Feedback control method

Unlike SPC techniques, standard feedback control methods such as PID-control, do exert control upon a process, in an effort to minimize y, — yk. Control in Statistical Process Control is as such not regulatory control, but a semantic means of relating SPC to quality control—a means that often leads to the hybrid term SQC. Ogunnaike and Ray [14, Sec. 28.4] offer advice on when to use SPC and when to use standard feedback control methods When the sampling interval is much greater than the process response time, when zero-mean Gaussian measurement noise dominates process disturbances, and when the cost of regulatory control action is considerable, SPC is preferred. 

The measurement of compounds in bioprocesses, including fermentations, using conventional laboratory techniques such as HPLC, TLC or calorimetric assays is often tedious, invasive, requires sample handling and difficult to do in real time. For a bioprocess where it is important to gain information about the reactor status for feedback control, methods enabling rapid and reliable measurement of components are desirable. 

Driving an automobile safely requires a large amount of individual skill. Even if not generally recognized, the driver needs an intuitive ability to utilize feedforward and feedback control methods. 

The traditional approach for process monitoring is to compare measurements against specified limits. This limit checking technique is a standard feature of computer control systems and is widely used to validate measurements of process variables such as flow rate, temperature, pressure, and liquid level. Process variables are measured quite frequently with sampling periods that typically are much smaller than the process setthng time (see Chapter 17). However, for most industrial plants, many important quality variables cannot be measured on-line. Instead, samples of the product are taken on an infrequent basis (e.g., hourly or daily) and sent to the quality control laboratory for analysis. Due to the infrequent measurements, standard feedback control methods fike PID control cannot be applied. Consequently, statistical process control techniques are implemented to ensure that the product quality meets the specifications. 

The effect of the disturbance on the controlled variable These models can be based on steady-state or dynamic analysis. The performance of the feedforward controller depends on the accuracy of both models. If the models are exac t, then feedforward control offers the potential of perfect control (i.e., holding the controlled variable precisely at the set point at all times because of the abihty to predict the appropriate control ac tion). However, since most mathematical models are only approximate and since not all disturbances are measurable, it is standara prac tice to utilize feedforward control in conjunction with feedback control. Table 8-5 lists the relative advantages and disadvantages of feedforward and feedback control. By combining the two control methods, the strengths of both schemes can be utilized. 

Feedback control can never be perfect as it only reacts to the disturbances which are already measured in the system output. The feed-forward method tries to eliminate this drawback by an alternative approach. Instead of using the process output, the measured variable is taken as the measured inlet disturbances and its effect on the process is anticipated via the use of a model. The action is taken on the manipulated variable using the model to relate the measured variable at the inlet, the manipulated variable and the process output. The success of this control strategy depends largely on the accuracy of the model prediction, which is often imperfect as models can rarely predict the... 

While the decrease in extraction time is favourable for laboratories in general, it can be critical when laboratory analyses are used in feedback control of production cycles and quality control of manufacturing processes. The volume of solvents used in PFE can be some 10 times less than traditional extraction methods (cf. Table 3.36). PFE cuts solvent consumption by up to 95 %. Because so little solvent is used, final clean-up and concentration are fast direct injection in analytical devices is often possible. Automated PFE systems can extract up to 24 sample cells. 

In the above-described measurement, which we call the absolute method, all pumps have equal speeds (rpm) owing to interconnection to the same drive-shaft. In order to express, if required, a deviation registered for the analyte concentration, one must calibrate with a standard by varying its rpm (B) with respect to that of the titrant (A) a B/A rpm ratio greater than unity means a proportionally lower concentration and vice versa. In general, the absolute method serves to control a sample stream with nearly constant analyte concentration as a sensor one uses not only electroanalytical but often also optical detectors. However, with considerably varying analyte concentrations the differential method is more attractive its principle is that in the set-up in Fig. 5.15 and with the sensor adjusted to a fixed and most sensitive set-point, the rpm of the sample stream (C) is varied with respect to that of the titrant (A) by a feedback control (see Fig. 5.3a) from the sensor via a regulator towards the... 

Recently there has been a growing emphasis on the use of transient methods to study the mechanism and kinetics of catalytic reactions (16, 17, 18). These transient studies gained new impetus with the introduction of computer-controlled catalytic converters for automobile emission control (19) in this large-scale catalytic process the composition of the feedstream is oscillated as a result of a feedback control scheme, and the frequency response characteristics of the catalyst appear to play an important role (20). Preliminary studies (e.g., 15) indicate that the transient response of these catalysts is dominated by the relaxation of surface events, and thus it is necessary to use fast-response, surface-sensitive techniques in order to understand the catalyst s behavior under transient conditions. 

Integrating chemical analysis methods and physical sensors with microreactors enables monitoring of reaction conditions and composition. This ability renders instrumented microreactors powerful tools for determining chemical kinetics and identifying optimal conditions for chemical reactions. The latter can be achieved by automated feedback-controlled optimization of reaction conditions, which greatly reduces time and materials costs associated with the development of chemical synthesis procedures. 

There are a variety of feedback controller tuning methods. Probably 80 percent of all loops are tuned experimentally by an instrument mechanic, and 75 percent of the time the mechanic can guess approximately what the settings will be by drawing on experience with similar loops. We will discuss a few of the time-domain methods below. In subsequent chapters we will present other techniques for tinding controller constants in the Laplace and frequency domains. 

In Chap. 12 we will learn this new Chinese language (including several dialects Bode, Nyquist, and Nichols plots). In Chap. 13 we will u.se frequency-domain methods to design closedloop feedback control systems. Finally, in Chap. 14, we will briefly discuss some frequency-domain and other methods for experimentally identifying a process. 

The design of feedback controllers in the frequency domain is the subject of this chapter. The Chinese language that we learned in Chap. 12 is now put to use to tune controllers. Frequency-domain methods are widely used because they have the significant advantage of being easier to use for high-order systems than the time- and Laplace-domain methods. 

The use of feedback-control techniques to modulate combustion processes in propulsion systems has recently received extensive attention [1-3]. Most of the previous studies involved direct implementation of existing control methods designed for mechanical devices, with very limited effort devoted to the treatment of model and parametric uncertainties commonly associated with practical combustion problems. It is well established that the intrinsic coupling between flow oscillations and transient combustion responses prohibits detailed and precise modeling of the various phenomena in a combustion chamber, and, as such, the model may not accommodate all the essential processes involved due to the physical assumptions and mathematical approximations employed. The present effort attempts to develop a robust feedback controller for suppressing combustion instabilities in propulsion systems. Special attention is given to the treatment of model uncertainties. Various issues related to plant... 

The outline of this paper is as follows. First, a theoretical model of unsteady motions in a combustion chamber with feedback control is constructed. The formulation is based on a generalized wave equation which accommodates all influences of acoustic wave motions and combustion responses. Control actions are achieved by injecting secondary fuel into the chamber, with its instantaneous mass flow rate determined by a robust controller. Physically, the reaction of the injected fuel with the primary combustion flow produces a modulated distribution of external forcing to the oscillatory flowfield, and it can be modeled conveniently by an assembly of point actuators. After a procedure equivalent to the Galerkin method, the governing wave equation reduces to a system of ordinary differential equations with time-delayed inputs for the amplitude of each acoustic mode, serving as the basis for the controller design. 

Another method is to apply feedback control as in Figure 7.1, but to periodically interrupt the circuit for a brief moment so that the magnitude of iRu can be determined and a correction applied. Several variations of such a control system have been published, and one version is available as an accessory for a potentiostat. The outstanding feature of these approaches is that Ru does not have to be known and it can vary during an experiment with only minor decrease in control accuracy. In many situations, the brief interruption of the feedback loop is a small price to pay for such convenience. Microprocessors will foster further developments in this direction the advent of digital potentio-stats is imminent. 

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