Research Of Intelligent Control Research Articles

Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction

Aiming at the problem that it is difficult to effectively predict the concentration of nitrogen oxide emissions from boilers under the changing load conditions of power plants, this paper proposes a dynamic modeling method of nitrogen oxide (NOx) emissions from power plants, which considers the delay-feature extraction of input variables. This method combines principal component analysis and Gaussian regression modeling, and uses principal component analysis to extract the feature information extracted from the input variable data, and the current value and historical sequence value of extracted information are used as the input of Gaussian regression model. In addition, the historical time series value of boiler NOx concentration is added to the input of the model as feedback data. Taking the combustion system of a 1,000MW ultra-supercritical unit boiler as an object, and combining with the actual operation data on site, a dynamic model of boiler NOx emission is established. The experimental results show that the built boiler NOx emission dynamic model has high prediction accuracy and strong generalization performance, which has a certain reference value for boiler NOx emission modeling and intelligent control research.

Physics-based machine learning method and the application to energy consumption prediction in tunneling construction

Representing causality in machine learning to predict control parameters is state-of-the-art research in intelligent control. This study presents a physics-based machine learning method providing a prediction model that guarantees enhanced interpretability conforming to physical laws. The proposed approach encodes physical knowledge as mapping relationships between variables in engineering dataset into the learning procedure through dimensional analysis. This derives causal relationships between the control parameter and its influencing factors. The proposed machine learning method's objective function is further improved by the penalty term in the regularization strategy. Verifications on the energy consumption prediction of tunnel boring machine prove that, the established model accords with basic principles in this field. Moreover, the proposed approach traces the impact of three major factors (structure, operation, and geology) along the construction section, offering each component's contribution rates to energy consumption. Compared with several commonly used machine learning algorithms, the proposed method reduces the need for large amounts of training data and demonstrates higher accuracy. The results indicate that the revealed causality and enhanced prediction performance of the proposed method advance the applicability of machine learning methods to intelligent control during construction.

Summary of Research on Intelligent Control algorithm and Application of Power system Automation

With the development of HVDC technology and the continuous progress of AC/DC hybrid power grid, the research of intelligent control in the direction of power system automation has made some progress and has a certain foresight. The power system automatic intelligent control technology algorithm discussed in this paper includes neural network, fuzzy control algorithm and genetic algorithm, application development and significance, through the development of related intelligent algorithms to provide some reference for the field.

Path tracking control optimization algorithm for mobile robot based on backstepping control algorithm

Path tracking control of wheeled mobile robot is always an important research content in intelligent control research. Aiming at the control problem that the nonholonomic nature of wheeled mobile robot and external interference factors affect the tracking control effect, an improved backstepping control algorithm is proposed based on backstepping algorithm and speed compensation control method. Firstly, the kinematics model of wheeled mobile robot is constructed, and the mathematical relations of kinematics and path tracking state error are deduced. Secondly, the kinematics speed control law of mobile robot is designed based on the backstepping control algorithm and speed compensation control method, which improves the robustness of path tracking control. Finally, the design is verified by Lyapunov stability theorem. The tracking convergence of the tracking control algorithm is discussed. Compared with the traditional backstepping control algorithm, the improved algorithm improves the robustness of path tracking control. The tracking control algorithm proposed in this paper can be applied in the related fields of wheeled mobile robot, and the practical benefit is remarkable.

IoT-Enabled Automatic Synthesis of Distributed Feedback Control Schemes in Smart Buildings

There is currently a rapid evolution of technologies enabling the “Internet-of-Things” paradigm, which leads to the design of systems with augmented sensing, analysis, and actuation capabilities. The intelligent control research community is beginning to recognize the challenge of designing flexible and adaptable control systems that will take advantage of the online evolution of the number and types of “things” in a large-scale system. Responding to this challenge, in a previous article, we designed and presented a semantically enhanced control system supervisor that integrates a knowledge graph and deductive inference capabilities, to achieve the online (re)configuration of feedback control schemes. In this article, we show that by exploiting the capabilities of this supervisor, we facilitate the automatic synthesis and plugging of model-based controllers for the space heating problem in multizone buildings. The supervisor retrieves stored knowledge about the building and the available control system components and uses it to configure online a scheme of distributed feedback controllers, one per building zone. The controllers are designed to utilize information online and optimally allocate the control signal to multiple heating devices in each building zone.

Characteristic Analysis of Digital Large Flow Emulsion Relief Valve

The relief valve is an important control and overload protection component of the emulsion pumping station. Its performance will affect the overall performance of the emulsion pumping station and the stable and intelligent control of the working surface. However, the research on high pressure and large flow relief valve for mine emulsion pumping station is still inadequate. In order to meet the requirements of emulsion pump station for large flow sensitivity, stability, reliability, and remote intelligent control of overflow valve, this paper uses the digital control method to establish the mathematical model of the relief valve and uses the software such as AMESim to its dynamic characteristics. The simulation results show that the structural parameters such as spool quality, damping hole, and spring stiffness have an effect on the working characteristics of the relief valve. It also provides reference for the intelligent control research of the large flow relief valve for the emulsion pumping station.

Research of Intelligent Control of Traffic Signal

At present, in the field of intelligent control of traffic signal, most of scholars at home and abroad use fuzzy control and intelligent algorithm, such as genetic algorithm, ant colony optimization, particle swarm optimization, multi-agent, artificial neural networks, fuzzy method etc. This paper summarizes and analyzes these algorithms, points out the problems and shortcomings in the present research, puts forward the direction and trend in the future research. These works have certain directive significance to the research and development of intelligent control of traffic signal.

Track-Hunting Intelligent Car Based on Linear CCD Sensor Using Fuzzy Controller

Nowadays intelligent vehicles play a more and more important role in daily life. In this paper, a track-hunting intelligent car is presented for intelligent control research. In the direction control of this car, a fuzzy Proportional-Integral-Derivative (PID) controller is applied to make a distinction between different situations and execute self-tuning operation at the same time. The controller is tested by simulation and experiments in this paper, and results prove effectiveness of the controller.

Numerical Simulation of Semi-Active Control during Hazard Evolution under Strong Earthquake in ABAQUS

With the development of the control research, intelligent control has become one of the most popular research topics worldwide. The traditional method of Numerical simulation on control theories are realized by the programs in MATLAB, which is based on numbers of assumptions and simplification. However, it is difficult to ensure that the seismic response of the structure is always in linear elastic state during the process of hazard evolution under strong earthquake so that certain errors during such simulation are inevitable. In order to solve this problem, a numerical method of the realization of semi-active control in commercial finite element software ABAQUS is proposed in this paper. One of the main advantages of this method is that the structural plastic response can be taken into account. In this paper, the seismic response of a bridge with three-span continuous girder is numerically studied, and the semi-active control is successfully realized in ABAQUS. The proposed method can be used to carry out the refined simulation of the seismic response of the structures with semi-active control set. The highlight of this paper is to provide an effective approach for the research of intelligent control.

Intelligent Control Research of PVC Polymerization Kettle Reaction and Optimal Application

PVC is one of the five biggest common plastic in the global scope, the demand for PVC resin in third place, its position is very important. This article firstly studied PVC industry development present situation and prospect of all kinds of PVC industrial properties. And then we study the process flow of polymerization kettle reaction, we point out that abnormal phenomenon of the judgment and coping measures, there are five aspects. The most important part, we study the polymerization kettle reaction intelligent control, main have batch control and adaptive control temperature, the reaction of polymerization kettle has the important meaning. Polymerization kettle temperature control is the reaction of the most important control parameters, therefore, this paper polymerization kettle reaction temperature control is optimized, and then put forward now is more extensive use of the industrial control system and the reaction kettle. At the same time it reaches a polymerization kettle of reaction control related engineering application.

Research of Intelligent Control of Air Compressor at Constant Pressure

The air separation plays an extremely important role in the development of metallurgical industry. And the air compressor which provides not only the required pressure air for the cooling equipment and refrigeration plant but also the raw material required for the separation plant is a key device in the air separation plant. Utilization of the air compressor is extremely extensive in many significant departments such as the metallurgy, oil and chemical industry. The air compressor system is a time-varying, delay and nonlinear complex system. Defects in the traditional control method and controller lead to the pressure instability of the compressed air outputted from the air compressor in volatile gas-consumption situations, sometimes a wide range of fluctuation arising, for gas consumption in the industrial field is irregular. Therefore, the working state of the air compressor becomes unstable. In general, electric energy consumption of the air compressor system working in the stable state counts up to about twenty percentages of the total energy consumption in the enterprise. Moreover, energy consumption in the unstable state is more. It results in both a waste of resources and greater energy consumption. Therefore, guaranteeing the output pressure of the air compressor system steady is extraordinarily significant for improving efficiency and saving energy for the overall air separation system. The turbine compressor system was taken as the research object to analyze its characteristics and control requirements. DCS control technology applied to the system, the ECS-700 system of SUPCON was utilized into configuring the air compressor system and monitoring real-time values of important parameters in the various components. According to the working characteristics of the air compressor system, the fuzzy control theory, as one of the modern control theories, was applied to the constant pressure control system to design a fuzzy pressure controller. The simulation results showed that the controller not only was more convenient and effective but also provided a certain directive significance and reference for control of other parameters of the air compressor system.

Intelligent Control Research of Multibody Satellite for High Stability

Control system of multibody satellite is designed to improve attitude stability in this paper. The control law consisting of variable structure control and neural network control is obtained by using Lyapunov stability theory. Variable structure control is a nonlinear control strategy employing feedback of a discontinuous signal, and is very robust to matched plant uncertainties and external disturbances when state variables enter the sliding surface. Neural network systems are physical cellular systems that can acquire, store, and utilize experiential knowledge. The knowledge is in the form of stable states or mappings embedded in networks that can be recalled in response to the presentation of the data input to the network systems. The application of the neural network is made possible by recognizing function approximation capability in this paper. Since dynamics of multibody satellite can not be obtained accurately, the conventional controllers will not ensure the good performance of control system. A suitable technique cope with such system is variable structure control and neural network control. By Integrating their merits, robust capability and high control accuracy can be achieved. In addition, compensation control is introduced into the feedforward loop to eliminate the disturbance caused by the motion of antenna, so convergence of angular velocity is in the form of exponent guaranteed. Simulation results illustrate the control system has the good attitude stable characteristics using the proposed controller in the presence of external disturbances and parameters uncertainties.

Intelligent control at the OSU Control Research Lab

The field of intelligent control has witnessed remarkable growth recently. Two main trends fuel this surge: our increasing understanding of how biological systems operate and astounding advances in computer technology. Together, these advances encourage the development of the field of intelligent control by providing alternative strategies for designing and implementing controllers for dynamical systems. As remarkable as this growth has been, development of the resulting intelligent controllers still fits within the conventional engineering methodology for the construction of control systems. Thus, effective intelligent control research requires a team of investigators, well grounded in the theory and application of conventional control methods. The Control Research Laboratory at Ohio State's Department of Electrical Engineering is one such group. The article includes some of the major players at the CRL and gives an overview of the work going on there.