Single input-multiple output

A SISO process is a single input-single output process and corresponds to transformation processes or substitution reactions. MISO processes (multiple inputs-single output) comprise convergent processes and SIMO processes (single inputs-multiple output) comprise divergent processes similar to decomposition and synthesis, respectively. MIMO... 

There are three dominant network motifs found in E. coli (Shen-Orr et al., 2002) (i) a feedforward loop, in which one transcription factor regulates another factor, and, in turn, the pair jointly regulates a third transcript factor (ii) a single-input multiple-output (SIMO) block architecture and (iii) a multiple-input multiple-output (MIMO) block architecture, referred to as a densely overlapping regulon by biologists. 

Interaction can be between two or more processes or between actions produced by different control loops applied to a single process. The former has already been discussed in Section 1.53. Some degree of interaction between control loops will nearly always occur in a multiple-input/multiple-output (MIMO) system. For example, consider the distillation process described in Section 7.3 (Fig. 7.9). Suppose it is desired to control simultaneously the compositions of both the overheads product stream (by manipulating the reflux flowrate) and the bottoms product stream (by regulating the steam flowrate to the reboiler). A typical arrangement is shown in Fig. 7.73. 

Empirical Model Identification. In this section we consider linear difference equation models for characterizing both the process dynamics and the stochastic disturbances inherent in the process. We shall discuss how to specify the model structure, how to estimate its parameters, and how to check its adequacy. Although discussion will be limited to single-input, single-output processes, the ideas are directly extendable to multiple-input, multiple-output processes. 

Depending on how many controlled outputs and manipulated inputs we have in a chemical process, we can distinguish the control configurations as either single-input, single-output (SISO) or multiple-input, multiple-output (MIMO) control systems. 

Kiranoudis et al. [20] developed a dynamic model for the simulation of conveyor-belt dryers and proposed a SISO (single-input, single output) control scheme for the regulation of material moisture content. In a subsequent work, Kiranoudis et al. [21] extended the dynamic model of this process to include MIMO (multiple-input, multiple output) schane to control the material moistme content and temperature. In both works, PI controllers were appropriately tuned and nonlinear simulations were performed. 

Figure 21.1 shows the block diagram for a multiple-input, multiple-output (MIMO) process to be controlled by two single-loop controllers. Having closed one of the loops (yi — ,), the controller in the second loop, which manipulates 2 based on the feedback of y2, must be tuned. A desirable feature of the process, as seen by this controller, is to have the effective process gain remain invariant, regardless of the action of the other control loop. 

In the examples of controlled processes that we have been discussing, we have worked with systems handling one resource and with one target variable, i.e., single-input/single-output (SISO) systems. However, in actual practice, the process is affected by multiple input variables and has multiple target variables, i.e., a multiple-input/multiple-output (MIMO) system. 

Chemical and biochemical units with multiple-input multiple-output (MIMO) and with multiple reactions (MRs) for all of the above-mentioned systems are also covered. Nonreacting systems and single-input single-out-put (SISO) systems are treated as special cases of the more general MIMO, MR cases. The systems approach helps to establish a solid platform on which to formulate and use these generalized models and their special cases. 

MBC-Tool an environment facilitating the testing of conventional and advanced model based control strategies within Matlab. It includes feed-forward control, cascade control, delay compensation, IMC control as well as decoupling control techniques. It can be used for both single-input single output and multi input multiple output processes and allows the computation of different robustness measures to compare alternative designs. 

Another general characteristic of multistage columns is that they usually require controUing several interrelated variables using a number of interrelated manipulated variables. This is a multiple-input, multiple-output (MMO) problem as compared with the basic control action in a single-input, singleoutput (SISO) problem. In a multiple variable process each manipulated variable may affect more than one controlled variable due to process interactions. The multiple variable control problem in multistage columns may be handled either by multiple control loops or by a multivariable controller. 

Tactic 3 Systems such as distillation columns are composed of multiple unit operations with a single input or output stream It is sometimes necessary to consider such equipment combinations as blocks before implementing Tactics 1 and 2. 

For situations where there was no single input or output stream, sj tems containing multiple unit operations were created. The tracing techniques for these conpound systems did not provide the information needed to determine the internal flows for these systems. In order to determine reflux ratios for columns, for example, the process flow table must be consulted. 

Here, Aw(fc) is the inferred change of output and p is the gain factor of the controller. Single or multiple inputs can be used and single or multiple outputs can be chosen depending on the control problem. 

MIMO (multiple input, multiple output) process modeling is inherently more complicated than SISO modeling. For linear systems, the Principle of Superposition holds, which allows MIMO models to be developed through a series of single step tests for each input, while holding the other inputs constant. For a process with three inputs (n) and three outputs (y), we can introduce a step change in and record the responses for yi, y2, and 3. The three transfer functions involving u, namely... 

In previous chapters, we have emphasized control problems that have only one controlled variable and one manipulated variable. These problems are referred to as single-input, single-output (SISO), or single-loop, control problems. But in many practical control problems, typically a number of variables must be controlled, and a number of variables can be manipulated. These problems are referred to as multiple-input, multiple-output (MIMO) control problems. For almost all important processes, at least two variables must be controlled product quahty and throughput. 

The model is obtained through the approach of a set of linear transfer functions that includes the nonlinearities of the whole system. The parametric identification process is based on black box models (Ljung, 1987 Norton, 1986). The nonholonomic system dealt with in this work is considered initially to be a MIMO (Multiple-Input Multiple-Output) system, as shown in Figure 4, due to the d5mamic influence between two DC motors. This MIMO system is composed of a set of SISO (single input single output) subsystems with coupled connections. 

In this section a systematic approach is proposed to design the control structures for these three types of reactive distillation flowsheets. Because all five reactive distillation systems (Table 7.5) have almost equal molar feedflows (neat flowsheet), the stoichiometric balance has to be maintained. Here we adjust the feed ratio to prevent accumulation of unreacted reactants attributable to stoichiometric imbalance. The next issue is, how many product compositions or inferred product purities should be controlled For the esterification reactions with A -f B C + D with a neat flowsheet, controlling one-end product purity implied a similar purity level on the other end, provided the product flowrates are equal. Thus, a single-end composition (or temperature control) is preferred. This leads to 2 x 2 multivariable control, as opposed to a 3 x 3 multiple-input-multiple-output system. The... 

The next task is to seek a model for the observer. We stay with a single-input single-output system, but the concept can be extended to multiple outputs. The estimate should embody the dynamics of the plant (process). Thus one probable model, as shown in Fig. 9.4, is to assume that the state estimator has the same structure as the plant model, as in Eqs. (9-13) and (9-14), or Fig. 9.1. The estimator also has the identical plant matrices A and B. However, one major difference is the addition of the estimation error, y - y, in the computation of the estimated state x. 

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