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SISO system

As with a SISO system, a sensitivity function may be defined... [Pg.315]

We use a simple liquid level controller to illustrate the concept of a classic feedback control system.1 In this example (Fig. 5.1), we monitor the liquid level in a vessel and use the information to adjust the opening of an effluent valve to keep the liquid level at some user-specified value (the set point or reference). In this case, the liquid level is both the measured variable and the controlled variable—they are the same in a single-input single-output (SISO) system. In this respect, the controlled variable is also the output variable of the SISO system. A system refers to the process which we need to control plus the controller and accompanying accessories such as sensors and actuators.2... [Pg.82]

We first establish the closed-loop transfer functions of a fairly general SISO system. After that, we ll walk through the diagram block by block to gather the thoughts that we must have in synthesizing and designing a control system. An important detail is the units of the physical properties. [Pg.88]

In a SISO system, we manipulate only one variable, so we must make a decision. Since our goal is to control the tank temperature, it would be much more sensible to manipulate the steam temperature TH instead of the inlet temperature. We can arrive at this decision with physical... [Pg.88]

Our analyses of SISO systems seldom take into account simultaneous changes in set point and load.2 We denote the two distinct possibilities as... [Pg.90]

After we have chosen the controlled and manipulated variables, the remaining ones are taken as load variables in a SISO system. [Pg.90]

We now return to the use of state space representation that was introduced in Chapter 4. As you may have guessed, we want to design control systems based on state space analysis. State feedback controller is very different from the classical PID controller. Our treatment remains introductory, and we will stay with linear or linearized SISO systems. Nevertheless, the topics here should enlighten( ) us as to what modem control is all about. [Pg.171]

There are many advanced strategies in classical control systems. Only a limited selection of examples is presented in this chapter. We start with cascade control, which is a simple introduction to a multiloop, but essentially SISO, system. We continue with feedforward and ratio control. The idea behind ratio control is simple, and it applies quite well to the furnace problem that we use as an illustration. Finally, we address a multiple-input multiple-output system using a simple blending problem as illustration, and use the problem to look into issues of interaction and decoupling. These techniques build on what we have learned in classical control theories. [Pg.189]

It is apparent from Eq. (10-22) that with interaction, the controller design of the MIMO system is different from a SISO system. One logical question is under what circumstances may we make use of SISO designs as an approximation Or in other words, can we tell if the interaction may be weak This takes us to the next two sections. [Pg.203]

After proper pairing of manipulated and controlled variables, we still have to design and tune the controllers. The simplest approach is to tune each loop individually and conservatively while the other loop is in manual mode. At a more sophisticated level, we may try to decouple the loops mathematically into two non-interacting SISO systems with which we can apply single loop tuning procedures. Several examples applicable to a 2 x 2 system are offered here. [Pg.207]

We will simply state that the SISO system design tool sisotool, as explained in Session 6, can be used to do frequency response plots. Now, we want to use the default view, so we just need to enter ... [Pg.251]

The DMC discussed in this chapter is for a SISO system. We will say more about DMC in Chap. 17 since this methodology is fairly easily extended to multi-variable systems, which is where its real potential usefulness occurs. [Pg.288]

It is clear that the matrix equation [Eq. (15.64)] is very similar to the scalar equation describing a closedloop system derived back in Chap. 10 for SISO systems. [Pg.555]

The usual way to use the Nyquist stability criterion in SISO systems is to not plot 1 + and look at encirclements of the origin. Instead we simply... [Pg.564]

In a SISO system we normally make a Nyquist plot of the total openloop transfer function B. If the system is closedloop stable, the (— 1,0) point will not be encircled positively (clockwise). Alternatively, we could plot l/Gu B). This inverse plot should encircle the (— 1, 0) point negatively (counterclockwise) if the system is closedloop stable. See Fig. 16.4. [Pg.579]

In multivariable systems the question of robustness is very important. One method developed by Doyle and Stein (IEEE Trans., 1981, Vol. AC-26, p. 4) is quite usefiil and easy to use. It has the added advantage that it is quite similar to the maximuni closedloop log modulus criterion used in SISO systems. [Pg.585]

A. SCALAR SISO SYSTEMS. Remember in the scalar SISO case we looked at the closedloop servo transfer function G B/ll + GuB). The peak in this curve, the maximum closedloop log modulus L (as shown in Fig. 16.9a), is a measure of the damping coefficient of the system. The higher the peak, the more underdamped die system and the less margin for changes in parameter values. Thus, in SISO systems the peak in the closedloop log modulus curve is a measure of robustness. [Pg.585]

Skogestad and Morari recommend the use of uncertainty models for the design of robust controllers. The idea is easy to visualize for an SISO system. Suppose we have a process with the following openloop transfer function ... [Pg.588]

So the multiloop SISO diagonal controller remains an important structure. It is the base case against which the other structures should be compared. The procedure discussed in this chapter was developed to provide a workable, stable, simple SISO system with only a modest amount of engineering effort. The resulting diagonal controller can then serve as a realistic benchmark, against which the more complex multivariable controller structures can be compared. [Pg.595]

One of the major questions in multivariable control is how to tune controllers in a diagonal multiloop SISO system. If PI controllers are used, there are 2N tuning parameters to be selected. The gains and reset times must be specified so that the overall system is stable and gives acceptable load responses. Once a consistent and rational tuning procedure is available, the pairing problem can be attacked. [Pg.599]

In theory, the internal model control methods discussed for SISO systems in Chap. 11 can be extended to multivariable systems (see the paper by Garcia and Morari in lEC Process Design and Development, Vol. 24, 1985, p. 472). [Pg.609]

Pitchfork bifurcation of Yrd versus Kc for the closed-loop SISO system with simple... [Pg.469]

Pole and zero placement using a dynamic compensator for an SISO system can be accomplished by specifying analytically the closed loop servo response (e.g., first or second order with deadtime). Suppose that the specified response is defined by P(s) solving the closed loop equation (5) yields an analytical... [Pg.103]

The minimum variance control for an SISO system finds the unrestricted minimum of the expected value of a quadratic objective function ... [Pg.106]

Various performance indices have been suggested [54, 53, 149, 20, 148] and several approaches have been proposed for estimating the performance index for SISO systems, including the normalized performance index approach [53], the three estimator approach [175[, and the filtering and correlation analysis (FCOR) approach [115[. A model free approach for linear quadratic CPM from closed-loop experiments that uses spectrum analysis of the input and output data has been suggested [136]. Implementation of SISO loop based CPM tools for refinery-wide control loop performance assessment has been reported [294]. [Pg.234]

In the chemical industry most of the processing systems are multiple-input, multiple-output systems. Since the design of SISO systems is simpler, we will start first with them and progressively cover the design of MIMO systems. [Pg.20]

What is a SISO system and what is a MIMO system Give examples from the chemical engineering field for both. [Pg.27]


See other pages where SISO system is mentioned: [Pg.724]    [Pg.232]    [Pg.251]    [Pg.317]    [Pg.7]    [Pg.80]    [Pg.211]    [Pg.251]    [Pg.571]    [Pg.550]    [Pg.584]    [Pg.585]    [Pg.12]    [Pg.97]    [Pg.99]    [Pg.100]    [Pg.12]    [Pg.548]    [Pg.887]    [Pg.20]    [Pg.29]    [Pg.596]   


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