Manufacturing, Control, and Automation

This chapter identifies the common needs for process controls and automation that include methodologies to enable in-situ-level process controls, optimization at the plant or industry level, open-architecture software tools, adaptive control systems, methods and diagnostic tools for condition-based maintenance of process equipment in a manufacturing industry.

Mathematical modeling of technological process in formalin production

Methods and algorithms of the sustainable synthesis of adaptive control systems for the technological process of formalin production have been proposed in this article. Parameters of the regulator are estimated in the developed adaptive control system, i.e. the purpose of adaptation is reduced to the estimation of coefficients of the regulator providing the specified behavior of the system. The results obtained and the analysis of transition processes based on the modeling of the control system showed that with adaptive control, mass fractions of formaldehyde and methanol in formalin are held within 36.5÷37.5% and 0.7÷0.9% respectively, while the specific consumption of the methanol decreases by 3÷4%, which allows the process in mode close to the optimal.

Adaptive control systems for solar collectors

En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlinear neural model-based predictive control of a solar plant. In Proc. European Control Conf. ECC'97. Brussels, Belgium, Volumen TH-E I2, p. paper 264. [2]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998a. Comparison of RBF algorithms for output temperature prediction of a solar plant.. In Proc. CONTROLO'98, 9-11 September. [3]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998b. Neural identification applied to predictive control of solar plant. Control Engineering Practice, Volumen 6, pp. pp. 333-344. [4]Aström, K. J. & Wittenmark, B., 1989. Adaptative Control. Aström, K. J. & Wittermark, B., 1984. Computed controlles Systems, Theory and Design. Englewood Cliffs, NJ: Prentice Hall. [5]Barão, M., 2000. Dynamic and no-linear control of a solar collector field. Thesis (in Portuguese). Universidade Técnica de Lisboa, Instituto Superior Técnico. [6]Barão, M., Lemos, J. M. & Silva, R. N., 2002. Reduced complexity adaptative nonlinear control of a distribuited collector solar field. J. of Process Control, Volumen 12(1), pp. pp. 131-141. [7]Berenguel, M., Arahal, M. R. & Camacho, E. F., 1998. Modeling free responses of a solar plant for predictive control. Control Engineering Practice, Volumen 6, pp. pp. 1257-1266. [8]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1994. Simulation software package for the Acurex field.. Departamento de Ingeniería y Automática. [9]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1997. Advanced Control of Solar Plants. Londres: Springer-Verlag.

Design of components of adaptive control system of conveyor transport

The task design of components of adaptive control system of conveyor transport was carried out. The analysis of existing researches and publications, in which the main problem is highlighted, namely that uncertainty and external conditions during operation leads to the need to introduce new components, functions of the actuator conveyor and ensuring the adaptation based on intelligent control. As a result of the existing researches analysis and publications, the purpose of research is set, namely: increasing the efficiency of the conveyor line control system by designing the components of the adaptive control system that implement the algorithm of adaptation in conditions of uncertainty. The concept of а multicriteria choice, set of indicators for assessing the properties of a design system and its total effect have been substantiated. The results of the research are as follows: structured the functions of adaptive systems; the basic modes of development of adaptive control systems and their realization in industrial conditions are set, for that purpose, the mathematical support for exposing the vagueness of control worked out by ACIT KHNADU is used; the principles of development of adaptive control systems, technical support and requirements produced to the basic components of system (subsystems) are proposed. The practical value lies in the fact that the choice of components control systems makes it possible to improve the accuracy and the possibility of data correction. The originality lies in the use of multicriteria evaluation method and parameter optimization. Models are universal, will allow to select a set of technical means of CT control system according to the selected criteria and restrictions of each type of elements.

ADAPTIVE CONTROL OF TECHNOLOGICAL PROCESSES WITH DELAY IN CONTROL

A method for the synthesis of adaptive systems by technological objects with control delay is proposed in the presence of a significant uncertainty in the setting of the delay value and time-variable coefficients of the linear inertia part model, which ensures the robustness of the system with respect to the delay. Adaptive identification-type systems and direct adaptive control systems without predictor are considered. The system with roughness in relation to the change in the delay value is used as the main contour

Deep Learning of Adaptive Control Systems Based on a Logical-probabilistic Approach

The problem of automatic selection of subgoals is currently one of the most relevant in adaptive control problems, in particular, in Reinforcement Learning. This paper proposes a logical-probabilistic approach to the construction of adaptive learning control systems capable of detecting deep implicit subgoals. The approach uses the ideas of the neurophysiological Theory of functional systems to organize the control scheme, and logical-probabilistic methods of machine learning to train the rules of the system and identify subgoals. The efficiency of the proposed approach is demonstrated by an example of solving a three-stage foraging problem containing two nested implicit subgoals