Control applications, adaptive

Isermann, R. Digital Control Systems. Springer-Verlag, Vol. 1 Fundamentals, Deterministic Control 2nd rev. ed. (1989) Vol. 2 Stochastic Control, Adaptive Control Multivariable Control, Adaptive Control, Applications 2nd rev. ed. (1991)... 

However, the main reason for the lack of wide application of on-line adaptive control is the lack of economic incentive. On-line identification is rarely required because it is usually possible to predict with off-line tests how the controller must be retuned as conditions vary. The dynamics of the process are determined at different operating conditions, and appropriate controller settings are determined for all the different conditions. Then, when the process moves from one operating region to another, the controller settings are automatically changed. This is called openloop-adaptive control or atn scheduling. 

Abstract Synthesis of carbon adsorbents with controlled pore size and surface chemistry adapted for application in medicine and health protection was explored. Conjugated polymers were used as carbon precursors. These polymers with conjugated double bonds C = C have high thermal stability. Formation of sp carbon structures occurs via condensation and aromatization of macromolecules. The structure of carbon materials obtained is related to the structure of the original conjugated polymer, thus the porous structure of carbon adsorbents could be controlled by variation of the conjugated polymer precursor. 

Gain Scheduling Adaptive Control is a special application of this procedure. For example we may have a control valve whose characteristic (input signal/valve stem position relationship) is non-linear. In this case, the valve stem position would be measured in order to obtain the gain of the valve (the appropriate relationship must be known) and the valve gain is used then to adjust the gain of the controller. If the auxiliary variable relationships are more complicated then it may be necessary to employ a Programmed Adaptive Control procedure. 

Morant, F., Martinez, M. and Pic6, J. In Application of Artificial Intelligence in Process Control by Boullart, L., Krijgsman, A. and Vingerhoeds, R. A. eds. Section VI. Supervised adaptive control. 

Here we examine the control of migration in a periodically driven nonlinear oscillator. Our aim is to demonstrate that application of the approximate solution found from the statistical analysis of fluctuational trajectories optimizes (minimizes) the energy of the control function. We compare the performance of some known adaptive control algorithms to that of the control function found through our analysis. 

While the single-loop PID controller is satisfactory in many process applications, it does not perform well for processes with slow dynamics, time delays, frequent disturbances, or multivariable interactions. We discuss several advanced control methods below that can be implemented via computer control, namely, feedforward control, cascade control, time-delay compensation, selective and override control, adaptive control, fuzzy logic control, and statistical process control. 

SSFSE process, the ethanol production can be enhanced when compared to a batch process. For all these reasons, in this work a novel adaptive control scheme for a fed-batch SSFSE process is proposed, which due to its simplicity is suitable for industrial applications. 

Furthermore, poisoning by Zn, Pb and P creates an amorphous, vitreous surface on the bead that clogs the micropores and only leaves the macropores open. Thus the distribution of micro and macropores must be well designed as illustrated in Fig. 5. Photographs 6 to 9 show some details of a porosity distribution which is exceptionally well adapted to the automotive exhaust control application. 

Adaptive control systems have been applied in chemical processes. The range of their applicability has expanded with the introduction of digital computers for process control. Several theoretical and experimental studies have appeared in the chemical engineering literature, while the number of industrial adaptive control mechanisms increases continuously. Most of the adaptive control systems require extensive computations for parameter estimation and optimal adjustment of controller parameters, which can be performed on-line only by digital computers. Therefore, we will delay any discussion on the quantitative design of such systems until Chapter 31, after we have studied the use of digital computers for control. 

Methods and Applications in Adaptive Control, H. Unbehauen (ed.), Springer-Verlag, Berlin (1980). 

Chapter 12 introduces the use of neural network techniques and their application in modeling physiological control systems. As their name imphes, neural networks are computational algorithms based upon the computational structure of the nervous system, and are characterized as distributed processing and adaptive. Neural networks have been used to describe the control of arm movements with electrical stimulation and the adaptive control of arterial blood pressure. 

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]. 

The control program is checked for syntax, words, program block composition, tool path definition, macro adaptation, and application or actual machine tool parameters. Specifications for the program language, machine control unit, and machine tool are applied. 

Ocampo-Martinez, C., Puig, V. (2009). Faul-tolerant model predictive control within the hybrid systems framework application to sewer networks. International Journal of Adaptive Control andSigrud Processing., 25(8), 757-787. 

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