Industrial Automation - Process control

Industrial Automation - Process control

Basic Control Concepts

 Most basic process control systems consist of a control loop as shown in Figure

1.  This has four main components which are:

• A measurement of the state or condition of a process

• A controller calculating an action based on this measured value against a preset or desired value (set point)

• An output signal resulting from the controller calculation which is used to manipulate the process action through some form of actuator

• The process itself reacting to this signal, and changing its state or condition.

 

Block diagram showing the elements of a process control loop

Two of the most important signals used in process control are called

•       Process Variable or PV

•       Manipulated Variable or MV

In industrial process control, the Process Variable or PV is measured by an instrument in the field and acts as an input to an automatic controller which takes action based on the value of it. Alternatively, the PV can be an input to a data display so that the operator can use the reading to adjust the process through manual control and supervision.

The variable to be manipulated, in order to have control over the PV, is called the Manipulated Variable. If we control a particular flow for instance, we manipulate a valve to control the flow. Here, the valve position is called the Manipulated Variable and the measured flow becomes the Process Variable.

              Principles of Control Systems

To perform an effective job of controlling a process, we need to know how the control input we are proposing to use will affect the output of the process. If we change the input conditions we need to know the following:

•       Will the output rise or fall?

•       How much response will we get?

•       How long will it take for the output to change? .

•       What will be the response curve or trajectory of the response?

The answers to these questions are best obtained by creating a mathematical model of the relationship between the chosen input and the output of the process in question. Process control designers use a very useful technique of block diagram modeling to assist in the representation of the process and its control system. The following section introduces the principles that should apply to most practical control loop situations.

The process plant is represented by an input/output block as shown in Figure 

Basic block diagram for the process being controlled

In Figure 3. 2, we see a controller signal that will operate on an input to the process, known as the ‘manipulated variable’. We try to drive the output of the process to a particular value or set point by changing the input. The output may also be affected by other conditions in the process or by external actions such as changes in supply pressures or in the quality of materials being used in the process. These are all regarded as ‘disturbance inputs’ and our control action will need to overcome their influences as well as possible.

The challenge for the process control designer is to maintain the controlled process variable at the target value or change it to meet production needs whilst compensating for the disturbances that may arise from other inputs. So for example, if we want to keep the level of water in a tank at a constant height while others are drawing off from it, we will manipulate the input flow to keep the level steady.

The value of a process model is that it provides a means of showing the way the output will respond to the input actions. This is done by having a mathematical model based on the physical and chemical laws affecting the process.

For example in Figure 3. 3, an open tank with cross sectional area A is supplied with an inflow of water Q₁ that can be controlled or manipulated. The outflow from the tank passes through a valve with a resistance R to the output flow Q₂. The level of water or pressure head in the tank is denoted as H. We know that Q₂ will increase as H increases and when Q₂ equals Q₁ the level will become steady.

Example of a water tank with controlled inflow

Elementary block diagram of tank process

           Stability

A closed loop control system is stable if there is no continuous oscillation. A noisy and disturbed signal may show up as a varying trend; but it should never be confused with loop instability. The criteria for stability are these two conditions:

•       The Loop Gain (KLOOP) for the critical frequency <1;

•       Loop Phase Shift for the critical frequency < 180°.

          Loop gain for critical frequency

Consider the situation where the total gain of the loop for a signal with that frequency has a total loop phase shift of 180°. A signal with this frequency is decaying in magnitude, if the gain for this signal is below 1. The other two alternatives are:

•       Continuous oscillations which remain steady (Loop Gain = 1);

•       Continuous oscillations which are increasing, or getting worse

(Loop Gain > 1).

        Loop phase shift for critical frequency

Consider the situation where the total phase shift for a signal with that frequency has a total loop gain of 1. A signal with this phase shift of 180° will generate oscillations if the loop gain is greater than 1. Increasing the Gain or Phase Shift destabilizes a closed loop, but makes it more responsive or sensitive.

Decreasing the Gain or Phase Shift stabilizes a closed loop at the expense of making it more sluggish.

The gain of the loop (K_LOOP) determines the OFFSET value of the controller; and offset varies with Set point changes.

        Control Modes

There are five basic forms of control available in Process Control:

•       On-Off

•       Modulating

•       Open Loop

•       Feed Forward

•       Closed loop

On-Off control: The oldest strategy for control is to use a switch giving simple on-off control, as illustrated in Figure 3. 5. This is a discontinuous form of control action, and is also referred to as two-position control. A perfect on-off controller is 'on' when the measurement is below the set point (SP) and the manipulated variable (MV) is at its maximum value. Above the SP, the controller is 'off' and the MV is at a minimum.

           Response of a two positional controller to a sinusoidal input

Modulating control: If the output of a controller can move through a range of values, this is modulating control.

Modulation Control takes place within a defined operating range only. That is, it must have upper and lower limits. Modulating control is a smoother form of control than step control. It can be used in both open loop and closed loop control systems.

Open loop control: Open loop control is thus called because the control action (Controller Output Signal OP) is not a function of the PV (Process Variable) or load changes. The open loop control does not self-correct, when these PV’s drift.

Feed forward control: Feed forward control is a form of control based on anticipating the correct manipulated variables required to deliver the required output variable. It is seen as a form of open loop control as the PV is not used directly in the control action.

Closed loop or feedback control: If the PV, the objective of control, is used to determine the control action it is called closed loop control system. The principle is shown below in Figure

                                          The feedback control loop

The idea of closed loop control is to measure the PV (Process Variable); compare this with the SP (Set Point), which is the desired, or target value; and determine a control action which results in a change of the OP (Output) value of an automatic controller.

In most cases, the ERROR (ERR) term is used to calculate the OP value.

ERR = PV - SP

If ERR = SP - PV has to be used, the controller has to be set for REVERSE control action.

        Control modes in closed loop control

Most Closed loop Controllers are capable of controlling with three control modes which can be used separately or together

•       Proportional Control (P)

•       Integral, or Reset Control (I)

•       Derivative, or Rate Control (D)

       

        Proportional control(P)

This is the principal means of control. The automatic controller needs to correct the controllers OP, with an action proportional to ERR. The correction starts from an OP value at the beginning of automatic control action.

Proportional error and manual value: This is called as starting value manual. In the past, this has been referred to as "manual reset". In order to have an automatic correction made, that means correcting from the manual starting term, we always need a value of ERR. Without an ERR value there is no correction and go back to the value of manual.

Proportional band: Controllers Proportional Band is usually defined, in percentage terms, as the ratio of the input value, or PV to a full or 100% change in the controller output value or MV.

            Integral control(I)

Integral action is used to control towards no OFFSET in the output signal. This means that it controls towards no error (ERR = 0). Integral control is normally used to assist proportional control. The combination of both is called as PI-control.

Formula for I-Control:

Formula for PI-Control:

T_int is the Integral Time Constant.

3.3.3.             Derivative control (D)

The only purpose of derivative control is to add stability to a closed loop control system. The magnitude of derivative control (D-Control) is proportional to the rate of change (or speed) of the PV.

Since the rate of change of noise can be large, using D -Control as a means of enhancing the stability of a control loop is done at the expense of amplifying noise. As D-Control on its own has no purpose, it is always used in combination with P-Control or PI-Control. This results in a PD-Control or PID-Control. PID-Control is mostly used if D-Control is required.

Formula for D-Control:

OP = K      *Tder (dERR /dt )

Where

T_der is the Derivative Time Constant.

        Tuning of Closed Loop Control

There are often many and sometimes contradictory objectives, when tuning a controller in a closed loop control system. The following list contains the most important objectives for tuning of a controller:

Minimization of the integral of the error : The objective here is to keep the area enclosed by the two curves, the SP and PV trends; to a minimum.

                                    Integral on error

Minimization of the integral of the error squared: As shown in Figure 3. 8, it is possible to have a small area of error but an unacceptable deviation of PV from SP for a start time. In such cases, special weight must be given to the magnitude of the deviation of PV from SP. Since the weight given is proportional to the magnitude of the deviation, the weight is multiplied by the error. This gives error squared (error squared = error * weight). Many modern controllers with automatic and continuous tuning work on this basis.

                                           Integral on error square

Fast control: In most cases, fast control is a principle requirement from an operational point of view. However, this is principally achieved by operating the controller with a high gain. This quite often results in instability, or prolonged settling times from the effects of process disturbances.

Minimum wear and tear of controlled equipment: A valve or servo system for instance should not be moved unnecessarily frequently, fast or into extreme positions. In particular, the effects of noise, excessive process disturbances and unrealistically fast controls have to be considered here.

No overshoot at start up: The most critical time for overshoot is the time of start up of a system. If we control an open tank, we do not want the tank to overflow as a result of overshoot of the level. More dramatically, if we have a closed tank, we do not want the tank to burst. Similar considerations exist everywhere, where danger of some sort exists.

Minimizing the effect of known disturbances: If we can measure disturbances, we may have a chance to control them before their effects become apparent.

      Continuous cycling method (Ziegler Nichols)

This method of tuning requires determining the critical value of controller gain (K_C) that will produce a continuous oscillation of a control loop. This will occur

when the total loop gain (K_LOOP) is equal to one. The controller gain value (K_C) then becomes known as the ultimate gain (K_U).

If we consider a basic liquid flow control loop utilizing:

•       A venturi flow meter with a 4-20 mA output feeding into…

•       a PID controller which in turn has a 4-20 mA output that controls...

•       a valve actuator that in turn varies the flow rate of…

•       the process.

When the product of the gains of all four of these component parts equals one, the system will become unstable when a process disturbance occurs (a set-point change) . It will oscillate at its natural frequency which is determined by the process lag and response time, and caused by the loop gain becoming one.

Then measure the frequency of oscillation (the period of one cycle of oscillation), this being the ultimate period PU.

In addition, the final value of K_C is the critical gain of the controller (K_U) . This gain value, when multiplied with the unknown process Gain(s), will give a Loop Gain, K_LOOP, of 1.

    The stages of obtaining closed loop tuning (continuous cycling method)

•       Put Controller in P-Control Only

•       Select the P-Control to ERR = (SP - PV)

•       Put the Controller into Automatic Mode

•       Make a Step Change to the Set point

•       Take action based on the Observation

•       Conclude the Tuning Procedure.

          Damped cycling tuning method

This method is a variation of the continuous cycling method. It is used whenever continuous cycling imposes a danger to the process, but a damped oscillation of some extent is acceptable.

The steps of closed loop tuning (damped cycling method) are as follows:

•       Put the Controller into P-Control Only

•       Select the P-Control to ERR = (SP - PV)

•       Put the Controller in Automatic Mode

•       Make a Step Change to the Set point

•       Take action based on the Observation.

      Cascade Control

If the OP of the temperature controller TC drives the SP of this newly added fuel flow controller FC, then there is a situation that the OP of the temperature controller TC then drives the true flow and not just a valve position.

Fuel flow pressure would practically have no effect on the outlet temperature. This concept is called ‘cascade control’. The principle is shown in Figure

                 

               Single loop temperature control

  The concept of process variable or PV-tracking

PV-Tracking is active if the secondary (FC) controller is in manual mode.

Controllers can be set up to make use of PV-Tracking or not.

The concept is that an operator sets the OP value of the fuel controller manually until they find an appropriate value for the process.

 Initialization of a cascade system

Initialization is actually a kind of manual mode where the operator does not drive the OP value of the primary controller. (temperature controller, TC, in this case.) Instead, fuel controller FC supplies its set point (SP) value, back up the cascade chain to the OP of the controller that will be driving it (the FC’s SP) when the system is in automatic mode. If selected, PV-Tracking can take place in the primary controller as it would occur in normal manual mode.

 Feed forward Control

If, within a process control’s feedback system, large and random changes to either the PV or Lag time of the process occur, the feedback action becomes very ineffective in trying to correct these excessive variances.

These variances usually drive the process well outside its area of operation, and the feedback controller has little chance of making an accurate or rapid correction back to the SP term.

The result of this is that the accuracy and standard of the process becomes unacceptable. Feedforward control is used to detect and correct these disturbances before they have a chance to enter and upset the closed or feedback loop characteristics.

Feedforward Control has

•       Manual feedforward control

•       Automatic feedforward control

   Manual feed forward control

Here, as a disturbance enters the process, it is detected and measured by the process operator. Based on his knowledge of the process, the operator then changes the manipulated variable by an amount that will minimize the effect of the measured disturbance on the system.

This form of feedforward control relies heavily on the operator and his knowledge of the operation of the process. However, if the operator makes a mistake or is unable to anticipate a disturbance, then the controlled variable will deviate from its desired value and, if feedforward is the only control, an uncorrected error will exist.

   Automatic feed forward control

Disturbances that are about to enter a process are detected and measured. Feedforward controllers then change the value of their manipulated variables (outputs) based on these measurements as compared with their individual set-point values.

Feedforward controllers must be capable of making a whole range of calculations, from simpe on-off action to very sophisticated equations. These calculations have to take into account all the exact effects that the disturbances will have on controlled variables.

Pure feedforward control is rarely encountered; it is more common to find it embedded within a feedback loop where it assists the feedback controller function by minimizing the impact of excessive process disturbances.

     Time matching as feed forward control

Time taken for a process to react in one direction (heating) is different to the time taken for the process to return to its original state (cooling). If the reaction curve (dynamic behavior of reaction) of the process disturbance is not equal to the control action, it has to be made equal.

Normally Lead/Lag compensators as tools are used to obtain equal dynamic behavior. They compensate for the different speeds of reaction. A problem of special importance is the drifting away of the PV. One can be as careful as one wants with evaluation of the disturbances, but never reach the situation of absolute perfect compensation. There are always factors not accounted for. This causes a drifting of the PV which has to be corrected manually from time to time, or an additional feedback control has to be added.

     Process dead time

Overcoming the dead time in a feedback control loop can present one of the most difficult problems to the designer of a control system. This is especially true if the dead time is greater than 20% of the total time taken for the PV to settle to its new value after a change to the SP value of a system.

If the time from a change in the manipulated variable (controller output) and a detected change in the PV occurs, any attempt to manipulate the process variable before the dead time has elapsed will inevitably cause unstable operation of the control loop. Figure 3. 10 illustrates various dead times and their relationship to the PV reaction time.

Reaction curves showing short, medium and long dead times

   Overcoming Process dead time

Solving these problems depends to a great extent on the operating requirement(s) of the process. The easiest solution is to “de-tune” the controller to a slower response rate. The controller will then not overcompensate unless the dead time is excessively long.

The integrator (I mode) of the controller is very sensitive to “dead time” as during this period of inactivity of the PV (an ERR term is present) the integrator is busy “ramping” the output value.

Ziegler and Nichols determined the best way to “de-tune” a controller, to handle a dead time of D minutes, is to reduce the integral time constant T_INT by a factor of D² and the Proportional constant by a factor of D.

The derivative time constant T_DER is unaffected by dead time as it only occurs after the PV starts to move.

If, however, we could inform the controller of the dead time period, and give it the patience to wait and be content until the dead time has passed, then detuning and making the whole process very sluggish would not be required. This is what the smith predictor attempts to perform.

    First term explanation(disturbance free PV)

The first term is an estimate of what the PV would be like in the absence of any process disturbances. It is produced by running the controller output through a model that is designed to accurately represent the behavior of the process without taking any load disturbances into account. This model consists of two elements connected in series.

•       The first represents all of the process behavior not attributable to dead time. This is usually calculated as an ordinary differential or difference equation that includes estimates of all the process gains and time constants.

•       The second represents nothing but the dead time and consists simply of a time delay, what goes in, comes out later, unchanged.

      Second term explanation(predicted PV)

The second term introduced into the feedback path is an estimate of what the PV would look like in the absence of both disturbances and dead time. It is generated by running the controller output through the first element of the model (gains and TC’s) but not through the time delay element.

It thus predicts what the disturbance-free PV will be like once the dead time has elapsed.

                     The smith predictor in use

If it is successful in doing so and the process model accurately emulates the process itself, then the controller will simultaneously drive the actual PV toward the SP value, irrespective of SP changes or load disturbances.