The theory-practice gap has existed for decades and each of us needs to bridge this gap in all we do. As control is an essential element in all multidisciplinary systems, let's start there, and begin to bridge the gap that exists between the theory of control and its digital implementation. Top 10 lists are always popular, so here is one for classical control.
Control theory for the practitioner:
Feedback control is a pervasive, powerful, enabling technology that, at first sight, looks simple and straightforward, but is amazingly subtle and intricate in both theory and practice.
In a dynamic system, changes cannot be effected instantaneously, and so an otherwise correct control decision applied at the wrong time could result in catastrophe.
Nonlinearities are always present, e.g., backlash, Coulomb fricton, saturation, hysteresis, quantization, dead band, and kinematic nonlinearities. A linearized model can be used to approximate a nonlinear system near an operating point.
Stability of a dynamic system must be guaranteed. Closed-loop systems go unstable because of an imbalance between strength of corrective action and system dynamic lags. Stable systems must have adequate stability margins to work once built.
Stable systems have a frequency response. If a stable linear system has a sinusoidal input applied, then the steady-state output will be a sinusoid of the same frequency, however, the amplitude ratio and phase difference of the two sinusoids are frequency-dependent.
The open-loop transfer function is the product of all the transfer functions in the loop, e.g., controller, actuator, plant, and sensor. Compared to the closed-loop system transfer function, the open-loop transfer function is much less complex. The Nyquist criterion and the Root Locus procedure allow one to use the open-loop transfer function to predict closed-loop system performance.
After stability, performance is everything. Command following, disturbance rejection, insensitivity to modeling errors, and insensitivity to unmodeled high-frequency dynamics and noise are the main reasons for using feedback control, once a system is guaranteed to be closed-loop stable.
Time delays can be deadly. Always conserve phase, the equivalent of time delay. Integral control adds 90 degrees of phase lag at every frequency and digital control adds time delay primarily due to D/A conversion. Imagine trying to make decisions using old information.
High control gain has lots of benefits, e.g., good command tracking and good disturbance rejection. However, there are three areas of concern: roll-off, saturation, and noise.
People's lives may be at stake. There are no "details" in control engineering, as even the most insignificant detail may prove to be important. Real control systems must be extremely reliable, especially if people's lives depend on them.
Using Siemens NX software, a team of engineering students from the University of Michigan built an electric vehicle and raced in the 2013 Bridgestone World Solar Challenge. One of those students blogged for Design News throughout the race.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.