Expert System Approach of Industrial Process Adaptive Control

Bare-bones languages serve small systems with ease: Cushmann, R HEDN Vol 25 No 16 (5 September 1980) pp 171–177

Considering different types of time-varying lag system, control strategy is determined by recognition of transient characteristics of responses and cooperation with expert system, forming the basic idea of intelligent controller design. Based on the dynamic response mode of the controlled object, multi-control mode was applied in control law, which can solve the oscillation problems appeared in common control system during pure lag link and improve the robustness of the control system. The application of intelligent temperature controller based on the pattern recognition in the same category of controlled objects requires no specific parameters and mathematical model of the controlled objects, but can bring a stable increase of manipulated variable, quick response, small overshoot, and no steady-state error, enabling it appropriate for different types of controlled objects and achieving perfect control effect.

Adaptive control has been developed for decades, and now it has become a rigorous and mature discipline which mainly focuses on dealing parametric uncertainties in control systems, especially linear parametric systems. Nonparametric uncertainties were seldom studied or addressed in the literature of adaptive control until new areas on exploring limitations and capability of feedback control emerged in recent years. Comparing with the approach of robust control to deal with parametric or nonparametric uncertainties, the approach of adaptive control can deal with relatively larger uncertainties and gain more flexibility to fit the unknown plant because adaptive control usually involves adaptive estimation algorithms which play role of “learning” in some sense. This chapter will introduce a new challenging topic on dealing with both parametric and nonparametric internal uncertainties in the same system. The existence of both two kinds of uncertainties makes it very difficult or even impossible to apply the traditional recursive identification algorithms which are designed for parametric systems. We will discuss by examples why conventional adaptive estimation and hence conventional adaptive control cannot be applied directly to deal with combination of parametric and nonparametric uncertainties. And we will also introduce basic ideas to handle the difficulties involved in the adaptive estimation problem for the system with combination of parametric and nonparametric uncertainties. Especially, we will propose and discuss a novel class of adaptive estimators, i.e. information-concentration (IC) estimators. This area is still in its infant stage, and more efforts are expected in the future for gainning comprehensive understanding to resolve challenging difficulties. Furthermore, we will give two concrete examples of semi-parametric adaptive control to demonstrate the ideas and the principles to deal with both parametric and nonparametric uncertainties in the plant. (1) In the first example, a simple first-order discrete-time nonlinear system with both kinds of internal uncertainties is investigated, where the uncertainty of non-parametric part is characterized by a Lipschitz constant L, and the nonlinearity of parametric part is characterized by an exponent index b. In this example, based on the idea of the IC estimator, we construct a unified adaptive controller in both cases of b = 1 and

This work presents the solution based on the augmenting sequence of linear programming problems (LPP) as a tool for intellectualizing home environment. The proposed solution empowers the intelligent decision making procedure which can be applied to various intelligent control applications. The augmenting self-training procedure based on LPP approach is presented as well, which allows making reasonable decisions having only limited data about the controlled environment. The method permits retraining the decision making system when new data is available. As a proof of concept, this solution is applied to intelligent light control application. The obtained simulation results show the method’s capability in making reasonable decisions according to users preferences. DOI: http://dx.doi.org/10.5755/j01.itc.44.2.10235

Artificial intelligence and cognitive science must look at the world of industrial-process control to find the technological reifications of the concept of mind.

The characteristics and applications of the system for welding machines based on SCM , based on PLC control system and PC-based industrial control system was introduced. It has certain significance to the design of welding machine control system.

An expert system is a computer software technology developed from artificial intelligence research. It may be used for intelligent manufacturing process control and, when properly designed, has the capability to imitate human behavior. An expert system’s value is to assist a human in executing realtime process control decisions in a complex system. This article provides an introduction to the concepts of intelligent process control and includes a process control scenario applying an expert system as well as statistical and optimization technologies. A simple guide of how to get started and a description of expert system tools are also presented.

An alternative technique for developing intelligent process monitoring and control systems is expert systems (ESs). ESs use logic rules to carry out heuristic reasoning. Knowledge used to reach a conclusion is transparent because ESs have explanation capabilities. In this respect, ESs are superior to neural networks because the knowledge embedded in neural networks is opaque. It has long been recognised that a critical issue in developing knowledge based ESs is the bottleneck of knowledge acquisition. Traditionally knowledge acquisition has been dependent on consulting domain experts. A disadvantage of this approach is that experts are usually better in collecting and archiving cases than in expressing the experience and cases explicitly into production rules. There are more difficulties in compiling knowledge about process operations due to the large number of interacting variables.

As we all know, big data and intelligent computing have been widely used in information science, life science, computer science and intelligent control, intelligent robots, vehicle networking, space technology, marine development and other fields, especially in the future life science and medical field. This paper is based on a new digital optimal decision-making idea of intelligent signal processing and control. On the basis of extending non-digital signal processing and information prediction to signal processing and information prediction in the digital generalized information measurement space, and making full use of optimization algorithms such as digital filtering and frequency spectrum analysis, this article discusses in depth the intelligent optimal control of a class of non-stationary random processes in artificial intelligence, biomedicine, radio communication, automatic control and other scientific and technological fields. It provides a new theory, a new approach and an effective and reliable new method for the mathematical modeling and optimal control of such processes, and has broad application prospects and value.

In the new stage for intelligent manufacturing of traditional Chinese medicine(TCM) from pilot demonstration to in-depth application and comprehensive promotion, how to raise the degree of intelligence for the process quality control system has become the bottleneck of the development of TCM production process control technology. This article has sorted out 226 TCM intelligent manufacturing projects that have been approved by the national and provincial governments since the implementation of the "Made in China 2025" plan and 145 related pharmaceutical enterprises. Then, the patents applied by these pharmaceutical enterprises were thoroughly retrieved, and 135 patents in terms of intelligent quality control technology in the production process were found. The technical details about intelligent quality control at both the unit levels such as cultivation, processing of crude herbs, preparation pretreatment, pharmaceutical preparations, and the production workshop level were reviewed from three aspects, i.e., intelligent quality sensing, intelligent process cognition, and intelligent process control. The results showed that intelligent quality control technologies have been preliminarily applied to the whole process of TCM production. The intelligence control of the extraction and concentration processes and the intelligent sensing of critical quality attributes are currently the focus of pharmaceutical enterprises. However, there is a lack of process cognitive patent technology for the TCM manufacturing process, which fails to meet the requirements of closed-loop integration of intelligent sensing and intelligent control technologies. It is suggested that in the future, with the help of artificial intelligence and machine learning methods, the process cognitive bottleneck of TCM production can be overcome, and the holistic quality formation mechanisms of TCM products can be elucidated. Moreover, key technologies for system integration and intelligent equipment are expected to be innovated and accelerated to enhance the quality uniformity and manufacturing reliability of TCM.

In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.

Based on the European Structural Funds it was developed the Intelligent Mobile Box, Intelligent Panel Controller with intelligent adaptive controllers within the industrial research and experimental development in the company Kybernetes, s.r.o. Within the frame of the academic-industry cooperation, the intelligent adaptive controller was tested at the Department of Cybernetics and Artificial Intelligence, Technical university of Kosice, Slovakia. The tests of the mobile intelligent adaptive controller were performed on two levels of university study, on the Bachelor level on the exercises from the subject “Control of Technological Processes” and on the Engineering level the exercises from the subject “Intelligent Control Networks” and on one Diploma project. Goals of students of the Control of the technological processes course had two goals, firstly to connect the intelligent adaptive controller to pre-defined controlled system (real plant, real model or simulated model) and next to validate the control results. Students of the Diploma project on the Engineering level had more advanced goals. Tasks defined for engineering students were to connect the intelligent adaptive controller to non-defined controlled system, setup the adaptivity process of the controller regarding the learning error, parameterize the control system, observe and validate the control results. Both sides concluded this cooperation as very valuable. Main contributions for students were (U1) the challenge to apply studied theoretical knowledge on the real industrial controllers, (U2) experience with new research results and technologies deployed in industry and (U3) the implementation of the control and adaptive algorithms from abstract mathematical area to real PLC controller. On side of industry research company the main contributions were (C1) testing of designed algorithms and (C2) user feedback from students to make the application HMI interface more understandable a native.

In view of the characteristics of the electrohydraulic servo system, a man-simulated intelligent controller is designed so as to improve the system static precision and dynamic characteristic by application of intelligent control theory in this paper. The results of the emulation and experiment indicate that this controller has good control.

The principles of intelligent control and the design of an expert intelligent control system (EICS) are discussed. Based on these principles, a novel control system is proposed to deal with the process control of gas carburizing furnaces. Since no critical and accurate mathematical models of the controlled plants are required, this EICS can be directly applied to many industrial furnaces. Its benefits are demonstrated by digital simulation and practical experiments. >

This paper reviews the application of adaptive control in three areas: aircraft control systems, process control and electrical drives. In the aeronautical field, where pioneering work on adaptive control was carried out, relatively few applications have been achieved on account of competition of rival techniques such as air data scheduling and also due to deficiencies in basic adaptive control theory. From the field of applications in the cement industry, the steel and metallurgical industry, the chemical industry, the paper industry, power plants, and some miscellaneous areas of process industry, the most interesting developments in adaptive control during the last five years are discussed, although only relatively few applications have been reported. It is pointed out that the reason for this situation is due to the complicated dynamic behaviour of these industrial processes as well as the (healthy) conservative management of many industrial processes. The field of electrical drives seems to be one of the most promising areas for the application of adaptive control. The dynamics of such systems are well understood and limitations of theory are less restrictive.