Design representation space

We could say that a design representation space (DRS) is an equivalent of our background knowledge (BK), but in this case our BK is presented in a formal and systematic way, which is useful for both the human designers and for various computer tools. A DRS has two major parts the symbolic and the numerical. The symbolic part contains all the symbolic attributes and their feasible values, while the numerical part contains all the numerical attributes and their specific feasible values or their feasible ranges of variation. 

DRS can be presented as a table with rows representing individual attributes and their values. Such a form is useful for design purposes and is consistent with one of the most popular inventive designing methods, which is 

For example, we would like to represent our knowledge about a class of small objects of various shapes. In this case, we could use, for example, only three symbolic attributes and one numerical attribute. Our first symbolic attribute is 

Al Material and it defines the kind of material used with four feasible values steel, concrete, wood, and plastic. The second attribute A2 Shape is also symbolic. It describes the shape of our object with the three feasible values of cube, cuboid, and cone. The last symbolic attribute, A3 Homogeneity, determines if our object is solid or hollow. The attribute A4 Height is a numerical attribute with three specific numerical values. Obviously, more specific values could be used for the individual attributes, depending on our needs (Table 4.1). 

The following example represents a specific combination of four values 

---

Design representation space is an organized collection of attributes and their feasible values, which is necessary and sufficient to describe all known designs and has a potential for finding many new and unknown designs. It represents the State of the Art of knowledge in the problem domain. 

Table 4.1 Design representation space for a class of small objects...

These attributes and their values are shown in a conceptual design representation space below. A combination of values describing a truss is shown in the table as bolded words. Obviously, a steel truss is a steel structure with straight members that are connected by hinges that is, a steel truss is described by a specific combination Al Material = Steel, A2 Member Shape = Straight, and A3 Connection Type = Hinged (Table 4.2). 

Very rarely, if ever, only three stages are sufficient to produce meaningful results. Usually, several hundred stages are sufficient, but sometimes tens of thousands of stages are necessary for complex design problems associated with a large design representation space that needs to be searched. 

All subproblems and their solutions can be represented in a systematic way in a single table with a number of rows and columns. Such a table becomes the problem s design knowledge representation space. 

The morphological table can be interpreted as a design knowledge representation space and can be also used for various AI applications. We could say that learning morphological analysis is a natural introduction to AI in engineering. 

Chemoinformatics (or cheminformatics) deals with the storage, retrieval, and analysis of chemical and biological data. Specifically, it involves the development and application of software systems for the management of combinatorial chemical projects, rational design of chemical libraries, and analysis of the obtained chemical and biological data. The major research topics of chemoinformatics involve QSAR and diversity analysis. The researchers should address several important issues. First, chemical structures should be characterized by calculable molecular descriptors that provide quantitative representation of chemical structures. Second, special measures should be developed on the basis of these descriptors in order to quantify structural similarities between pairs of molecules. Finally, adequate computational methods should be established for the efficient sampling of the huge combinatorial structural space of chemical libraries. 

This tutorial looks at how MATLAB commands are used to convert transfer functions into state-space vector matrix representation, and back again. The discrete-time response of a multivariable system is undertaken. Also the controllability and observability of multivariable systems is considered, together with pole placement design techniques for both controllers and observers. The problems in Chapter 8 are used as design examples. 

Figure 9-5. Schematic representation of mixing space. (Source Nauman, E. G., Chemical Reactor Design, John Wiley Sons, 1987.)...

We have reviewed here, in the brief space available, some recent developments in phase equilibrium representations for polymer solutions. With these recent developments, reliable tools have become available for the polymer process designer to use in considering effects of phase equilibrium properly. 

We now finally launch into the material on controllers. State space representation is more abstract and it helps to understand controllers in the classical sense first. We will come back to state space controller design later. Our introduction stays with the basics. Our primary focus is to learn how to design and tune a classical PID controller. Before that, we first need to know how to set up a problem and derive the closed-loop characteristic equation. 

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. 

When we used root locus for controller design in Chapter 7, we chose a dominant pole (or a conjugate pair if complex). With state space representation, we have the mathematical tool to choose all the closed-loop poles. To begin, we restate the state space model in Eqs. (4-1) and (4-2) ... 

---