Motion-control designers are consistently confronted with increasing demands for higher performance. Requirements for greater efficiency and lower costs provide the motivation. And those lead to systems with higher throughput thanks to higher accelerations and velocities, with shorter settling times.

Acceleration and velocity are restricted by the voltage and current limitations of motors and drives. However, an aggressive motion profile does not necessarily yield higher throughput. In many cases, the practical acceleration and velocity are reduced well below maximum achievable values because the profile duration, or the commanded move time, is not important by itself. The important factor is the total sum of the move time including settling time. This last figure is determined by various dynamic effects and can significantly increase in case of an aggressive profile.

A problem can manifest itself when higher resolution is required and the settling window is narrowed. It should be noted that the resolution of instruments in the medical industry is approaching 1 nm. Instruments with this type of resolution are inevitably sensitive to even the smallest vibration or disturbance. Selecting appropriate drives, motors, and mechanics can contribute to the reduction in vibration and equate to a faster settling time.

High-performance motion-control applications commonly use three-phase permanent magnet synchronous motors, widely known as brushless motors. These are capable of higher speeds ? more than 10,000 rpm, compared to brush motors with speeds of 1,000 to 2,500 rpm. Brushless motors are also smaller, lighter, create less noise, and are more efficient than brush motors with comparable outputs.

Brushless motors are typically operated by amplifiers that deliver sinusoidal currents to produce a relatively smooth motion. Yet, brushless-motor shafts do not rotate smoothly. They experience a number of periodic disturbances from imperfections in motor structure and non-ideal phase commutation. These disturbances create a certain amount of speed fluctuation, vibration, acoustic noise, and an excitation of lightly damped electrical resonances. Though small, the disturbances can introduce critical errors in applications that need smooth, accurate motion.

Cogging is the most common source of periodic disturbances and is caused by the slots in the stator, combined with a non-uniform air gap. As the rotor turns, the magnets are more attracted to the stator's teeth than the gaps between them. The permanent-magnet flux in the rotor creates a cogging torque as it seeks a path of minimum reluctance. The effect exists even when the drive is disabled. The level of the disturbance depends on the design of the motor structure.

One way to eliminate the disturbances is called feed-forward compensation. A selected number of sinusoidal signals are added to the drive output as feed-forward commands. Each command is based on an integer multiple of the mechanical angle and has a constant amplitude. The latter assumption is not necessarily correct, but it is reasonable for many of the common disturbances.

The amplitude, phase, and order of the major harmonic are found by a simple experiment in which the motor turns at a relatively low speed. It is assumed that the frequency of the major disturbances is low and well within the velocity-loop bandwidth.

The order, amplitude, and phase of the major torque harmonics are identified by analyzing the velocity-loop output. Although the feed-forward method reduces torque ripple, it has several disadvantages. It is complicated and tedious to manually set its parameters while handling only a limited number of harmonic disturbances. The technique does not consider disturbances that vary as a function of current amplitude. Nor does it take into account the disturbance variance and time dependency.

Another solution, an adaptive cogging-compensation algorithm, can minimize the clogging disturbance. It more effectively compensates for multiple disturbance harmonics. The controller identifies major disturbance components by an iterative learning process that runs while homing the system. The controller then injects a compensation signal into the command for current.

The adaptive algorithm compensates for time and command dependencies of the disturbances. The compensation is also active when the motor is in open-loop mode.

This algorithm controls brushless motors in applications requiring smooth, precise motion at low and high speeds, such as medical imaging equipment, cell imaging, and DNA sequencing.

The user can feel the effectiveness of the algorithm by rotating the motor shaft by hand. The shaft turns smoothly without noticeable cogging.