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Development of Sensorless PM Servo-System for
Harsh Environments
B.Allotta, L.Pugi, L.Paolucci, P. D’Adamio
L.Nocentini
DIEF Industrial Engineering Department
University of Florence
Florence, Italy
M.Niehaus. E. Grasso, E. Kanapari
Laboratory of Actuation Technology
Saarland University
Saarbrücken, Germany
Abstract—Aim of this work is the development of efficient and
robust filters for speed and position controls to be operated in
harsh environments and in particular for underwater
applications. PM motors are the most commonly adopted solutions
for this kind of applications where the reliability of the component
has important consequences both for the safety of the vehicle and
the sustainability of the application. Common applications are the
inspections of valuable underwater sites or the maintenance of
potentially dangerous industrial facilities such as submerged
plants of Oil&Gas industry. In particular in this work authors
have concentrated their attention on the part concerning robust
and efficient speed and position estimators for sensorless drive
systems.
Keywords—Mechatronics;Drive and Actuation Tecnology;
Harsh Environment, Underwater Robotics
I. INTRODUCTION
AUVs (Autonomous Underwater Vehicles) and ROVs (Remotely Operated Vehicles) are quite interesting applications both from an industrial and a research point of view. An example of ROV and AUV vehicle is represented in Fig. 1
Fig. 1. Example of AUV [1], [2] (left), ROV [3] (right) and tracked ROV for
seabed/bottom operations[4]
In particular they can be used for scouting and inspection of valuable underwater sites where the intrinsic value of the application should be scientific (geological or bio-marine studies) [5], cultural (archeologic sites) [1] or industrial (maintenance and construction of underwater plants)[6].
Most of these vehicles are electrically or hydraulically operated. Electrical actuators typically offer greater efficiency and flexibility of use but still suffers of reliability troubles especially when operating in deep water and harsh environmental working conditions.
Most of the electrical actuator currently used for underwater applications are PM servo-motor originally developed for standard industrial applications. Performed tasks should be classified in two main sub categories:
Speed Controlled Motors for Propulsion;
Position Controlled Motors for Maneuvering (control of rudders and other navigation surfaces), Manipulation (control of robotic arms or other tools), Data acquisition (motion control of samplers and other motorized sensors), and traction of tracked/wheeled vehicles.
For an operating depth over 100 meters, this kind of motors have to be protected against external pressure and the contamination of the surrounding water using the technique of fluid compensation, which is illustrated in the scheme of figure 2. The motor case is filled with an inert fluid such as an halogenated hydrocarbon (as example Fluorinert™), a dielectric oil (silicon or carbon based) or a bio-compatible organic oil in order to preserve the surrounding environment from any contamination.
Fig. 2. Typical layout of an oil filled motor
Conventional sensored controls of PM motors involve the usage of Hall sensors or other kind of sensors for position feedback. Prolonged testing activities performed both on vehicles and on the testing facilities of the University of Florence[7], have shown that Hall sensors are one of the main
concern with respect to this sealing system for the following reasons:
Additional wiring required: hall sensor signals need to be wired until the motor controller, which typically is not in the same place of the motor; this represents not only an additional cost, but also an additional source of failures.
Aging of fluid: the fluid used to fill the motor is subjected to the high temperature of windings (typically rated for 125C°), to contamination of worn metal particles from gearbox and of small percentage of water. Aging of the fluid should produce segregated solid or liquid phases, which negatively affect the performances of the hall feedback sensors.
This work, deals with the development of sensorless drive system. In particular, authors concentrated their attention one of the most critical part of the sensorless control algorithm which is represented by the position-speed estimation filter that have to be implemented to estimate the rotor position. This to perform an optimal commutation of currents on windings. A control scheme is shown in Fig. 3, which is referred to [10] .
The most common techniques for sensorless control of PMSM were based on the observation of the back electromotive force (Back-EMF) [8],[9],[10],[11]. As the EMF is proportional to rotor speed, such techniques typically perform well when the motor spins at speed over 10-20% of the nominal one. Indeed at lover speed EMF waveform amplitude is null (standstill) or badly scaled with respect to noise. As a consequence, a feed forward startup of the motor is required. For applications to naval propulsion, this problem is typically mitigated by the fact that the standstill torques of the moved propeller loads are typically null or negligible.
Fig. 3. Layout of a sensorless control with a sliding mode estimator[10]
Techniques that exploit the magnetic anisotropies of the machine are capable to operate also at rotor standstill and at very low speeds [11]. Such techniques are based on injecting signals that excite the machine at frequencies that are different from the operating frequency of the machine and with negligible influences on the mechanical behavior of the motor. Among these techniques, it has been developed a method which works either at low speed and standstill and is called INFORM [12].This technique calculates machine inductances by measuring the currents and their transients as well as the voltages induced on the windings.
Nevertheless, such methods require accurate current measurements and therefore precise and reliable current sensors,
which typically introduce noise and are characterized by a relatively small bandwidth.
For PMSM with stator star connected windings it’s also possible to perform a patented technique [13] here called DFC (acronyms of Direct flux control) which is able to directly estimate motor flux linkages by measuring the star center voltage variations that are associated to specific PWM driving pattern as visible in the scheme of Fig. 4.
Fig. 4. Principle of operation of the DFC method (electric connections and
mesurements, on the left, associated PWM patterns)
In this work, it is proposed a comparison of the preliminary results obtained by the two cooperating universities (Saarland University, Germany and University of Florence, Italy) concerning the development of sensorless control for PM drive systems. In particular the work is divided in two parts:
Improvement of smart Back EMF estimators
Evaluation, implementation and further development of the patented technology of DFC [13]
II. IMPROVEMENT OF SMART BACK EMF ESTIMATORS
This activity represent a further improvement of estimators based on the principle of Back EMF estimation, like the ones described in [8]-[11]: different standard Back-EMF, sensorless filters and estimators are first simulated using Matlab-Simulik-Simscape™ and then tested on a real motor control; this in order to compare and optimize the different techniques.
The typical mathematical model of a PMSM is described, in
two-axis α,β with stationary reference (a,b), as follows:
𝑑𝐼𝛼
𝑑𝑥= −
𝑅
𝐿𝐼𝛼 −
1
𝐿𝑒𝛼 +
1
𝐿𝑉𝛼 ,
(1)
𝑑𝐼𝛽
𝑑𝑥= −
𝑅
𝐿𝐼𝛽 −
1
𝐿𝑒𝛽 +
1
𝐿𝑉𝛽 ,
(2)
𝑒𝛼 = −𝜆0𝜔𝑒 sin 𝜃𝑒 ,
(3)
𝑒𝛽 = 𝜆0𝜔𝑒 sin 𝜃𝑒 ,
(4)
where vα, vβ are the α and β axis voltages; iα, iβ, are the α
and β axis currents, R is the phase winding resistance; L is the
inductance; ωe is the rotating speed of magnet flux; λ0 is the
permanent magnet flux linkage.
The estimator can be described as follows:
𝑑𝐼�̂�
𝑑𝑥= −
𝑅
𝐿𝐼�̂� −
1
𝐿𝑒𝛼 +
𝑙1
𝐿𝑠𝑖𝑔𝑛(𝐼�̅�)
(5)
𝑑𝐼�̂�
𝑑𝑥= −
𝑅
𝐿𝐼�̂� −
1
𝐿𝑒𝛽 +
𝑙1
𝐿𝑠𝑖𝑔𝑛(𝐼�̅�)
(6)
Where Iα̂ and Iβ̂ are estimated currents; l1 is a constant
observer gain; 𝐼�̅�and 𝐼�̅� are the observer mismatches (𝐼�̅� = Iα̂ −
𝐼𝛼 , 𝐼�̅� = Iβ̂ − 𝐼𝛽).
Equations (5) and (6) can be expressed with the following flow diagram (Fig. 5):
Fig. 5. Flow diagram of the estimator
This estimator is able to gather the rotor speed and position through Back EMF estimation. For this reason, the first part of the estimator generates an esteem of the Back EMF. The second part of the estimator, which is the Phase Locked Loop, estimates the rotor speed and position through Back EMF esteem previously generated. The block diagram of the PLL is shown in Fig. 6. From Fig. 6 it we have:
∆𝑒 = �̂�𝛼 cos �̂� − �̂�𝛽 sin �̂�
= 𝜆0𝜔𝑒 𝑠𝑖𝑛 �̂�𝑒 𝑐𝑜𝑠 �̂� − 𝜆0𝜔𝑒 𝑠𝑖𝑛 �̂�𝑒 𝑠𝑖𝑛 �̂�
= 𝜆0𝜔𝑒 sin(�̂�𝑒 − �̂�)
(7)
Fig. 6. Flow diagram of the PLL
Fig. 7. Matlab-Simulink™ model of the Back EMF estimator
Fig. 8. Matlab-Simulink™ model of the PLL for speed and position
estimation
Once implemented estimator model in Matlab-Simulink™ the estimator operation has been simulated by connecting it to a motor Simulink model. Later it has been possible to check if the model was working properly by acquiring voltage and current waveforms from a real motor. By varying rotor speed and acquiring voltage and current waveforms it has been possible to test the estimator either online and offline by saving a dataset of the measured waveforms. This way all the implemented filters are compared with respect to the same coherent experimental data. Tested motor and propellers are the same adpted for MARTA AUV [7] (Maxon model 386676 )
In particular in order to test different estimators the experimental layout described in Fig. 9 is used: a motor is controlled using a C-2000 microcontroller in which it has been implemented the algorithm described in the scheme of Fig. 3 [10].
Fig. 9. Testing layout for data acquisition and filter testing using dSPACE platform.
Fig. 10. Testing layout for data acquisition using CompactRIO platform.
Two acquisition platform have been tested: the dSPACE platform (Fig. 9) and the CompactRIO platform (Fig. 10).
The adopted dSPACE platform was a Micro-Autobox controller with a Power-PC Microprocessor running at 900MHz.
Using this platform it was possible to implement the filter with a sampling frequency of about 25KHz. In this configuration the filter directly implemented from Matlab-Simulink Code performed very well confirming the robustness of the adopted approach respect to bandwidth limitations introduced by the low sampling rate. However the Dspace Micro-Autobox controller was unsuited to manage the acquisition and the storage of voltage and current measurements performed at high sampling rates. So the Dspace environment was used only to preliminary verify the functionality of the implemented filter
CompactRIO platform instead was equipped with an FPGA, allowing a very fast and robust acquisition of current and voltages measurements that can be used for further calibration of the estimation filter. For this reason, it has been acquired a dataset of the voltage and current waveforms through the CompactRIO platform with the motor spinning at different speed. In (Fig. 12) it is shown the voltave waveform and current waveform (Fig. 11) obtained by using CompactRIO at a high frequency acquisition and storing rate.
Fig. 11. Current waveform acquisition on the three phases ()
Fig. 12. Voltage waveform acqiosition on the three phases
With the resulting dataset, it has been possible to operate the estimator offline by reading these values with the dSPACE platform. This way it is possible to operate estimator tuning and compare different solutions in homogeneous conditions.
In Fig. 13 it is shown the average error between reference speed and estimated speed obtained by processing the dataset described above with the estimator previously described. In Table 1 are listed the values of mean error and error variance by varying reference speed. The table summarize the reliability of the estimator over a certain range of speed.
Fig. 13. Average error between refenence speed and estimated speed.
Looking at results of Table I, it’s interesting that the mean error between reference speed and measured speed is very low and almost constant: this fact can be easily explained considering that also the real speed of the motor is a bit lower respect to the reference due to steady state errors of the drive speed loop. This aspect has been preliminary investigated by comparing the spectral composition of measured currents with
the estimated speed. A future development of the testing device should be the installation of an high resolution tachometers for a precise measurement of the real mechanical speed of the motor.
It’s also interesting the good response of the estimation filter during motor transitions respect to different reference speed as visible in figure 13.
Sliding Mode Estimator
RPM
Estimated
Speed Mean Error
Mean Error
% Variance
2000
2003,30 -3,30 -
0,17% 348,56
4000
4006,94 -6,94 -
0,17% 579,16
6000
6010,51 -10,51 -
0,18% 705,57
8000
8013,19 -13,19 -
0,16% 751,61
10000
10020,13 -20,13 -
0,20% 748,30
12000
12021,12 -21,12 -
0,18% 681,48 Table 1: performance of the tested filters in terms of errors respect to
drive reference speed
Authors experienced different speed filters and estimators
under different loading conditions. It is interesting to notice that
the most interesting results have been obtained by using a filter,
which implement both PLL filtering of signals [8] and gain
scheduling of parameters respect to known variable of interest
such as motor reference speed and motor estimated speed error.
This particular feature that was introduced by authors in this
work and that should better explained in the full length
publication. It drastically optimize the performance of the filter
respect both to motor and to load features as visible in the
preliminary experimental results. The solution proposed needs to be implemented in an
embedded system, in order to integrate it into the desired system. For this reason, the last step is the testing of the estimator in an embedded system such as PIC32 microcontrollers. The microcontroller used is the PIC32MZ that operates at a sufficient clock frequency and it is provided with the necessary peripherals such as ADC converters for signal acquisition. To achieve it, it has been necessary to re write the estimator algorithm in C so that a microcontroller could execute it. Also in this case, it will be necessary to acquire current and voltage waveforms from the drive system and processing it in real time.
Fig. 14. Estimator testing layout with a PIC microcontroller
Experimental data of the estimator running on an embedded
system as shown in Fig. 14 are still not available because the
system has been designed and assembled but programming and
testing activities are still not completed. Once the estimator will
run properly on the PIC microcontroller, the next step will be
the integration of it into a complete control algorithm.
III. DFC SPEED POSITION/SPEED ESTIMATOR
In order to test the DFC estimator in a very simple control loop, the testing layout described in Fig. 15 was implemented using a Microchip microcontroller, the PIC32MZ: position of flux linkages is used to estimate the motor electrical angle needed to control the commutations performed by the inverter. Since the method implies the usage of a motor with star connected windings, the motor used for the testing was internally assembled in the laboratories of the Saarland University using commercial spare parts.
The adoption of the proposed position estimation method, which implies the construction of PM motors with star connected windings, star center connection and a devoted drive system, should be justified only to achieve superior performances in terms of robustness of the position evaluation especially at standstill conditions.
Preliminary tests gave interesting results. In particular in some experimental data concerning estimated flux linkages and positions during locked rotor tests. The test is performed in the following way: while the rotor is running, the rotor is blocked causing a sudden stop/locking of the machine; after some seconds of locking, the braking load is removed and the machine returns to its normal working speed. As clearly visible in, Fig. 16 the system proves a very smooth and robust behavior when dealing with sudden speed variation.
Fig. 15. Testing Layout of the DFC-position estimator which is used to regulate the commutation of the currents on stator windings (top) and the corresponding star neutral voltages (due to PWM patterns) measured during motor operations (bottom)
Fig. 16. Exp. Results from locked rotor tests, calculated values for both flux linkages (top) and motor angular position (bottom)
CONCLUSIONS AND FUTURE DEVELOPMENTS
According experimental results obtained, the DFC method seems to be a quite interesting technology that should be applied
to perform sensorless position and speed control. Its further diffusion and development is mainly constrained by the need of a star connected motor with a wired, measurable neutral point. This is a kind of PM motor which need a custom design for this application with obvious consequences in terms of cost and availability that could be compensate only with a massive industrial production. On the other hand, experiences performed with speed estimation techniques, clearly shows that the performances in terms of precision and minimum working speed of this kind of methods can be drastically improved by adopting more sophisticated estimation techniques like the ones illustrated in the preliminary examples of this work.
REFERENCES
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