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Tightly Integrated Devices Yield More Efficient Motor Control
Truly efficient motor control requires a number of complex digital and analog operations. Now
the emergence of fast, highly integrated processing engines enables such control to operate with
the needed speed and accuracy.
by Yvonne Lin, Actel
Motor controls are comprised of the power electronics that control the application of voltage and
current to the motor. Digital motor controllers manage the power electronics to achieve the target
motion results such as speed or torque at a system level. The control algorithms implemented
within the motor controller significantly influence the overall motor system efficiency.
The most basic way to control a motor is by controlling voltage and current supplied. The most
simple and primitive motor drivers use a linear amplifier that regulates the voltage supplied to
the motor in a linear fashion. For example, if it is desired to deliver 10 amperes of current across
a motor at zero speed that has only 1 ohm of phase-to-phase resistance, then only 10 volts is
needed from the motor controller. If the linear motor controller is supplied with 50 volts, for
example, then 40 volts multiplied by 10 amperes, or 400 watts is dissipated in the controller.
Very few applications implement linear controllers because they are terribly inefficient.
Pulse Width Modulation (PWM) schemes, on the other hand, depend on motor inductance and
overall reactance to pulse the entire supply voltage across the motor at high speeds with duty
cycles proportional to the average voltage they wish to produce (Figure 1). While PWM
controllers introduce eddy-current losses in the motor, those losses are significantly lower than
the improvement in efficiency gained within the controller using PWM techniques. The PWM
controller has essentially three power states: 1) Off, where the output power electronic switch is
not conducting any current; 2) On, where the output power electronic switch is conducting
current, however, with very little voltage across it (1 to 3 volts); or 3) switching from either off
to on or on to off, where the power electronic switches incur switching losses proportional to
how fast they can switch the current and how fast the voltage falls and rises across them. All of
these states are relatively very low loss states compared to the linear controller.
Applying Motor Control Algorithms
With today’s processing power, it should be clear that digital control is really the only choice.
Today’s microcontrollers are more capable than the digital signal processors of just 10 years ago.
These devices are designed for real-time embedded applications and include peripherals and
mechanisms to help ease the engineer’s job at applying them to control motors.
Unlike AC induction motors, where loss is created due to slip, permanent magnet synchronous
motors (PMSM) are far more efficient, as they are synchronous machines. PMSM fall into
several categories that impact performance, value, speed, motor constant and power density. The
leading motor types are surface magnet (SM), interior permanent magnet (IPM) and axial flux,
which can be either SM or IPM. The SM motor provides a robust platform that is relatively easy
to control.
The IPM motor has the permanent magnets buried within the rotor structure, which is made up of
iron laminations stacked along the rotor. The introduction of the iron laminations adds rotor
inductance that can be utilized to more effectively control the motor and effectively manipulate
its characteristics and parameters. This added measure of control, however, comes with a price of
control complexity when compared to SM motor control algorithms.
The control algorithm of choice for most permanent magnet motors today is the field-oriented
control (FOC) algorithm. This algorithm “componentizes” the id and iq current and voltage
vectors and closes the control loop around them after they have been subjected to coordinate
transforms that reference them to the rotor’s angular position. Permanent magnet motors coupled
with effective control algorithms are the most efficient combinations available today.
Figure 2 shows the block diagram of field-oriented control for a PMSM. Three measurements are
taken as feedback from the motor: phase currents and position of the rotor. There are many ways
of obtaining feedback to calculate position, such as a Hall effect sensor, encoder or sensorless
using back emf. By measuring only two phase currents (ia and ib), the stator current vector,
which is comprised of three components (ia, ib and ic) can be constructed; this is because ia + ib
+ ic = 0. The Clarke transform is used to convert these three current components into orthogonal,
2-axis coordinates, iα and iβ. Based on the position of the rotor, the angle θ can be calculated and
is used to bias iα and iβ within the Park transform to essentially null the effect of rotor angular
change, making the output of the Park transform a DC component relative to shaft angle.
The resultant current vector is mapped into a d-q axis as shown in Figure 2. These values are fed
back into a PI (Proportional + Integral) controller as current error. The output of the PI regulator
is a command for voltage. However, it is referenced to the rotor’s angular position. Before we
can output voltage to the stator, we must now reference the voltage back to the stator, which is
stationary. The Inverse Park transform accomplishes this by doing the inverse of the Park
transform, just as the name suggests. The transform takes the rotor’s angle and offsets the id and
iq voltage vectors into iα and iβ voltage vectors that now include amplitude components for the
rotor’s angle. Finally, a Space Vector Pulse Width Modulator (SVPWM) converts iα and iβ into
three voltage vectors, Va, Vb and Vc.
As shown in Figure 3, an FOC control system is designed to keep id and iq orthogonal, which
helps to ensure optimal angle control and better current utilization in producing torque. In order
to ensure perpendicularity between these vectors, the algorithms must be computed quickly to
minimize computational delays. Delays essentially obsolete the voltage vectors and decrease the
effective efficiency because the ideal 90 degree angle between id and iq is not maintained.
In the past, because of the unavailability of fast controllers and digital devices, implementing
FOC algorithms was only possible through the use of expensive, high-end DSPs or with
assembly language. This constraint and the added cost of sensors have deterred designers from
adoption in many applications that could benefit from the gains in torque management, control
and efficiency. However, with the introduction of many complex and integrated electronic
devices such as MCUs, lower cost DSPs and FPGAs, computational power and cost have come a
long way in providing the power to make these methods available to even simple control
applications. Implementing many of the control structures in hardware description language
(HDL) in tandem with lower cost processors provides an excellent opportunity to segment the
computing tasks into pieces appropriate for the subsystems in today’s SoCs. Figure 4 shows how
this might be implemented.
For example, placing the Clarke, Park, PI controller and SVPWM in HDL would allow an MCU
to pass off these calculations to hardware algorithms that can quickly crunch the numbers and
spew back results, minimizing computational delay and reducing processor costs and
computational requirements. In addition, hardware-based actions that are clock driven provide
faster system-level protection for high-speed fault monitoring and protection schemes, making
products more robust in even the harshest of environments.
Implementing FOC on Intelligent Mixed Signal FPGA
SmartFusion devices integrate the three components essential to implementing motor control on
a single device: an FPGA, ARM Cortex-M3 microcontroller and programmable analog
subsystem. An analog-to-digital converter is used to capture motor feedback, such as phase
current or back EMF voltage, while the Cortex-M3 microcontroller is used to manage various
tasks such as sampling and communications. The FPGA enables implementation of algorithms in
hardware, especially those that are system and time critical. When implementing for motor
efficiency, a user can partition the FOC algorithm between hardware and software. For example,
system-critical components that require instantaneous response, such as critical fault detection
and other supervisory functions, should be placed within the FPGA fabric. Any tasks that are
computationally intensive and iterative, such as PWM generation, should also be placed in the
FPGA fabric. Because these three components work across a high-speed 32-bit bus, off-chip
delays are eliminated, yielding fast and flexible motor control implementation.
The convergence of MCU systems on the same die with a programmable logic fabric provides
system-level benefits that make it possible for engineers to incorporate sophisticated control
techniques. These techniques add significant value to products by making them more energyefficient and more exacting in their control mechanisms and results. Intelligently partitioning
tasks will allow engineers to decide which tasks are computationally better served by faster
programmable logic, passing results back to the MCU for further processing or directing to
output devices. Additionally, the clock-driven logic inherent to programmable logic devices
allows engineers to include robust and fast fault detection logic that ultimately delivers better,
more reliable products to their customers. In the end, it is possible to deliver better control,
higher efficiency and more reliable products to markets hungry for them.
Actel, Mountain View, CA. (650) 318-4200. [www.actel.com].