i.p.a.s.-systeme ADCO (2.2) (ADaptive COntroller Siemens S7-4xx und S7-318)

Transcription

1 ADCO (2.2) (ADaptive COntroller Siemens S7-4xx und S7-318) 1. Introduction 2 2. CONTROLLER CONFIGURATION/PROGRAMMING Inputs: Outputs: 9 3. Controller tuning Example how to adapt a pressure control Example how to adapt a temperature control Continuous Adaption Cascade Control Loop Multirange-Controller (ADMR) Inputs: Outputs: S7 Specials System reqirements Installation List of blocks in example project Tips and Tricks 23 Page 1 of 25

2 1. Introduction i.p.a.s.-systeme In most cases the tuning (optimization) of PID-controllers is based on so-called trial and error methods. This requires special experience and also takes a lot of time especially when trying to control sluggish processes (e.g. temperature processes). Above that the control quality does not correspond to the optimum and still leaves quite some room for improvements. The tuning procedure gets even more difficult if there are non-linear or time-variant processes to be controlled. The adaptive controller ADCO provides solutions to all these problems. It automatically adapts itself to changing process characteristics but it can also be operated as a controller with constant parameters. In this instance the adaptation is turned off after the initial optimization step and the controller then serves as a better alternative to a regular PID-controller. If necessary the adaptive mode can be turned back on any time during the operation of the controller. Besides standard lag processes ADCO is especially suited to control processes with integrating characteristics and also processes with significant dead times. It is common knowledge that regular controllers have problems with these types of processes. As opposed to PID-controllers ADCO provides an equally optimal control behaviour in set point control as well as disturbance control (non-measurable signals acting on the process variable) tasks. Figure 1: Block structure of the adaptive control loop Page 2 of 25

3 ADCO basically consists of two main parts: The process model estimation is based on a method which is known as DSF (Discrete Square Root Filtering) or SRIF (Square Root Information Filter). This procedure calculates a parameter model of the process to be controlled by evaluating the process signals (manipulated variable / controller output, process variable) according to the method mentioned above. The controller optimization is based on an estimated process model which is validated through a supervisory function. The algorithm delivers an optimal state controller. Besides the actual control error a few more states allowing a prediction about upcoming process variable values are fed into the calculation of the manipulated variable. Since the state controller evaluates more information about the process behaviour than any PID-controller it provides a superior control quality even when acting on simple linear processes. After a set point change or a disturbance of the process variable all state deviations are reduced to 0. The control behaviour depends on one tuning parameter (controller sensitivity) which can adapt values between -100 and 150. The default value for this parameter is 25 and does not have to be changed in most applications. Increasing the sensitivity basically means increasing the activity of the controller, i.e. the controller is acting stronger onto the process using up more energy. Outstanding advantages compared to regular controllers: essentially faster control parameter tuning better control quality controlling easy to handle processes significantly better control behaviour controlling processes with integrating and/or dead time characteristics optimal tuning for set point and disturbance control adaption to changing process characteristics basically no overshoot Table 1: Advantages compared to regular controllers Page 3 of 25

4 2. CONTROLLER CONFIGURATION/PROGRAMMING Like standard function blocks within STEP S7 the adaptive state controller is to be configured by connecting block inputs and outputs to variables, constants or to inputs/outputs of other function blocks. Bild 2.1: ADCO (FB50) Funktionsbaustein Page 4 of 25

5 2.1. Inputs: PV_IN (Offset 0): Process variable (e.g. temperature, pressure, level etc.) of the control loop in physical units. NM_PVLR (Offset 102): Low range of the process variable in physical units. NM_PVHR (Offset 106): High range of the process variable in physical units. SP_EXT (Offset 114): An external set point (in physical units) can be connected to this input. This feature is necessary to set up e.g. cascade control loops. SP_EXT_ON (Offset 4.0): With this output it is possible to relay the external-set point-mode to other function blocks.. NM_LMNLR (Offset 6): Low range of the manipulated variable (controller output). NM_LMNHR (Offset 10): High range of the manipulated variable (controller output). LMN_LLM (Offset 14): Low limit of the manipulated variable within the controller output range. LMN_HLM (Offset 18): High limit of the manipulated variable within the controller output range. TTIME (Offset 22): The transition time (for an exact definition see chapter 3) must be defined during the configuration or later during runtime (in single loop displays) just before the controller is to be optimized. The dimension of this entry is [min]. The transition time determines the internal scan rate of the controller (internal scan rate = transition time/60). The main scan time of the function block has to be adjusted in a way that the internal scan rate according to the formula above can be realized. The reason for the need to specify a separate (internal) scan rate is that it does not make sense to acquire data for a sluggish process (like a temperature process) with a frequency in the higher [Hz] range. In this case the differences of the process signals (between two scans) would not deliver any information about the process behaviour. The differences are then based on disturbances with a share of almost 100%. If a value of 0 is being entered into this field then the internal scan rate is set equal to the scan rate of the function block. Page 5 of 25

6 DTIME (Offset 26): The model based state controller is especially suited to control dead time processes. The process dead time (for an exact definition see chapter 3) however is not calculated or estimated by the control algorithm but it has to be entered (in [min]) by the user. During the calculation of the manipulated variable this entry is evaluated. In other words the calculation of the manipulated variable is not based on the actual process variable but on a process variable in the future which is predicted by means of the estimated process model and the specified dead time. The dead time can be changed on-line to accommodate changing process characteristics. SENS (Offset 30): The sensitivity of the controller basically is the only tuning parameter ( ) to be adjusted by the user. This field has a default value of 50 which in most cases does not have to be modified. Increasing this value also means increasing the activity of the adaptive controller. Decreasing the value of course means decreasing the activity. DIRECT (Offset 34.0): A lot of industrial controllers require the specification of the so-called controller action as part of the tuning procedure. This entry usually determines whether a decreasing process variable (below the set point) should be controlled by an increasing (1 or TRUE: direct) or a decreasing (0 or FALSE: reverse) manipulated variable. In this algorithm the specification of the controller action is used to validate the process model estimated by the identification routine. Direct means that the process must have a positive gain factor, reverse of course means the opposite. The estimated process model will only be conveyed to the controller optimization if - besides other checks - the estimated process gain factor corresponds to the gain factor derived from the specification of the controller action. For processes with an integrating characteristic this entry is not relevant since a gain factor is not defined for this type of process. NO_VAL (Offset 34.1): Before an estimated process model is handed over to the controller optimization procedure it is validated by applying different checks. Only if all checks show a positive result the process model is released and can be used to base a set of control parameters on. By means of this selection field the process model check can be turned off. This should only be done when - because of very noisy signals - a valid process model can not be found. However this should happen very rarely (0 or FALSE: model validation; 1 or TRUE: no model validation). LMN_DEL (Offset 36): The value of this input limits the change velocity of the manipulated variable (output change - i.a. % - per [min]). This limitation can be applied to valves where the opening and closing speed have to be limited (due to process related reasons) to a maximum value. This entry is not relevant in output track and manual mode. A value of 0 means no limitation. Page 6 of 25

7 LMN_INI (Offset 40): The initial (after the first system start) manipulated variable value of a newly configured controller is determined in this field. In most cases this value remains at 0 (default value). A deviating value should be entered when setting up split-range control loops. In this case the value is usually specified so that both valves involved are started up in a de-energized state. LMN_TRK, LMN_SEL (Offset 46, 44.0): If this mode (LMN_SEL; 0 or FALSE: no track mode; 1 or TRUE: track mode) is enabled the controller output (manipulated variable) is overwritten by a predefined value (LMN_TRK). SP_TRK_ON (Offset 50.0): To ensure a bumpless transfer from manual to automatic mode the set point can be defined to track the process variable value (in manual mode only). After switching to automatic the control error (set point - process variable) is 0 which means that no step change is generated at the controller output, i.,e. the manipulated variable (0 or FALSE: no set point track; 1 or TRUE: set point track). NO_BUM (Offset 50.1): In the inner controller of a cascade loop it is not possible to apply the set point track mode ensuring a bumpless transfer to automatic. By means of this input it is nevertheless possible to do the job. When enabled the manipulated variable is temporarily processed through a low pass filter (0: no low pass filtering; 1: low pass filtering). LMN_STB, LMN_STBON (Offset 52, 50.2): The control algorithm offers a mode (LMN_STBON; 0: no standby mode; 1: standby mode) where the adaptive controller can be operated parallel to an already existing and active control concept. By means of the input LMN_STB the manipulated variable of the active controller is usually transferred into the adaptive state controller. Based on that signal and also on the process variable a process model can be estimated and subsequently a controller can be optimized. In this mode the manipulated variable of the adaptive controller should not modify the corresponding valve position (this has to be ensured by an overall function block layout). The manipulated variable should just be recorded. By comparing the manipulated variable of the adaptive controller with the output of the active (acting on the valve) controller it should be possible to make a statement about the control quality of the adaptive algorithm. In this way the adaptive controller can be tested without taking any risk of upsetting the process. AD_OVR (Offset 56.0): If this input is activated (0 -> 1) the controller is forced into the non-adaptive mode. Forcing the controller into this mode is appropriate e.g. when the process variable value can no longer be acquired because of a sensor failure. Otherwise (when continuing to run the controller in the adaptive mode) a disrupted relationship (manipulated variable <-> process variable) would possibly be projected into the estimated process model. If the reason (e.g. sensor failure) for the switch over (override) is no longer existing the controller does not automatically go back to the adaptive mode. If necessary this has to be done by a manual user interaction. Page 7 of 25

8 AU_OVR (Offset 56.1): If this input is activated (0 -> 1) the controller is forced into manual mode. Forcing the controller into this mode may be applicable e.g. during certain emergency shutdown strategies. If the reason for the switch over (override) does no longer exist, the controller does not directly go back to the automatic mode. If necessary this has to be done by a manual user interaction. IL_VAL, ILCK (Offset 58, 56.2): If this mode (ILCK; 0 or FALSE: no interlock; 1 or TRUE: interlock) is activated the controller goes into the interlock state, i.e. the adaptation is deactivated, the controller changes to manual and the controller output adapts a predefined value (IL_VAL). OSHT (Offset 62.0): By means of this input it is possible to define whether it should be possible to operate the controller immediately after the switch over to the interlock state (one-shot) or whether the controller should be forced to this state as long as the interlock input is set (0 or FALSE: regular mode; 1 or TRUE: one-shot mode). SAMPLE_T (Offset 64): Scan time of the function block in [sec]. SP_OP (Offset 110): Set point of the control loop in physical units. LMN_IN (Offset 118): Manipulated variable (controller output) in manual mode. AUTO (Offset 122.0): This input defines the controller mode (0: manual; 1: automatic). ADAP (Offset 122.1): This input defines the adaptive mode of the controller (0: adaptation off; 1: adaptation on). Page 8 of 25

9 RESET (Offset 122.2): With this input it can be determined whether the adaptive controller is to be reset. Reset means that the controller loses all previously gathered information about process characteristics which in turn means that it has to be optimized again (0: no reset; 1: reset). RNG_ADA (Offset 68.0): If this input is set the controller increases the process variable range by 50% if the process variable approaches its current high limit. Likewise it decreases the range by 50% if the process variable approaches its current low limit value. SUB_ZER (Offset 68.1): If this input is set and RNG_ADA is set then the low limit of the process variable range can (during a range modification step) adapt a value below 0. Otherwise this is not possible Outputs: LMN (Offset 70): Manipulated variable. SP (Offset 74): Set point of the controller. IDENT (Offset 78.0): If the adaptation is turned on and if the controller detects a sufficient dynamic movement of the process variable then the process model estimation procedure within the adaptive algorithm is activated. An active estimation routine is indicated by setting this block output. VAL_M (Offset 78.1): The estimated process model is checked before it is passed to the control parameter optimization procedure. This check contains several validation steps. Only if all validation steps show a positive result the process model is released to the optimization procedure. A positive result is indicated by setting this block output. ORIG_M (Offset 78.2): This output indicates that a first valid process model has already been found and that therefore the controller can be switched to automatic. IF ORIG_M = 1, IDENT = 0 and VAL_M = 0, the ADCO has found a valid model. You can now deactivate the adaption (ADAP = OFF). QAUTO (Offset 78.3): This output represents the controller mode (0: manual; 1: automatic). Page 9 of 25

10 QADAP (Offset 78.4): This output indicates whether the controller works in the adaptive mode (0: adaptation off; 1: adaptation on).. QLMN_SEL (Offset 78.5): With this output it is possible to relay the track-mode (LMN_SEL) to other function blocks. QSP_EXT (Offset 78.6): With this output it is possible to relay the external-set point-mode (SP_EXT_ON) to other function blocks. QILCK (Offset 78.7): With this output it is possible to relay the interlock-state (ILCK) to other function blocks. ER (Offset 80): Current control error, i.e. the deviation of the Process value from the set point value (SP PV_IN). F_SCAN (Offset 84.0): During the first scan after the configuration/programming (F_SCAN = 0) of a new controller certain instance variables of the adaptive controller have to be initialized. During the initialization the value F_SCAN is set to TRUE or 1 which means that during the following scans the initialization routine is skipped. VERSIO (Offset 86): Version number of the adaptive controller (e.g. Rev. 2.2 ). Page 10 of 25

11 3. Controller tuning i.p.a.s.-systeme If a new controller has been configured or an existing one has been reset the control algorithm does not have any information about process characteristics. Therefore the controller optimization which is based on an estimated and validated process model cannot be performed. In this situation the algorithm prevents the controller from being switched to automatic. Through manual stimulation (changing the manipulated variable) knowledge about the process behaviour has to be relayed to the identification routine. First of all the adaptive control algorithm needs some basic information about the process dynamics (transition time) and possibly about process dead times. The transition time (see figure 4.1) is defined for lag as well as for integrating processes. Concerning lag processes the transition time is the time necessary for the process to reach a new steady state after a step change of the manipulated variable (controller output). Dealing with integrating processes the transition time is the time the process needs - starting out at a steady state - to change its process variable by n/2 % as a response to a step change of the controller output of also n % (e.g. 20 % step change of the manipulated variable -> 10% change of the process variable). It is sufficient to enter the transition time as an approximate value in [min]. The control algorithm is so robust that the entered value can be five times higher or five times lower than the real transition time without impairing the resulting control quality. The dead time [min] should have a higher degree of accuracy. If these times are unknown they have to be determined by applying a step change to the manipulated variable (with the adaptation turned off). The necessary numbers can be classified by taking a look at the resulting process variable graph. During the following learning phase (adaptation turned on!!) a classical transfer function (answer to a step change of the manipulated variable) can be recorded. Furthermore it is also possible to adjust the controller output several times during the learning phase. So it is conceivable that the process variable is manually controlled and led to its set point. As soon as the algorithm detects its first valid process model the controller can be switched to automatic, i.e. the internal interlock to force the controller to manual mode is no longer effective. With the majority of processes it is not necessary to operate the controller in a continuously adaptive mode. The control algorithm can then work with a constant control parameter set (after turning off the adaptation). Page 11 of 25

12 Figure 2: Lag process Figure 3: Integrating process Page 12 of 25

13 2.1. Example how to adapt a pressure control If you want to adapt a pressure control, you don t have to change the value of the manipulated value as much as when you want to adapt a temperature control (see chapter 2.2, page 15). The next figure can help you to adapt a pressure control. Info: You have to change the value of the input DIRECT to Gain=Reverse, if the value of the MV increases and the PV decreases (reverse gain). Page 13 of 25

14 Figure 4: ADCO-Adaption Page 14 of 25

15 2.2. Example how to adapt a temperature control The temperature adjustment requires some recommendations that we would like to mention in this chapter. To get a good model, we ll always need to take the faster phase. For a temperature control, in normal cases, the warm-up phase is significantly faster than the cool-down phase. If it s possible, give the value of the manipulated variable a high jump (e.g. from 0% to 80%), if not, take smaller values. During the heating phase, it s important to interrupt it at the right moment, and to initiate the cool-down phase immediately. In this example, we assume that the temperature range of the heater goes from room temperature (control valve = 0%) till a maximum temperature of 1200 C (control valve = 100%). We start our adaption with a high jump of the manipulated value (from 0% to 80%). At 1/3 of our maximum temperature (about 400 C), we ll slowly decrease the value of the manipulated value to get the dynamic ranges needed for the calculation of our model. Info: Figure 5: Example for adapting a temperature control The above change of the manipulated value mostly depends of the heating type. Depending on the heater design, you can also try to use different values for the MV to get the dynamic areas. In electric heaters with Thyristor Power Controller, the continuous control output of the controller must be connected to a 3-point step FB (S7 Library), so that it can be converted into a digital output signal to control the Thyristor Power Controller. The value of the sensitivity should be set in this case, maybe to a very high value (100 or more), so that the heating process will start with a high or the highest heating power. Page 15 of 25

16 4. Continuous Adaption i.p.a.s.-systeme If processes show a distinct non-linear or time-variant behaviour it may be necessary to operate the controller in a continuously adaptive mode. Through this the estimated process model is being adapted to changing process characteristics which again causes the control parameters to track the optimal controller settings. Figure 6: Block diagram for continuous adaption Since the process identification evaluates all process signals and projects these signals into a model, attention should be paid to the fact that signal failures can severely disrupt the information gathered in the process model so far. In the block diagram a signal failure turns off the adaptation and also switches the controller to the output track mode. Since the manipulated variable is fed back into the track variable the controller output keeps its last (before the signal failure) valid value. Page 16 of 25

17 5. Cascade Control Loop The adaptive state controller can also be applied to cascade control loops. The function block structure in the next figure shows one possible layout. Figure 7: Function block structure in a cascade loop If the inner controller (ADCO 2) is switched to manual mode, to track mode, to interlock mode or to local set point then the adaptation of the outer (primary) controller is turned off and the track mode of this controller is activated. The controller output (manipulated variable) of ADCO 1 (set point for ADCO 2) now tracks the process variable of the inner controller. This means that ADCO 2 can be switched back to its regular mode without causing a bump. Furthermore the functionality of the complete cascade is reestablished by simply adjusting the desired mode of the inner controller. If necessary the adaptation feature of ADCO 1 (outer controller) has to be re-enabled through a separate user interaction. In a cascade control loop set up with two adaptive state controllers it should never happen that both controllers are operated in the continuously adaptive mode at the same time. This could cause mutual disturbances which most likely diminish the control quality of the cascade loop. Page 17 of 25

18 6. Multirange-Controller (ADMR) Besides the regular adaptive controller the software also contains the so-called Multirange-Controller. A special feature of this controller is that it can be subdivided in up to 8 different zones and that these zones can individually be optimized. The switch over between zones can be initiated by the user or any other process event. If the process shows a distinct non-linear behaviour then the process variable range can be split up into e.g. 8 sections. Through this the process can be linearized section by section. Since individually optimized control parameter sets are assigned to the different linearized zones an essential improvement of the control quality can be achieved. Another conceivable application is the control of batch processes where process characteristics are predictably changing during a production lot. Here the zone specific control parameters can automatically be activated depending on the progress of the corresponding batch. Transition time, dead time and controller sensitivity are defined only once per Multirange-Controller. They are equally valid for all controller zones. The start-up (tuning) procedure for the Multirange-Controller basically coincides with the procedure described in chapter 3. The only difference is that the procedure - except for the definition of the transition time and dead time - has to be performed for each configured (up to 8) range. Page 18 of 25

19 Bild 7.1: ADCO-Mehrbereichsregler (FB80) 7.1. Inputs: RANGE (Offset 68): The range will be set via the Faceplate. The definition of the controller range (1... 8) determines which control parameter set is to be loaded during the operation of the controller. RES_RN (Offset 130.3): The algorithm of the Multirange-Controller contains 2 ways to reset or initialize the controller. Either the currently selected zone (RES_RN) or all controller ranges (RESET) can be initialized. Page 19 of 25

20 FILL (Offset 130.4): By means of this block input the tuning parameters of the actual range are copied to all remaining ranges which are not tuned yet. This block input is defined as a so-called IN-OUT -input, i.e. it can be modified like a regular block input as well as through the internal algorithm. This however means that the output of another upstream block must not be connected to this input type Outputs: ORI_M1... ORI_M8 (Offset , 83.0, 83.1): These outputs indicate whether the corresponding range (1... 8) has already been optimized. QRANGE (Offset 84): This output contains the number (1... 8) of the currently selected and active range. Page 20 of 25

21 8. S7 Specials 8.1. System reqirements S7 4xx-Serie oder S7-318 Step7 Basis V or higher 8.2. Installation Unzip S7_ADCO_LIB.ZIP in Lib directory in SIMATIC-Managers e.g.: C:\SIEMENS\TEP7\S7LIBS. After unzip library is in directory eg.: C:\SIEMENS\TEP7\S7LIBS\ADCO. Open Step7 project. Open KOP/FUP/AWL-Editor. Open library list. Open branch library Here are FB50 (ADCO) and FB80 (ADMR). Page 21 of 25

22 8.3. List of blocks in example project FB50 Adaptive controller ADCO FB60 LAG-block for simulation FB80 Adaptive multi range controller ADMR OB36 OB for cyclic execution of FBs with 50 msec cycle. OB100 Restart-OB (Initializing FBs) OB1 not used OB80 not used DB10 ADCO data block for interaction with simulation or HMI DB30 ADMR data block for interaction with simulation or HMI DB50 ADCO instance data block DB60 ADCO instance data block DB80 ADMR instance data block DB90 LAG instance data block VAT10 ADCO variables table for diagnostic / testing VAT30 ADMR variables table for diagnostic / testing Page 22 of 25

23 7. Tips and Tricks i.p.a.s.-systeme Basically the learning phase to set up a process model and to optimize a state controller based on this model can be started at any time. During the first learning phase (i.e. after a new controller is placed into the PCS7-program or after an existing controller has been initialized) the process model estimation should be started in a nearly static operating point and should end in a different but also nearly static operating point. The reason here fore is that during the transition from a static to a dynamic phase and also during the transition from a dynamic to a static phase the best process information can be transferred to the process model (see figure 9.1). A consequent fine tuning optimization (based on an already existing process model) can also be started in the course of a dynamic transition without impairing the resulting control quality. Figure 8: Transition phases with essential information Page 23 of 25

24 If a controller is operated in the continuously adapting mode then it makes sense to limit the change rate of the manipulated variable (LMN_DEL). Assumed that no limitation is introduced the manipulated variable can get into a oscillating state if a wrong process model despite all checks is conveyed to the optimization procedure (this should happen very rarely, but it cannot be guaranteed that it never happens). A high-frequent oscillation can lead to a static process variable which in turn means that no process information can be extracted from the process variable, i.e. the model can no longer be improved and the control algorithm is locked. The value for a limited change rate of the manipulated variable depends on the dynamic behaviour of the process and on the requirements on the control quality of the loop. A generally valid value cannot be indicated. If a process contains a significant dead time characteristic then the manually entered dead time value (DTIME) should always be lower as or equal to the real process dead time. If the indicated dead time is too low the control quality diminishes very slowly. However if it is too high the quality of the control loop is strongly affected. The reason for that behaviour is still to be examined. To establish an optimal control quality an exactly reproducible and constant scan time is necessary. This means that the adaptive controller has to be triggered by a cyclic OB (Organization Block). Triggering the controller through OB1 (i.e. in the so-called free cycle or PLC-mode) would impair the control performance. Moreover the basic scan or cycle time of the controller must not be lower than the scan time for external inputs (esp. the process variable). If this is not been followed then the control algorithm (i.e. the process model estimation) is dealing with identical numbers in subsequent scan steps even if the process is in a dynamic transition. This disturbs the process model and reduces the control quality. If process characteristics show a defined difference in certain transitions (e.g. a temperature process where heating up takes more time than cooling down) the faster transition should be the basis for a process model estimation and a subsequent controller optimization. In the example above (provided that cooling down shows faster dynamics) the system should first be heated up with the adaptation turned off. Then the adaptation should be turned on and the system should be cooled down. The resulting controller (with constant tuning parameters) is capable of handling both the heating up and cooling down phase. If the controller is acting too strong on the process, i.e. it produces an oscillating controller output (manipulated variable) and thereby approaches stability limits, the following actions should be taken (in that order): Page 24 of 25

25 Reduce the sensitivity factor (SENS) step by step if necessary down to Limit the change velocity (LMN_DEL) of the manipulated variable (controller output). The value to be entered depends on the process dynamic. As a first guess the value can be adjusted so that a 100% change of the controller output is possible within one oscillation period. E.g. if the oscillation period of the instable or nearly instable control loop is 0.5 min then LMN_DEL can be set to 200. If the controller is acting too sluggish on the process then the following actions can be taken (in that order): Increase the change velocity (LMN_DEL) of the manipulated variable (controller output) or set it to 0 to disable the limitation completely. Increase the sensitivity factor (SENS) step by step if necessary up to 150. Page 25 of 25