Developing a control system rapidly and cost effectively requires a disciplined approach that exposes design limitations early so they can be corrected before the costs and schedule go out of control. A trial-and-error development approach is utilized primarily when the control system is presumed to be simple or when there is insufficient time to conduct a full development cycle. This approach is characterized by a relatively small investment in up-front planning, followed by multiple iterations of build and test.
Rapid control system development relies first on obtaining a good system model of the process to be controlled, followed by, or in parallel with, controller algorithm development in a graphically oriented, interpretive software environment. The algorithm is then tested against the model and iterated upon as necessary before testing and applying it to the physical process and committing to controller hardware.
Development of the controller begins with understanding the system to be controlled, and to do so, a reliable model of it is created that is independent of any controller that may be chosen. To create a good system model, is important to obtain real, physical, system-specific data that fully describes the system to be controlled. Using only theoretical data at this stage often can lead to mischaracterizing the system and defeating the control development process. Extraordinary system behavior must be noted, as it often is an indicator of some greater complexity that affects control.
This process requires a good system model — one that is appropriate to the system to be controlled, and one that can be reduced to mathematical and/or logical description of how the output(s) respond to input(s) for all relevant environmental/ boundary conditions. For typical thermal systems, there are essentially two basic models to select from: a transfer system model and a finite element model. A system could be described with one or more transfer functions relating a linear or non-linear relationship between input and output through discrete (lumpedparameter) components. Alternatively, for a more distributed or continuous system where the concern is propagation, finite element modeling may be the best choice for a system model.
A transfer system model works well with linear systems that have discrete components or whose components can be represented as discrete parts. Transfer system models can be multidimensional, but due to interactions or interdependencies between the parts, the inputs and/or the outputs can complicate the system model very quickly.
A hot-water/cold-water temperature regulating system is an example of one that could be modeled with transfer functions. Two inputs consisting of cold water and hot water are controlled by valves to produce an output flow stream at a certain temperature and flow rate. In this system, changing either or both of the inputs affects both output variables (temperature and flow rate). One might assume for this control that the output flow rate and temperature is the linear superposition of what the cold-water valve can produce and what the hot-water valve can produce. Testing the system, on the other hand, may reveal that there also is a dependency upon both the hot- and coldwater supply pressure and that this pressure may fluctuate with time as other demands on the water supply dynamically change the supply pressures and affect output. For instance, a large nearby coldwater demand — like a toilet flushing — may require a different set of valve positions to maintain the desired output temperature and flow rate.
To adequately model this system and move to the next stage of controls development requires that all the parameters that affect output be accounted for and tested where possible. Thus, the variables of pressure, temperature, and valve position need to be examined to create a mathematical relationship between inputs and outputs that considers responsiveness, stability, and linearity (valves stick, and inlet water pressure and temperature vary). To adequately control a system, the controller must be faster than the parts and processes within the system, or it must have foreknowledge of what is to happen, so establishing the inherent bandwidth of the system to be controlled is essential.
When looking for temperature uniformity/ distribution of a sample or structure, the finite element model may be easier to create than a transfer function model. A finite element system model is a good candidate for distributed systems where energy propagates or stresses distribute through continuous material or materials. Almost always, there are boundary conditions or structural variations that influence propagation or distribution. In thermal systems, that means heat flow and temperature distribution in structures. More often than not, mechanical stresses and strains accompany temperature differentials that, in turn, may distort the structure and change how heat flows.
Upon deciding the system model or models to use, the next step is to create a basic model of the process to be controlled, and then experimentally verify that the model is a good representation of the physical process. Next, one must determine how the real system differs from the theoretical — especially during special conditions or situations. Nearly all real systems have some non-linear or non-stationary behavior, and nearly all of them have noise contamination. Here are some key considerations:
- Time delay. One of the largest factors in the ability to control a process is ensuring that the required information is presented to the controller in sufficient time for it to compute and execute a response. Knowing the time delays in a system and where they come from is essential to control.
- Non-linear behavior. Most systems exhibit some non-linear behavior, and usually if the non-linearity is small, it can be ignored. However, when non-linear behavior composes perhaps 10% or more deviation from linear, it probably needs to be seriously considered. Continuous non-linear behavior is, in many respects, easier to characterize and compensate for than discontinuous or eventful behavior.
- Noise contamination. Noise and signal distortion can be particularly troublesome to contend with in a control system, so it is important to find the sources of noise and other kinds of contamination before attempting to introduce control to a system. Noise can come from a lack of sensor signal sensitivity or from too much sensitivity as in sensor saturation. It can be external to the system, entering from uncorrelated and unaccounted for electrical/ electromagnetic, mechanical, or thermal sources including problems with shielding and grounding.
This article was written by Cal Swanson, Senior Principal Engineer, at Single Iteration, a division of Watlow Electric Manufacturing, Fenton, MO. For more information, click here .