Adaptive Control Approach

The adaptive control approach takes as input a sequencing graph model G, without timing constraints and directly maps the graph model into a synchronous control unit consisting of a modular interconnection of interacting finite-state machines. As its name indicates, adaptive control takes into account the variations in the execution times of the operations caused by the changing input data. 

Detailed timing constraints are not considered in the adaptive control scheme. In this case, the minimum control delay for any control implementation of G, is simply the length of the longest weighted path in G, from the source vertex Vo to the sink vertex v , where the weight of a vertex is equal to its execution delay for that particular input sequence. We will show that the adaptive control implementation is precise by guaranteeing its control delay to be minimum for all input sequences. Extensions to support detailed timing constraints are presented in the next section. 

We present an overview of the basic strategy in Section 8.1.1. Two control implementations are presented. Section 8.1.2 describes a simplified scheme that supports data-dependent delay operations and multiple execution flows, but the resulting control is not precise. We extend the simplified scheme in Section 8.1.3 to obtain a precise control implementation. Analysis of adaptive control is presented in Section 8.1.4. 

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Ghosh, S., Young, D, L., Gadkar, K. G., Wennerberg, L. and Basu, K. 2007. Towards optimal virtual patients An online adaptive control approach. ConfProc IEEE Eng Med Biol Soc, 2007,3292-5. 

Rusnak, I. A. Guez and I. Bar-Kana. Multiple Objective Approach to Adaptive Control of Linear Systems. In Proceedings of the American Control Conference. San Francisco, pp. 1101-1105 (1993). 

Nonmodel-based controllers, such as the least mean square (LMS) and artificial neural network back-propagation adaptive controllers, employ iterative approaches to update control parameters in real time [14-17]. However, those methods may encounter difficulties of numerical divergence and local optimiza-... 

Coughanowr, D. R. Process Systems Analysis and Control, 2nd edn. (McGraw-Hill, New York, 1991). Kuo, B. C. Discrete Data Control Systems (Prentice-Hall, Englewood Cliffs, New Jersey, 1970). Landau, Y. D. Adaptive Control—The Model Reference Approach (Marcel Dekker, New York, 1979). Popovic, D. and Bhatkar, V. P. Distributed Computer Control for Industrial Automation (Marcel Dekker, New York, 1990). 

Landau, Y. D. Adaptive Control—The Model Reference Approach (Marcel Dekker, New York, 1979). 

Therefore, the chapter is mainly focused on the design of model-based control approaches. Namely, a controller-observer control strategy is considered, where an observer is designed to estimate the heat released by the reaction, together with a cascade temperature control scheme. The performance of this control strategy are further improved by introducing an adaptive estimation of the heat transfer coefficient. Finally, the application of the proposed methods to the phenol-formaldehyde reaction studied in the previous chapters is presented. 

In the fifth chapter, a general overview of temperature control for batch reactors is presented the focus is on model-based control approaches, with a special emphasis on adaptive control techniques. Finally, the sixth chapter provides the reader with an overview of the fundamental problems of fault diagnosis for dynamical systems, with a special emphasis on model-based techniques (i.e., based on the so-called analytical redundancy approach) for nonlinear systems then, a model-based approach to fault diagnosis for chemical batch reactors is derived in detail, where both sensors and actuators failures are taken into account. 

An adaptive controller normally will incorporate the highly successful feedback structure. In the field of adaptive control, three general approaches have been developed (56) ... 

Model reference adaptive control is based on a Lyapunov stability approach, while the hyperstability method uses Popov stability analysis. All of the above methods have been tested on experimental systems, both SISO and MIMO (53), (54), (55). The selftuning regulator is now available as a commercial software package, although this method is not satisfactory for variable time delays, an important industrial problem. 

Shah, S. L., Fisher, D. G. and Karim, N. M., "Hyperstability Adaptive Control-A Direct Input-Output Approach without Explicit Model Identification," Proc. Joint Automatic Control Conference, 1979, 481. 

In this chapter we present an experimental approach, known as process identification, which can be used to construct a reliable model, either before or after the process has been placed in operation. In addition, we study how process identification can be coupled with various control systems to yield on-line adaptive control strategies. 

Complex, self-organizing systems continuously adapt to and change with their environments but do so in ways that are impossible to predict. Because of this, people have begun to realize that industrial firms operate very much like ecosystems (Boston, 2001). The result of this has been complexity theory, explaining how a complex adaptive systems approach can help make policy, business, education, and research decisions. Studying how ants find the closest food source, for instance, has helped industries to find efficient solutions to marketing soap, schedule movement of casks of whiskey, and control crowds at amusement parks (Figure 8.2.8). 

In direct inverse control, (Figure 12.2), the neural network is used to compute an inverse model of the system to be controlled ]Levin et al., 1991 Nordgren and Meckl, 1993]. In classical linear control techniques, one would find a linear model of the system then analytically compute the inverse model. Using neural networks, the network is trained to perform the inverse model calculations, that is, to map system outputs to system inputs. Biomedical applications of this type of approach include the control of arm movements using electrical stimulation [Lan et al., 1994] and the adaptive control of arterial blood pressure [Chen et al, 1997]. 

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