Application of Intelligent Control in Mine Mechatronics System

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

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

Models of manufacturing flexibility have become centred on highly automated systems. These fail to account for the flexibility that is characteristic of many small, human-centred manufacturing operations or in systems based on hybrid automation. This paper presents a generic framework that models flexibility in terms of the underlying capability of a manufacturing system and its control systems, human and machine. In this model, manufacturing versatility, the ability to change, is realized through the application of automatic control to the inherent capability of the system. Flexibility defined as the ability to cope with the uncertainty of change, extends versatility through the application of intelligent control. This process generates new knowledge about the system and its environment. The system learns by applying this knowledge and consequently the autonomic response of the system is dynamically improved. The model is hierarchical, accounting for capability, versatility and flexibility at various levels of manufacturing system, from the operator-machine level up to a whole organization. The efficacy of the model is demonstrated in a study of a semi-manual assembly cell in a small manufacturing company.

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.

Intelligent control techniques are suited for problems that are either difficult to tackle mathematically or where the use of these techniques provides improved performance, easier implementation, or faster time to market. The authors present some industrial applications of fuzzy logic and neural networks at Rockwell, comprising motor control, autopilot level change control, aircraft autothrottle control, aircraft gain schedule generation, sensor diagnostics, and control of color printing. The authors discuss the driving factors for using fuzzy and neural approaches in these applications and the benefits derived. The authors also comment on how to select the right application of intelligent control techniques to derive maximum commercial payoff.

In accordance with the technical features of large capacity, non-linear and dead time temperature resistance kiln system.a fuzzy predicted coordinated control algorithm is stated .Enhance tracking and anti-inference capabilities of The algorithm. Simulation results indicate that this method is better adaptive to the alteration and improverer the static precise that PID control.

Expert System Approach of Industrial Process Adaptive Control

The application of conventional and intelligent control paradigms to the dispensing of adhesive in the manufacture of mixed technology printed circuit boards, a real process with real time constraints and error conditions, is outlined in this paper. A description of the process and its various operation and error states is followed by a brief discussion of the application of conventional (system identification and incremental proportional control) and intelligent (knowledge-based, fuzzy logic and neural network) controllers to the system. Comments on the control and timing performance of each paradigm are given.

The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.

This paper proposes a three pieces game device based on machine vision and intelligent control for the limitations of traditional three pieces game. The device uses an integrated OpenART camera for image acquisition and LAB color space for accurate recognition of the chess board and pieces. First, the camera is calibrated to ensure the accuracy of image acquisition; subsequently, the coordinates of the chessboard grid, the border, and the color and position of the pieces are identified by image preprocessing and target detection algorithms. On this basis, the device adopts a geometric method to solve the robotic arm to realize the accurate picking up and placing of chess pieces. Experiments show that the device can accurately recognize the state of the board, quickly respond to the player's commands, and show good stability and intelligence in the process of the game. The three pieces game device not only provides players with a new game experience, but also demonstrates the prospect of the wide application of machine vision and intelligent control in the game field.

In intelligent control applications, neural models and controllers are usually designed by performing an off-line training, and then adapting it online when placed in the operating environment. It is therefore of crucial importance to obtain a good off-line model by means of a good off-line training algorithm. In the paper a method is presented that fully exploits the linear-nonlinear structure found in radial basis function networks, being additionally applicable to other feedforward supervised neural networks. The new algorithm is compared with two known hybrid methods.

This paper presents a type of solar thermal power plant and also studies the efficiency of the common type of this distributed generation resource. In order to improve the efficiency of the power station, several techniques are studied such as the combined cycle plant, the thermoelectric generator and the intelligent control application. The thermodynamic analysis of the power plant and the performance of the equipment with an intelligent control system are validated by the developed experimental prototype.

As the trend of the new generation of intelligent transportation systems, the application of the cooperative vehicle-infrastructure system (CVIS) is important to reduce traffic congestion and accidents. For the cooperative vehicle-infrastructure system, the roadside unit (RSU) is a new application without enough deployment experience. Meanwhile the deployment of traditional mechanical and electrical infrastructure is generally based on experience and is easy to change the design. This paper analyzes the key technical parameters of RSU and the Digital Measurable Image (DMI) method in context of the CVIS. After collecting field data from a smart highway pilot project (the Deqing-Fuyang expanded Huzhou section in Chang-Shen Highway) and matching the DMI method with highway mileage pile numbers, an RSU deployment scheme based on DMI data was completed. It provides significant support for smart highway demonstration projects with cooperative vehicle-infrastructure intelligent management and control applications.

The application of intelligent controllers in the automation of a small plant for recycling of secondary polymers : M. Hadjiiski, E. Panchev, Z. Zlatev, S. Patsoff, pp 233–238

Application of the fuzzy logic intelligent control of space-based systems has received relatively less attention compared to conventional control procedures. Basic concepts involved in development of a hierarchical control system using fuzzy logic are explained. Next, fuzzy logic is applied to control a novel two-link robotic arm, composed of revolute and prismatic joints, supported by a mobile base traversing along an orbiting platform. The control system that is developed has three levels. A conventional controller, consisting of the feedback linearization technique combined with proportional-derivativecontrol is used in the bottom level of the hierarchical system, to control the servomotors of the robot. A second layer consists of a servo-expert that preprocesses the high-resolution information coming from joint encoders and extracts the status of the system. A third, intelligent layer is added at the top of the hierarchy to complete the control structure. The main purpose of the top level is to tune the parameters of the crisp (conventional) controller to improve the response of the system. A decision table is developed of ine, for tuning the parameters of the crisp (nonfuzzy) controller, which considerably reduces the real-time computational effort. Typical pick-and-place results from the robot are presented that show that extra tuning can signiŽ cantly improve the performance of the manipulator.