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Control Systems and Adaptive Process . Regulators and Communication

Control Systems and Adaptive Process . Regulators and Communication. Adaptive Predictive Expert Control.

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Control Systems and Adaptive Process . Regulators and Communication

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  1. Control Systems and Adaptive Process . Regulators and Communication

  2. Adaptive Predictive Expert Control • PID controllers are present in the majority of control process. However, these controllers require manual adjustments that sometimes are not completely effective for tuning the controller to perform the required function. • Later were developed the so called expert systems, with which the adjustment task was more efficient. This type of systems should be designed specifically for each process, being very sensitive to changes that occur on this and, therefore, difficult and expensive to develop and maintain. • In the 70’s were conceived the adaptive predictive control principle, whose innovation is to make the predicted process output equal to the desired output,using a prediction model that can be automatically adjusted in real time (adaptive). This type of control may decrease in efficiency with the evolution of the process because it becomes less effective in adaptation.

  3. Adaptive Predictive Expert Control • In 2001 it was patented a new methodology combining adaptive predictive control with expert control. As a first step, in this model a series of set points values are introduced in a block which generates the desired output. This desired output is introduced into the predictive model, generating the control signal. The control signal in turn manages the adaptive model, which feeds back both: the generation of the desired signal, informing about the deviation of the output signal over the desired signal and proceeding this block to generate a new desired output, and the model predictive, to make that the prediction error is minimized. Furthermore the expert block acts on the generation of the desired signal, the predictive and the adaptive models, changing the operating conditions according the needs of the process.

  4. Adaptive Predictive Expert ControlPredictive control • In real life, the plants to control have delays or dead times caused by delays in sensors and actuators, the number of systems connected in serial etc., which generating complications for control systems due to delays in the detection of disturbances and/or the control action, or this action is done on the basis of errors that have become obsolete and are not updated. This causes a mismatch in the response as greater as higher is the frequency, causing that the gain becomes reduced and closed-loop instability can be reached. One solution to try to resolve this problem is to detune the PID control, but this is not an appropriate solution.

  5. Adaptive Predictive Expert ControlPredictive control • The concept of predictive control was introduced in 1974 and can be defined as the "control that, based on a model of the process, make that the dynamic output of the process, predicted by a model, be equal to the desired dynamic output, adequately chosen".

  6. Adaptive Predictive Expert ControlPredictive control: Smith predictor • The Smith predictor tries to solve the problem of the control in systems with delay. In this case, what is intended is to create a predictive model of the output (process) and use it for the feedback, eliminating in this mode the delay. • In a process with a delay e-tms, from the control action Gc(s) is generated the predictive model Gm(s); on one hand, to this predictive model is applied the plant delay and is feed backed with the plant, obtaining the prediction error e(t), and the other is feed backed with e(t) so that the prediction error is removed from the predictive model.

  7. Adaptive Predictive Expert ControlPredictive control: Smith predictor • The model of the plant in this case is Gm(s)e-tms. The closed loop transfer function of the system will be: • As you can appreciate, in the delay e-tms the module is one. This implies neither gain nor attenuation, but his phase is -tmω, therefore, with higher frequency greater mismatch. Introducing so delay, that the gain margin is reduced and may cause system instability. • The Smith predictor model is an advanced control structure very popular to solve the problem of the control in systems with delay. It was developed for continuous systems, but is more adequate for digital systems.

  8. Adaptive Predictive Expert ControlPredictive control: Smith predictor • In the Smith predictor a predictive model is generated but this model can be erroneous. In this case, the modelling error will be: and the closed loop transfer function will be: • It is observed that the modelling error affects to the characteristic polynomial and thus to the closed loop stability. However, as the term in the denominator that depends on the modelling error is also affected by Gc(s), the problem can be corrected by detuning the controller. • The Smith predictor model needs a correct tuning. For this, the controller can be a PI type because the system without delay is a fast loop, wherein Ti is selected so as to allows cancel the pole of the process and Kc according to the time constant desired for the process .

  9. Adaptive Predictive Expert ControlPredictive control: Smith predictor • PI Predictor: is a simplification of the Smith predictor, applicable to processes whose dynamic is so fast compared to the delay that can be approximated by a gain plus a delay. It has a plant model equal than Smith predictor Gm(s)e-tms, and a "fast" model Gm(s)=Kp

  10. Adaptive Predictive Expert ControlPredictive control: Smith predictor • PI predictive control: is other simplification of the Smith predictor. It is used in processes with long delay. Is a first order model with a PI controller. It has fewer tuning parameters.

  11. Adaptive Predictive Expert Control Adaptive techniques • The predictive model is an incomplete solution because it cannot predict natural changes that occur in the variables o disturbances of the operation environment of the real processes and that modify its dynamic, therefore, the accuracy in the prediction of the output process from a fixed parameter model cannot be guaranteed. This implies that it is necessary to adapt the predictive model to these variations and disturbances to achieve more satisfactory control. • When variations in the dynamic of the process are not predictable the techniques described above are not enough, being necessary to resort to adaptation. • The adaptive techniques emerged to treat processes that varying in time or were subjected to different operating conditions. • There are several adaptive techniques, among which are automatic tuning, gain scheduling and adaptation.

  12. Adaptive Predictive Expert Control Adaptive techniques • Automatic tuning: it is widely used in PID controllers. The automatic tuning can be incorporated into the controller itself or in external devices connected to the control loop only during the tuning process. In the latter case it is necessary to provide to these devices specifics characteristics of the controller used, so it must be designed to work with controllers from different manufacturers. • Gain scheduling: is a technique used in nonlinear processes, processes with variations in time or those with variable operating conditions. For proper use is necessary to find a number of measurable variables that are representative of the process. This technique, combined with automatic tuning simplifies greatly the design work as allows to measure the variable in different operating conditions, store these values ​​in a table and, through auto-tuning, determine the parameters of the controller when the system works around that point of operation.

  13. Adaptive Predictive Expert Control Adaptive techniques • The automatic tuning is the method by which the controller automatically tunes or under user request and is based on adaptive technique. The most of the commercial controllers incorporate some method of automatic tuning (auto-tuning). This process consists of three steps: - Generation of a disturbance that allows to know the dynamics of the process. - Assessment of response to this disturbance. - Calculation of the controller parameters. • There are two types of tuning according to the approach used: the model-based methods and rule-based methods.

  14. Adaptive Predictive Expert Control Adaptive techniques There are several methods of automatic tuning, here are presented some of them: • Method of step or pulse response: Once the auto-tuning function is activated, the controller switches to manual control and apply a step or pulse to the manipulated variable. According to the response of the process, identifies a first order model with delay, from which calculates, using tables, the new controller tuning. • Relay method: If the auto-tuning function is activated, a relay is connected instead of PID, which causes controlled oscillations in the process, so as to allows the identification of the dynamic characteristics of the process.

  15. Adaptive Predictive Expert Control Adaptive techniques • EXACT method: its name derives from Exact Adaptive Controller Tuning. The tuning is done continuously in closed loop. If the error exceeds certain limits, a new model of the process is identified by recognizing preset patterns. The controller calculates the new tune in real time using modified tables of Ziegler-Nichols and preset rules. Not all bad behaviours of a control loop can be corrected by automatic tuning because in many cases those bad behaviours may be due of inappropriate design, poor placement of sensors/actuators, wear of some elements etc., thus the automatic tuning, if not used properly, can lead to erroneous results.

  16. Adaptive Predictive Expert Control Adaptive techniques • Adaptive Control: adaptation is the mechanism by which the controller is capable of changing its parameters to respond to a change in operating conditions or in characteristics of the process. With this method the controller parameters are automatically adapted to upgrade with respect to the changing characteristics of the process.

  17. Adaptive Predictive Expert Control Adaptive techniques The most common types of adaptive control systems are: • Adaptive control programmed: consists in the programming before use of the changes required in the controller so that it can adapt to different situations in which it has to operate. Therefore is needed to know in advance the process and to have a quantification of how the controller parameters must be changed in base to known changes in the characteristics of the process. One way to do it would be by building tables with the values that should take the controller parameters at different values of the input and output variables of the system.

  18. Adaptive Predictive Expert Control Adaptive techniques • Adaptive control with reference model: the key component in this type of adaptive control is the reference model. Each reference model should reflect how the system should respond to the changes that occur. The adaptation program is usually an algorithm that optimizes the parameters of a given objective function for control.

  19. Adaptive Predictive Expert Control Adaptive techniques • Auto-tuning adaptive control: in these controllers are taken continuously values of the input and output to online and recursively estimate the values of parameters of an approximate model of the process. Thus, the changes that occur over time in the real system (nonlinear) are modelled by a linear process, whose parameters are changing over time to fit as much as possible to the real system. Since the estimation of the model will determine the effectiveness of the controller, the most important aspect of these controllers is to have a technique for parameter estimation sufficiently robust to obtain good results.

  20. Adaptive Predictive Expert Control Model-based methods The automatic tuning methods are done based on a reference from a process model. There are several types of models, including those based on transient response, frequency response and parameter estimation. • Transient response methods: can be based on the analysis of the open loop response, usually for a pre-tuning of the control system, or in closed loop, which will represent better the response of the control system in real conditions of use. The most are based on the analysis of the response to a step or pulse signal, although other type of disturbances can be used.

  21. Adaptive Predictive Expert Control Model-based methods • Frequency response methods: usually are used to determine the dynamic of the process. To know the appropriate frequency for his analysis can use the relay method which leads the system to an limit oscillation. By varying the amplitude and relay hysteresis allows knowing several points of the Nyquist plot. Another way to analyze the frequency response is through the online method, in which is introduced a band pass filter that allows to determine the content of the signal at various frequencies and thus determine the corresponding points in the Nyquist diagram. • Parameter estimation methods: it consists in to estimate a number of parameters for determining a low order model with which to calculate the parameters of the controller. This type of auto tuners can work by varying the parameters thus they can work as adaptive controllers that vary their parameters continuously. One of its main advantages is that they do not require a specific type of excitation signal, in contrast, usually need a pre-tune that can be performed according with previous methods.

  22. Adaptive Predictive Expert Control Rule-based methods • With these methods we try to imitate manual tuning based on previous experience. It performs the same process for the analysis of the system response to a disturbance that in model-based methods, but the adjustment of controller parameters is done not in function of a model, but in function to some known rules. • To carry out the automatic tuning with these methods it is necessary to know a series of response parameters that allow to determine the appropriate rule to carry out the adjustment (overshoot, decay ratio, etc..). However, although it is easy to determine rules to decide whether these parameters should be changed, is not so much to know in what percentage should be done this modification, so they are most suitable methods for continuous adaptive systems, where are done small changes in the adjustment with each transient. They have the disadvantage that, if there are two changes in the set point or in load disturbance in short time, can result in an erroneous tuning rule.

  23. Adaptive Predictive Expert Control Expert control • The adaptive predictive control has proven a satisfactory performance in a variety of processes, whenever there is an cause-effect relationship that determines the dynamic behaviour of the process and that it can be identified and managed by a model. However this is not always so. Sometimes it happens that the output variable can reach a situation in which their behaviour is not due to a cause and effect determined, usually in the operating extreme margins of the working variable, so that cannot be identified by a process model. Accordingly, the adaptive predictive control, as other methods, there are limits of application.

  24. Adaptive Predictive Expert Control Expert control • The expert control allows to make use of the prior knowledge that we have of the process to carry out their control, usually by the application of rules, in this way the controller can take decisions over the control to improve their function. In this way we can know the behavior of the plant in certain situations that elude the adaptive predictive control. The system must be able to interpret the information received from the plant and decide what actions should be carried out in base to the known rules. The complexity of the system is in how it makes the process of decision making. • As improve of the expert control has been developed the adaptive predictive expert control(ADEX), which combine the prediction of the control signal with the adaptation and the expert control rule-based in function of the response needs.

  25. Adaptive Predictive Expert Control • For an operating range of the working variable, the ADEX control divides this range in different domains of adaptive predictive control (AP) and expert control (EX), applying the appropriate control to each domain. In the AP domains the dynamic of the process can be identified by an adaptive control. In the experts domains the manual control can be more robust and efficient than adaptive control.

  26. Adaptive Predictive Expert Control • As shown in the block diagram of ADEX control, there is a expert block that can determine and modify the operation of the control block, the driver block and the adaptive mechanism based on the information it receives from process variables. • The control block initially will act with a predictive model, calculating the predictive control to apply. If it is necessary to apply an expert control, this block will act as a rule-based system, mimicking the actions of a human operator. • The driver block receives the relevant indications from the expert block to determine performance criteria to generate the most suitable path in adaptive domains. For example, depending on the distance at which it be the domain of the set point may be preferred a fast or slow change in the process.

  27. Adaptive Predictive Expert Control • The adjustment mechanism will receive from expert block the indication of when could be interesting to activate or not the adaptation of the AP model parameters according to the operating conditions. For example, if significant changes on the dynamic of the process are known in different domains of operation, the expert block can help to the adaptation mechanism in the choice of AP model parameters when there is a change in the process output between different domains.

  28. References Bibliography • J. M. Martín, Control Adaptativo Predictivo Experto. • Nevado, Conceptos Básicos de Filtrado, Estimación e Identificación.

  29. References Interesting links • http://www.control-class.com/Tema_8/Slides/Tema_8_Predictor_de_Smith.pdf • http://www.ie.itcr.ac.cr/einteriano/control/Laboratorio/5.EjemploPredictorSmith.pdf • http://dea.unsj.edu.ar/control3/teor%C3%ADa/capitulo6_predictor.pdf • http://www.mathworks.es/es/products/control/examples.html?file=/products/demos/shipping/control/smithdemo.html • http://www.enm.bris.ac.uk/anm/workshop-a/programme/control-talks/gawthrop.pdf • http://es.scribd.com/doc/6631155/T8-Estructuras-Avanzadas-Predictor-Smith • http://www.isa.cie.uva.es/~maria/pids.pdf • http://www.uclm.es/profesorado/marodrigo/pdf/cap/cap5.pdf • http://web.usal.es/~sebas/PRACTICAS/PRACTICA%207.pdf

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