Monica Lundh
2.2 BasiC Control ConCepts and human/maChine models
A general model of human/machine systems will be considered (see also van Cott and Kinkade, 1972; Ivergård, 1982).
Control is defined in international standards as a general concept to denote pur- poseful influence. Figure 2.8 shows schematically the main components of a control system. The system has input quantities (yi) and output quantities (yo) respectively.
The relationship between these input and output quantities is determined by a law or transfer function that is dependent on certain parameters (ps). The transfer function of a system may thus be expressed as an equation:
yo = f(yi,ps) (2.1)
A system can be described if the inputs, the transfer function, the parameters, and the output quantities are known. Where one or more of these is unknown, vari- ous methods may be used to define them. The following combinations of known and unknown quantities can occur:
1. Inputs, transfer function, and parameters are known and the output is required, for example, in the evaluation of a system under design.
2. Outputs, functions, and parameters are known, and the inputs are required.
The method used in this case is known as diagnostic and is used, for exam- ple, by doctors trying to find out the type of disease, i.e., trying to determine the reason (input) for the symptoms (output) shown by the patient.
3. Inputs, outputs, and transfer function are known, and the parameters are required. This method is known as identification, and is used, for example, when one wants a mathematical description of a particular event.
4. Inputs and outputs are known, and both the transfer function and the param- eters are required. The method for this type of problem is called the ‘black box’ technique, and is the one commonly used in the description and testing of very complex technical systems such as computers.
In process industry control systems, all quantities are more or less well known, depending on how well ‘identified’ the system is; in other words, they are like option (3). A skilled operator or a well-developed computer control system ‘knows’ the vari- ous parameters well. In another case, one may be working with some form of ‘trial
Control
Equipment Control System
Input Value yi
(input signal)
Controlling Quantity
Controlled
Quantity yo
(output signal) Disturbance
Figure 2. Diagram of simplified control system.
and error’ philosophy. In practice there are always some unknown factors—distur- bance quantities (yn)—for which the operator/control system must compensate.
Two types of systems are concerned in control: the controlling system (control equipment) and the controlled system (process) (see Figure 2.8). The control equip- ment controls with the help of the controlling quantity, so that the desired value (set value) of the process can be maintained. The true value which the process returns is called the actual value. The process is also affected from outside, and this effect is known as the disturbance quantity.
2.2.1 openAnd Closed Controls
In open control, the control equipment is not affected by the object or process. Cer- tain types of control equipment in central heating systems are examples of open con- trol (see Figure 2.9). The control equipment has the job of reducing the temperature in the water used for heating at night, according to a preset programme. The measur- ing device, for example, senses by means of electrical contacts switched on or off by pegs from the control device, the stage in the programme that has been reached.
The contacts ‘inform’ the central link, which processes the information. After pro- cessing, certain of the input conditions given by the measuring device will result in a command to the control device (for example, to start valve motor 1). Note that no signal from the process/control object affects the control equipment.
It is very difficult using open control to maintain the actual value at the set value.
This can only happen when the object is not exposed to any external disturbances, or where these are well known and can be taken into account in advance. Where these conditions are not met, large deviations from the desired values must be allowable in order for open control to be used.
Closed control (see Figure 2.10) has a system where the output signal (actual value) is fed back to the input signal (desired value) and compared with it. The input signal is, in certain cases, the desired output signal. When this required actual value, the desired value, is compared with the true value fed back, a difference results if the actual value deviates from the set (desired) value. After amplification via the control device, this deviation then goes in as the control quantity to control the process or object.
Measuring
Device Command
Decision Input
Quantity
Controlled Quantity Disturbance
Quantity
Control
Device Object
Control Equipment
Figure 2. Open control: Note that there is no feedback from the output of the controlled object to the controller, but only within the controller itself. Changes in the controlled quan- tity thus cannot affect the control equipment.
The controlled system is also affected by external disturbances (yn). The signal (yo) goes out from the system, is measured, and fed back.
When the comparison between the input (yi) and the feedback signal is an addi- tive, this is known as positive feedback. This type of feedback, which gives an accelerating change of the output signal, is used, for example, to achieve the fastest possible changes of output signal. When the comparison between yi and yo is subtrac- tive, we have negative feedback. This means that yd (difference quantity) decreases when the output yo increases, and thus attempts to stop yo rising. Negative feedback is the most common form of feedback. One well-known example of a control mecha- nism with negative feedback is the regulation of the water level in a WC cistern by a ball-cock valve. Figure 2.11 shows a diagram of a cistern with its ball-cock valve and the corresponding signal control diagram (a block diagram).
A distinction may be made between closed control systems, which only have to compensate for outside disturbances and not for changing inputs, and closed control systems which are not exposed to outside influences but which only attempt to follow changes in input quantity. The first type of regulation system (where the input value is not changed) is known as constant regulation. The second type, with variable inputs, is known as servo-regulating. A servo-regulating system only has the task of trying to achieve an output that follows as closely as possible the varying input level. The WC cistern described above is an example of constant regulation, which tries to keep the output (in this case, the water level) constant regardless of outside disturbances (that is, the water being flushed out of the cis- tern). Most regulation systems in process industries function as constant regula- tion systems (where the set value can be changed). At start-up, however, a certain degree of servo-regulation takes place.
According to the Swedish Electrical Standards (SEN 0106), a closed control system is not a regulated system when the control equipment consists of a person. The authors’ opinion is that this is not applicable here, where the same terms are used for both human and technical components. The concepts of a regulated system are therefore also used here for manual control with feedback.
Comparator Amplifier
Input Quantity
Controlled (regulated) Quantity (yo) Control
(regulatory)
Device Object
Regulatory Equipment
(yi)
Difference Quantity
(yd)
yn
Control Signal
Converter (yf)
Figure 2.10 Closed-loop control, also known as regulation. The regulated quantity affects the controlling signal.
2.2.2 HuMAn/MACHine Control systeMs
A human operator can become just as important a component in a regulatory or con- trol system as the electronic or mechanical components. Constant regulation (where the operator puts in new set values at certain intervals—running orders) is normally found in industrial processes. The concept of ‘controller’ should only be used where the worker is an ‘online’ part of the regulatory system; otherwise, the more general term ‘operator’ is usually used.
When someone is controlling a car, he or she has to carry out a number of mental differentiations and integrations, depending on the response produced to the input quantity (see Figure 2.12). Similar conditions exist for many other control tasks, for example, steering a ship. A human being’s ability to carry out these mental pro- cesses successfully is relatively limited. Two French researchers (Tarriere and Wis- ner, 1963) showed that fatigue produces very strong oscillations when controlling a car. The ability to carry out the necessary integrations and differentiations clearly worsens with fatigue.
The task of controlling a machine can be made easier by introducing various aids (off-loading mechanisms) for performing the integrating and differentiating tasks. There are two main types of such mechanisms:
1. Influence the controlled object directly, and thereby the actual value (‘A’
mechanisms—Aiding);
2. Influence the feedback, and thereby the information to the human operator (‘Q’ mechanisms—Quickening).
yd Difference
Float Lever Arm
Cistern yi
Water Pipe Water Level yo Valve
Comparator Amplifier
Input Quantity
Regulated Quantity (yo) Disturbance
Quantity (yn) Regulation
Device Object
Measurement Device Difference
Quantity (yd) (yi)
Figure 2.11 Regulation of water level using a float valve (constant regulation).
Introduction of an ‘A’ mechanism, as shown in Figure 2.13, brings about a reduction in the worker’s load, as certain calculations no longer need to be carried out. Also, the aiding mechanism, which lightens the load concomitantly makes the system faster.
Without this mechanism, the operator’s control output of the system is affected. As the quantities within the system are fed back continuously to the operators, they will be immediately aware of changes, which occur as a result of their actions.
Simplification of the controller’s task using an ‘A’-simplifying mechanism works preliminarily in two ways:
1. By relieving the operator of certain operations
2. By giving information more rapidly about changes in the process
‘A’-simplified mechanisms cannot be used where it is impossible to make the technical changes which will directly affect the output quantity of the controlled object. These output quantities may depend on quantities that cannot be controlled, such as wind, waves, and similar aerodynamic and hydrodynamic relationships. In such cases, a ‘Q’-simplifying mechanism can be used. ‘Q’-simplifying mechanisms do not work directly on the output quantities of a system, but they change and sim- plify the information which is presented to the controller. There are different types of ‘Q’ simplification:
1. Predictive ‘Q’ information 2. Complete ‘Q’ information 3. Partial ‘Q’ information
T ∫
yi
k1 k1 k2 d/dt
d/dt ∫
Driver Vehicle
yo (a)
(b) (a) The vehicle’s control response
(b) Driver-vehicle control response
∫ ∫
yi yo
T = Time delay (man’s reaction time)
Figure 2.12 Control response of a vehicle (a) and the driver-vehicle control response (b).
The need for ‘Q’-simplified mechanisms occurs in many industrial processes, such as the starting up of a process where certain values (for example, temperatures) have to be increased according to a particular time plan. Such systems often have considerable inertia, which means that there is no direct relationship between control movements and the output of the system, and it takes a long time before any result can be read from the instruments.
The operator has a control level whose movement (input quantity to the process) is integrated a number of times before the desired temperature change (output quan- tity from the process) is obtained. In such cases the desired temperature sequence cannot be set. In order to improve the performance of the operator, the temperature (output quantity) can be differentiated a suitable number of times, and this informa- tion will then form the basis of an automatic calculation (prediction) of the tem- peratures at various times in the future for different degrees of control movement.
These expected temperatures can be presented as a curve on a screen, on which the desired temperature sequence can also be displayed. Figure 2.14 shows the required sequence of values marked as a line on the screen. The prediction of the course of the change for the actual control move- ment is automatically calculated and should normally be shadowed by the line which shows the desired sequence.
In this way, any deviations can be seen very quickly. This is shown as predictive
‘Q’ information.
In modern industrial processes there is no direct regulation; the operator sets in the desired values at certain times, and the process is automatically controlled within these values (technical constant regulation). Starting and stopping of the process is done with what are known as group starts, where one control is used to
Expected Temperature (o C)
0 10 20 30 40 50
Time
Figure 2.1 Predicted values presented on a display.
T ∫ ∫
Controlled Object Controller
yi yd yo
k1 k2 k3
Figure 2.1 Introduction of aiding—the ‘A’ mechanism.
initiate the start-up, in the correct sequence, of whole groups of motors, valves, and so on. This is often thought to be necessary so that the handling of the process will be sufficiently rapid or accurate. Predictive ‘Q’ information should probably be used here as an alternative or an addition, as this would provide the operator with a better understanding of how the process works. The work would also be more interesting, which in turn could lead to the process being better controlled.
Figure 2.15 shows a system containing four integrating elements. One example of a machine which operates in this way is a submarine. When a change is made to the depth control of a submarine, it takes a long time before a result is obtained in the form of a new stabilised depth being reached. Also, the movement of the control is not proportional to the movement of the depth control integrated (in this example) four times. Because the movement of the submarine also depends to a large extent on water currents, hydrodynamic forces, and other external factors, the output—that is, the ultimate depth—cannot be influenced by an aiding mechanism which will directly affect the movement of the vessel, and thereby the output quantity. It is also very difficult to control the submarine solely with the aid of a depth gauge. One alternative is to feed information back directly to the operator on what is happening to the submarine at different stages, as shown in Figure 2.16. This is known as com- plete ‘Q’ information. Instruments continuously measure the depth, vertical posi- tion, change of depth, or diving angle with time (first time derivative of the output), rate of change of diving angle (second derivative of the output), and the acceleration of the diving angle (third derivative of the output).
In order for the ‘Q’-simplifying mechanism to function correctly, the constants in Figure 2.16 must be very carefully determined. There is no complete theoreti- cal method for determining these constants, but their optimal values can often be deduced with the aid of analogue computers set up to simulate the system in question.
The derivative which is weighted with the output quantity to produce the feedback need not be a time derivative but could also be a position derivative, for example.
Where it is not possible to measure the different derivatives directly, they can be calculated indirectly using, for example, electronic differentiation.
As a summary of the human ability to function as a continuous controller in a system, it maybe stated that the tasks performed should be as simple as possible in purely mathematical terms. If possible, they should not be more complex in practice than a person functioning as a simple amplifier, that is, no differentiation or integra- tion is required. With the aiding mechanisms described here, it is feasible to intro- duce tasks specially matched to a worker’s performance abilities.
The types of models described so far represent simple control circuits, for exam- ple, control of speed in a car or the position of the car on the road. They have their
T ∫
Information
Device ∫ ∫ ∫
yi x1 x2 x3
Controller Submarine
yo
Figure 2.1 System containing several integrating elements.
T Information Device yix1 x2 x3 Controller Submarine x4 yo k1 k2 k3 k4
∫ ∫ ∫ ∫ Figure 2.1 System feeding partial information back to the operator.
primary practical applications in the control of, for example, aeroplanes, cars, and ships. As far as the control of more complex processes within the process industries is concerned, these types of models are only of importance for the understanding of the control of the individual process parameters. A process industry, on the other hand, consists of a large number of parameters, commonly several hundred. When the operator is functioning directly in the control loop, however, it is important to understand these theoretical basics first.