NASA Tech Briefs describes proportional integral derivative (PID) controllers as "the modern industrial workhorse" because of their distinct role in automating the regulation tasks of today's advanced process control systems. PID controllers are well-suited for many speed, temperature, flow and pressure processes. In industrial and commercial settings where these elements are sometimes matched with processes that are nonlinear, that exhibit high inertia or that require multiple complex calculations, PID control can add significant value, automating many operations that would otherwise need to be performed manually.
Yet, for many hydronics and heating, ventilating and air-conditioning (HVAC) applications that use induction motors to power pumps and fans, the torque needed to drive the motor loads varies based on the shaft speed of the motor, which can be controlled by a variable frequency drive (VFD).
Leveraging advanced VFD algorithms for PID-controlled applications in industrial and commercial building settings is a unique approach that can improve the longevity of electric motors and reduce energy consumption for systems.
Tuning into Control Modes
PID controllers regulate output through the sum of their proportional, integral and derivative actions. Most control loops require proportional and integral methods, while motion control responds best to derivative methods. Temperature regulation often requires a mix of all three. The following descriptions highlight the differences between each type of control.
Proportional control simply matches output action to error proportionally.
In integral control, the controller reduces errors through incremental or decremental outputs that gradually improve or refine the overall process. The speed with which this occurs can be fast or slow depending on whether the error is large or small, respectively. But the integral timing must be precise to avoid problems in the loop, such as sluggishness or instability.
Derivative control provides faster, more responsive output action, but it is also highly sensitive to measurement noise. For this reason, it is used more selectively. Derivative controllers measure an adjustable time setting in minutes and seconds and derive the amount of output action to be produced based on that measurement. In other words, the controller reduces errors by calculating outputs against an observed rate of change. As the rate of change increases, more or less control is exerted. Instability can result if the derivative time is set too long.
Timing and tuning are paramount to achieving both optimal efficiency and high performance with PID controllers. This is why VFDs can often provide important support for process controls.
In the example of pumping and HVAC applications, the relationship between the shaft speed and the other key parameters for a pump or fan is expressed in the affinity laws: Flow is proportional to shaft speed, head/pressure is proportional to the square of the shaft speed, and power is proportional to the cube of the shaft speed. Based on these affinity laws, the torque needed to power a fan or pump at a lower reference frequency is lower than that required for a higher reference frequency.
The main benefit of reducing the input voltage to an induction motor is the consequent reduction in motor core losses, which are typically proportional to the square of the input voltage. Applying unnecessarily high voltage to the motor generates excessive motor core losses in the form of heat and noise.
In fact, noise and variability are the primary challenges for PID controllers. If the controller creates volatility in the process, efficiency will suffer and unnecessary wear and tear is likely to occur.
Most PID controllers require tuning to time the speed of the output action properly. Some tuning rules, such as Cohen-Coon, work well on almost all control loops that require a rapid response. But even with this approach, additional methods are still needed to address concurrent impacts on oscillation. In addition, tuning rules do not always work well with self-regulating processes that stabilize at a point of equilibrium.
To meet the specific objectives for each aspect of PID control, hundreds of controller-tuning methods have evolved. Two examples of PID-control designs include noninteractive and parallel algorithms.
Active Energy Control
One specialized energy control algorithm optimizes energy usage to produce 2 to 10 percent energy savings over standard drives.
A standard drive controls a motor based on a linear volts-per-hertz (V/Hz) relationship. The output voltage waveform of a standard VFD is typically based on pulse-width modulation (PWM). The amplitude, duty cycle and periodicity of the PWM waveform decide the effective voltage and frequency of the VFD output.