Signal conditioning basics using op-amps

In most real-world control applications, we need to use sensors to transform physical variables (heat, pressure, motor shaft position, etc.) to electrical variables (voltages or currents). Only then the signals could be sampled and processed by a computer. As it normally happens, the signal output from a sensor might not be clean enough to accurately represent the quantity it measures. Very likely that it would be contaminated by high-frequency noise, or undesired disturbance with fixed frequency (interference from 50 Hz household appliance, for example). At the other analog-digital junction, the direct signal from DAC module might need to be smoothen by a low-pass filter. For a low-cost application, a passive circuit may be used for signal conditioning purpose. The drawback is lack of impedance buffering and signal amplification. If your sensor or DAC has a limited sourcing capability, an active circuit using operational amplifier (op-amps) is a better choice.

This brief technical article summarizes the use of op-amps in certain signal conditioning circuits. We only provide basic derivations, circuit examples, and simulation results, leaving detailed analysis to standard textbooks.

Signal Conditioning Basics using Op-amps (.PDF)

System Modeling 101

In any model-based control design scheme, it is essential to first obtain a math model of the physical system, or the plant we want to control. The design then exploits that information in certain way to craft a controller for that particular plant, subjecting to user specifications or design constraints. So it is rational to say, regardless of any sophisticated design paradigm or software tool used, that a controller will be as good as the model that represents the actual plant. 

There are 2 basic approaches to achieve a math model of a physical system. The first relies on theory. One can start from physics; i.e., form an equation and substitute parameters, either from datasheet or measurement. The second approach uses data captured from the real plant and tries to identify the math model from such information. The latter is generally classified as system identification.

This article introduces some common modeling and identification methods, with emphasis on practical issues. Examples from our past research are provided.

System Modeling 101 (.PDF)