There are two common classes of control systems, with many variations and combinations:
i.) logic or sequential controls, and feedback or linear controls.
ii.) fuzzy logic, which attempts to combine some of the design simplicity of logic with the utility of linear control.
Some devices or systems are inherently not controllable.
The term “control system” may be applied to the essentially manual controls that allow an operator to, for example, close and open a hydraulic press, where the logic requires that it cannot be moved unless safety guards are in place.
(EXAMPLE FOR MANUAL CONTROL SYSTEM)
An automatic sequential control system may trigger a series of mechanical actuators in the correct sequence to perform a task. For example various electric and pneumatic transducers may fold and glue a cardboard box, fill it with product and then seal it in an automatic packaging machine.
(TC—> TEMPERATURE CONTROLLER)
In the case of linear feedback systems, a control loop, including sensors, control algorithms and actuators, is arranged in such a fashion as to try to regulate a variable at a setpoint or reference value. An example of this may increase the fuel supply to a furnace when a measured temperature drops. PID controllers are common and effective in cases such as this . Control systems that include some sensing of the results they are trying to achieve are making use of feedback and so can, to some extent, adapt to varying circumstances. Open-loop control systems do not directly make use of feedback, but run only in pre-arranged ways.
(LINEAR FEEDBACK CONTROL SYSTEM)
Pure logic controls were historically implemented by electricians with networks of relays, and designed with a notation called ladder logic. Nowadays, most such systems are constructed with programmable logic controllers.
Logic controllers may respond to switches, light sensors, pressure switches etc and cause the machinery to perform some operation. Logic systems are used to sequence mechanical operations in many applications. Examples include elevators, washing machines and other systems with interrelated stop-go operations.
Logic systems are quite easy to design, and can handle very complex operations. Some aspects of logic system design make use of Boolean logic.
For example, a thermostat is a simple negative-feedback control: when the temperature (the ‘measured variable’ or MV) goes below a set point (SP), the heater is switched on. Another example could be a pressure-switch on an air compressor: when the pressure (MV) drops below the threshold (SP), the pump is powered. Refrigerators and vacuum pumps contain similar mechanisms operating in reverse, but still providing negative feedback to correct errors.
Simple on-off feedback control systems like these are cheap and effective. In some cases, like the simple compressor example, they may represent a good design choice.
(ON-OFF CONTROL SYSTEM)
In most applications of on-off feedback control, some consideration needs to be given to other costs, such as wear and tear of control valves and maybe other start-up costs when power is reapplied each time the MV drops. Therefore, practical on-off control systems are designed to include hysteresis, usually in the form of a deadband, a region around the setpoint value in which no control action occurs. The width of deadband may be adjustable or programmable.
Linear controls use negative feedback to keep some desired process within an acceptable operating range.
When controlling the temperature of an industrial furnace, it is usually better to control the opening of the fuel valve in proportion to the current needs of the furnace. This helps avoid thermal shocks and applies heat more effectively.
Proportional negative-feedback systems are based on the difference between the required (SP) and measured (MV) value of the controlled variable. This difference is called the error. Power is applied in direct proportion to the current measured error, in the correct sense so as to tend to reduce the error (and so avoid positive feedback). The amount of corrective action that is applied for a given error is set by the gain or sensitivity of the control system.
At low gains, only a small corrective action is applied when errors are detected: the system may be safe and stable, but may be sluggish in response to changing conditions; errors will remain uncorrected for relatively long periods of time: it is over-damped. If the proportional gain is increased, such systems become more responsive and errors are dealt with more quickly. There is an optimal value for the gain setting when the overall system is said to be critically damped. Increases in loop gain beyond this point will lead to oscillations in the MV; such a system is under-damped.
In the furnace example, suppose the temperature is increasing towards a set point at which, say, 50% of the available power will be required for steady-state. At low temperatures, 100% of available power is applied. When the MV is within, say 10° of the SP the heat input begins to be reduced by the proportional controller. (Note that this implies a 20° ‘proportional band’ (PB) from full to no power input, evenly spread around the setpoint value). At the setpoint the controller will be applying 50% power as required, but stray stored heat within the heater sub-system and in the walls of the furnace will keep the measured temperature rising beyond what is required. At 10° above SP, we reach the top of the proportional band (PB) and no power is applied, but the temperature may continue to rise even further before beginning to fall back. Eventually as the MV falls back into the PB, heat is applied again, but now the heater and the furnace walls are too cool and the temperature falls too low before its fall is arrested, so that the oscillations continue.
The temperature oscillations that an under-damped furnace control system produces are unacceptable for many reasons, including the waste of fuel and time (each oscillation cycle may take many minutes), as well as the likelihood of seriously overheating both the furnace and its contents.
Suppose that the gain of the control system is reduced drastically and it is restarted. As the temperature approaches, say 30° below SP (60° proportional band or PB now), the heat input begins to be reduced, the rate of heating of the furnace has time to slow and, as the heat is still further reduced, it eventually is brought up to set point, just as 50% power input is reached and the furnace is operating as required. There was some wasted time while the furnace crept to its final temperature using only 52% then 51% of available power, but at least no harm was done. By carefully increasing the gain (i.e. reducing the width of the PB) this over-damped and sluggish behaviour can be improved until the system is critically damped for this SP temperature. Doing this is known as ‘tuning’ the control system. A well-tuned proportional furnace temperature control system will usually be more effective than on-off control, but will still respond slower than the furnace could under skillful manual control.
Apart from sluggish performance to avoid oscillations, another problem with proportional-only control is that power application is always in direct proportion to the error. In the example above we assumed that the set temperature could be maintained with 50% power. What happens if the furnace is required in a different application where a higher set temperature will require 80% power to maintain it? If the gain was finally set to a 50° PB, then 80% power will not be applied unless the furnace is 15° below setpoint, so for this other application the operators will have to remember always to set the setpoint temperature 15° higher than actually needed. This 15° figure is not completely constant either: it will depend on the surrounding ambient temperature, as well as other factors that affect heat loss from or absorption within the furnace.
To resolve these two problems, many feedback control schemes include mathematical extensions to improve performance. The most common extensions lead to proportional-integral-derivative control, or PID control.
The derivative part is concerned with the rate-of-change of the error with time: If the measured variable approaches the setpoint rapidly, then the actuator is backed off early to allow it to coast to the required level; conversely if the measured value begins to move rapidly away from the setpoint, extra effort is applied—in proportion to that rapidity—to try to maintain it.
Derivative action makes a control system behave much more intelligently. On systems like the temperature of a furnace, or perhaps the motion-control of a heavy item like a gun or camera on a moving vehicle, the derivative action of a well-tuned PID controller can allow it to reach and maintain a setpoint better than most skilled human operators could.
If derivative action is over-applied, it can lead to oscillations too. An example would be a temperature that increased rapidly towards SP, then halted early and seemed to ‘shy away’ from the setpoint before rising towards it again.
The integral term magnifies the effect of long-term steady-state errors, applying ever-increasing effort until they reduce to zero. In the example of the furnace above working at various temperatures, if the heat being applied does not bring the furnace up to setpoint, for whatever reason, integral action increasingly moves the proportional band relative to the setpoint until the time-integral of the MV error is reduced to zero and the setpoint is achieved.
Another common technique is to filter the MV or error signal. Such a filter can reduce the response of the system to undesirable frequencies, to help eliminate instability or oscillations. Most feedback systems will oscillate at just one frequency. By filtering out that frequency, one can use very “stiff” feedback and the system can be very responsive without shaking itself apart.
The most complex linear control systems developed to date are in oil refineries (model predictive control). The chemical reaction paths and control systems are normally designed together using specialized computer-aided-design software.
Feedback systems can be combined in many ways. One example is cascade control in which one control loop applies control algorithms to a measured variable against a setpoint, but then actually outputs a setpoint to another controller, rather than affecting power input directly.
(EXAMPLE FOR CASCADE CONTROL SYSTEM)
Usually if a system has several measurements to be controlled, feedback systems will be present for each of them.
Fuzzy logic is an attempt to get the easy design of logic controllers and yet control continuously-varying systems. Basically, a measurement in a fuzzy logic system can be partly true, that is if yes is 1 and no is 0, a fuzzy measurement can be between 0 and 1.
The rules of the system are written in natural language and translated into fuzzy logic. For example, the design for a furnace would start with: “If the temperature is too high, reduce the fuel to the furnace. If the temperature is too low, increase the fuel to the furnace.”
Measurements from the real world (such as the temperature of a furnace) are converted to values between 0 and 1 by seeing where they fall on a triangle. Usually the tip of the triangle is the maximum possible value which translates to “1.”
Fuzzy logic then modifies Boolean logic to be arithmetical. Usually the “not” operation is “output = 1 – input,” the “and” operation is “output = input.1 multiplied by input.2,” and “or” is “output = 1 – ((1 – input.1) multiplied by (1 – input.2)).”
The last step is to “defuzzify” an output. Basically, the fuzzy calculations make a value between zero and one. That number is used to select a value on a line whose slope and height converts the fuzzy value to a real-world output number. The number then controls real machinery.
If the triangles are defined correctly and rules are right the result can be a good control system.
When a robust fuzzy design is reduced into a single, quick calculation, it begins to resemble a conventional feedback loop solution. For this reason, many control engineers think one should not bother with it. However, the fuzzy logic paradigm may provide scalability for large control systems where conventional methods become unwieldy or costly to derive.
Fuzzy electronics is an electronic technology that uses fuzzy logic instead of the two-value logic more commonly used in digital electronics.
Since modern small microcontrollers are so cheap (often less than $1 US), it’s very common to implement control systems, including feedback loops, with computers, often in an embedded system. The feedback controls are simulated by having the computer make periodic measurements and then calculating from this stream of measurements.
Computers emulate logic devices by making measurements of switch inputs, calculating a logic function from these measurements and then sending the results out to electronically-controlled switches.
Logic systems and feedback controllers are usually implemented with programmable logic controllers which are devices available from electrical supply houses. They include a little computer and a simplified system for programming. Most often they are programmed with personal computers.
Logic controllers have also been constructed from relays, hydraulic and pneumatic devices, and electronics using both transistors and vacuum tubes.