, vol. 17, no. 11, pp. 1225-1233

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Posted in Actuators, Robotic devices, Robotics

Motor Selection for Robots (I)

In this post I am going to review the main parameters affecting the selection of the correct motor and gears for your application.

Motors are a very common component in many devices and embedded systems.  To function properly, their selection requires a careful step by step process that relies heavily on the intended operation of the motor. Therefore, before motor selection can begin, it is beneficial to define what the motor will have to do, the performance goals of the motor and overall system (i.e. how will you measure that it’s doing well), and how the motor will interact with the other system components (such as the power system).  Understanding these parameters will help the selection process by keeping the focus on what your system must achieve, and in turn can help you to better define motor technical requirements.

This post and the following will use a wheeled robot in order to illustrate the process to be followed on the determination of a motor.

Motor selection for robots

The most important steps when selecting a motor for a certain application are:

  1. Determine key performance goals of the system
  2. Transform the goals into torque and rotational speed requirements for the motor
    1. Speed
    2. Torque
    3. Motor connection interactions (i.e. what is the motor connected to and how does that influence its performance)
    4. Speed-Torque Curve
    5. Mechanical Power
    6. Constant Voltage Torque-Speed Line
  3. Utilize gearing systems if the operating point speed and torque do not match the motor speed and torque
  4. Relate these mechanical requirements into electrical power system requirements, including the potential of motor overheating
  5. Add sensors, such as encoders, based on the information needs of other systems
  6. Review additional requirements such as cost, time, environmental, serviceability and mounting requirements
  7. Deal with the reality that there is rarely a motor that exactly matches the calculated requirements and make proper trade-offs
    1. Review all requirements
    2. Determine rating system
    3. Generate a selection of motors
    4. Compare all options and select a motor

In the next sections, these steps will be review and explained with examples of application.

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