Intelligent controller based on pattern recognition

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

Expert System Approach of Industrial Process Adaptive Control

Ensemble of long short-term memory prediction and variable universe fuzzy control for seismic response reduction using electromagnetic dynamic vibration absorber

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

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.

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

This paper presents a three-phase three-wire shunt active filter based on intelligent controllers to compensate current harmonics. Shunt active filter is the best solution to eliminate harmonics drawn from nonlinear load especially for low power system, the most inverter used is the two-level voltage source. The control strategies adopted to determine the compensation currents are the synchronous reference frame (SRF) detection method and the synchronous current reference (SCR) detection method. To improve the performance of the conventional control scheme based on hysteresis controller and take advantages of intelligent techniques, fuzzy and ANN current controllers are proposed in this paper. To maintain the dc voltage capacitor constant and compensate the inverter losses a proportional integral voltage controller is used. The numerical simulation is developed and performed using MATLAB-Simulink and SimPowerSystemd Toolbox from complete structure including control and power circuits. The obtained results show the simplicity and the effectiveness of the two proposed intelligent current controllers.

A description of a new class of organizational and technical systems is given – intelligent road-building compaction control systems, the principles of operation of which provide for ensuring a given quality of compaction of asphalt mixtures in the process of road construction. The development of systems of that class is due, in the first place, to the transition to digital and intelligent control systems in construction. A scientific problem related to the relevance of creating methods for intelligent control of compaction of asphalt mixtures that affect the quality of road surfaces was formulated. To succeed, the usage of neural network structures was proposed. The usage will allow real-time monitoring of changes in the compaction quality index and efficient controlling of road machines using both the driver's manual means and the automatic compaction control system. The properties of the system under study – which determine the principles of its functioning: intelligence, informativity, speed, interconnectedness of subsystems, controllability and modularity – were presented. The above principles of the functioning of the proposed system, which are part of the methodology for managing the quality of asphalt compaction of pavement roads, were implemented in a set of methods and algorithms. Among them should be noted a new method of intelligent continuous control of compaction in the process of laying asphalt mixtures, an advanced method of intelligent prediction of compaction quality by vibrating rollers.

The Boeing Company is conducting a series of programs with NASA, DARPA, and the USAF that apply intelligent control technology to flight control systems. These control systems are designed to adapt to off-nominal damage or failure conditions. Reconfigurable control, artificial neural network models, self-learning neural networks, and matrix inverse control methods are some of the technologies used in these flight research projects. This paper addresses the need for intelligent control and the general requirements for the design of intelligent systems. A description is given of the major intelligent controls programs being conducted by Boeing.

The paper presents Intelligent Control (IC) Interfaces for real time control of mechatronic systems applied to Hardening Process Control (HPC) in order to improvement of the material’s mechanical characteristics. Implementation of IC laws in the intelligent real time control interfaces depends on the particular circumstances of the models characteristics used and the exact definition of optimization problem. The results led to the development of the IC interfaces in real time through Particle Swarm Optimization (PSO) and neural networks (NN) using off­line the regression methods.

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

Intelligent control is more available for some MIMO systems, especially for some processes with uncertain mathematic algorithm. This paper focuses on the simplification of design and implementation for automation based on the technology of intelligent fuzzy control. As an example, an industrial process on ball mill to pulverize coal in a coal-fired power plant is described. For this process control, the intelligent control is more simplified, efficient and practical than traditional adjustment.

The goal of this paper is to design a wind turbine generator operated under variable speed with intelligent pitch angle control capability under variable wind conditions. The control scheme for the variable speed wind turbines has been discussed and analysed using wind turbines driven by the permanent magnet generators connected to the grid. Main strategies of conventional control and intelligent control using fuzzy logic have been explained and compared to each other. Simulated output variables of voltage, current and power are also presented in this paper for various values of wind speed.

Most of the currently existing microclimate control systems operate on the basis of traditional P, PI, PID controllers. But the work of such systems is effective only in a narrow operating range. With significant fluctuations in environment, it is required to reconfigure the parameters of these regulators each time. In such cases, maintaining comfortable conditions in the room is recommended by using intelligent control technologies, so-called fuzzy control systems, operating in terms of fuzzy logic. The aim of the paper is to research the possibility of implementation of intelligent control systems to maintain the comfortable microclimate in the premises and checking the adequacy of the system through simulation. The climate control system is based on fuzzy inference and provides forming the base of fuzzy production rules, fuzzification of input values, aggregation of truth of sub conditions of each rule, activation of conclusions and defuzzification process that generates an output signal to control the smart home functional devices. Procedures of the fuzzy inference and simulation are implemented in the Matlab programming environment using the Fuzzy Logic Toolbox and Simulink packages. The results of the paper showed that the developed intelligent microclimate control system for premises based on fuzzy logic provides a flexible self-tuning of the engineering equipment and maintaining the microclimate parameters at the required level.