Complex Correlation System Control Based on Human Simulation Intelligence

Because there are lots of difficulties in mathematic modeling for uncertainty complex object, so it is not suitable to utilize traditional methods in some aspect such as identification, analysis, synthetical design and implementation. Especially, for the idea of system modeling, it is very different in comparison with traditional mathematic modeling, therefore, it is very difficult to study control problem for uncertainty system in technology and theory. On the basis of summarizing characteristics of uncertainty complex object, present to establish generalized control model, and apply intelligent control strategy to implement control effectively. In addition, authors also studied control algorithm and system structure. Engineering practice shows that it is effective and successful to establish generalized control model, and select intelligent control strategy for uncertainty control system.     

Uncertain reasoning model for the rocket safety decision system

In view of the complexity of the measuring environment and uncertainty of the a prior knowledge for rocket aviation,a rocket safety control system based on uncertainty reasoning is proposed.The uncertain representation and processing of the knowledge in expert systems are established according to the characteristics of the parameters .Following the characteristics of the decision rules and decision procudure,the reasoning control strategy is studied and the method for conflict resolving strategy and searching strategy is proposed.Then,the uncertain matching rules and the feasibility degree delivering method are expressed,and the decision-making network and the rule-based uncertainty reseaoning model were constructed.Simulation experiments show that this system can acquire the craft launching information quickly and accurately,which greatly improves the reliability and authenticity of safety control decision for rocket aviation,and can control the rocket aviating state effectively.     

Analysis on Stability for Uncertainty Complex Control System Based on Knowledge

Concept of stability is vague, but it is basic characteristic of dynamic system, and precondition that make all kinds of control system run normally. Today it is still a challenge to analyze stability for uncertainty complex control system, whose mathematic modeling is impossible or very difficult, so it is very hard to judge whether a system running is stable or not by mathematic stability criterion. A kind of analyzing method is presented, which combine absolute stability with self-locked concept of heuristic search, it may analyze system stability in more range through adjusting self-collected loop parameters for uncertain control system based on knowledge control model. It is shown that stability analyzing method is effective and suitable for practical physical system in the engineering project.     

One Auto-optimized Fuzzy Control of Car-engines Based on GA

One new auto-optimized fuzzy control model of car-engines based on genetic arithmetic was introduced aiming at the problem that the parameters of car-engine are of the discreteness, the nonlinear and the uncertain characteristics, with which it is difficult to control accurately the excess air coefficient and the spark advance angle in dynamic procedure when using a conventional control model. The rule of the design scheme of the controller achieving the model based on embedded operating system is discussed. Further more, it is probed that the fuzzy control parameters are optimized automatically using GA based on on-line real-time simulation. The results of experiments show that the dynamic indexes and the fuel economy indexes are all improved greatly and the advantage of the model on reducing fuel consumption is obvious.     

Control Strategy of DO Parameter Based on Human Simulated Intelligence in the Wastewater Treatment Process

The treatment of wastewater is a complicated biological and chemical process, which can not be described precisely with a mathematical model. It is almost impossible to get a satisfactory control result when a traditional PID control method is adopted, for the parameters of the activated-sludge process are uncertain. The paper takes DO parameter controlling as an example, and proposes a human-simulated intelligent control strategy to reduce the fluctuation problem of DO parameter. Both the control rules and the structure of control system are presented. The simulation experiments shows that the control effect of activated-sludge process by HSIC is much better than PID control.     

A Uncertain Reasoning Machine

This paper firstly discusses some problem about the uncertain reasoning machine in the agricultural expert system based on HPC, such as the knowledge representation, the certainty factor, the representation of evidence uncertainty, the calculation of multi evidence uncertainty, the update of uncertainty and so on. Secondly, the control strategy of this uncertain reasoning machine is discussed. This control strategy adopts a converse reasoning strategy based on target, and it adopts a conflict removing strategy based on certainty factor. The arithmetic to realize the uncertain reasoning machine is also given in detail. At last, this paper stats the example of malnutrition diagnosing of grape as the application of the reasoning machine.     

Fuzzy Adaptive Variable Structure Control of Speed Process of DC Motor

The load changed of motor affects normal run. If it is not adjusted, the state of motor will become poor, while it affects steady of the system. This paper presents an efficient method for the motor speed controlling, it applies Tacagi-Sugeno's method that each linear submodels are connected for fuzzy algorithm, an algorithm of the fuzzy model identifying and fuzzy control is fulfilled through whole nonlinear model which are replaced with local linear models. It makes speed of motor to maintain steady, and variable structure control is applied to guarantee performance of dynamic flowing for process controlled, the design of stability and robustness is proposed in the scheme. Practice running results show that it is efficient that uncertain dynamic processes are controlled.     

Robust Adaptive Control Based on On-line Identification to Multi-parameters

In system controlled,Sampling these parameters and with parameters for controller in time, it is an object to adaptive control. The article presents a scheme that the structure parameters on it's model and model parameters is robust identified by on line, while to optimal of algorithm on adaptive control is fulfilled. In control scheme, method of intelligent identifying to the structure parameters(levels of model) and algorithm of robust pole placement are introduced also It is advancer for already mentioned scheme. Whole scheme presents new model on adaptive control.     

Rapid Prototype Control of Hydraulic Automatic Transmission System Based on dSPACE

dSPACE is a prototype development and real-time control testing platform which is based on MATLAB/Simulink software. By establishing mathematic model of hydraulic automatic transmission system, the shift rules are achieved. A testing platform for hydraulic automatic transmission system is developed whose control software is programmed by simulink and using the dSPACE as the centre controller. The experiment of electric throttle control and power performance matching control of hydraulic automatic transmission system is carried out. the result shows that well matching performance of hydraulic automatic transmission system can be realizedby using the control strategy proposed in this paper, it provides a fast and efficient approach for researching and developing the hydraulic automatic transmission system.     

An Excitative Control in Operating Process for Synchronous Motor

Aimed at the excitative control for synchronous motor, based on main adjust for power factor and aided adjust for power angle,a kind of new control strategy is presented in this paper. In the steady running,the excitation current will be adjusted in keep. power factor constant,and in dynamic running,it will be calibrated by means of power angle when the disturbance happened.For specified object,the improved algorithm of artificial intelligence controller is also presented in the paper,the simulation result shows that the control algorithm is satisfactory.     

Guaranteed Cost Control of a Class of Linear Stochastic Systems on the Basis of Robust Analysis

This paper discusses the guaranteed cost control of a class of linear stochastic systems on the research of uncertain linear systems.The authors give the definition of this linear stochastic systems' guaranteed cost control,by using Ito formula and linear matrix inequality,combined its square-stability,under the supposition of the square stability of this kind of linear stochastic system,gets the accordingly guaranteed cost control condition.The conclusion is,if the linear stochastic system is square-stability,then its guaranteed cost control exists.Finally,the problem of designing a optimal guaranteed cost control can be turned into a convex optimal problem.     

Overall control strategy and operational performances of a doubly fed wind turbine generator system

In order to comprehensively and correctly analyze the real-time operational characteristics of a grid-connected wind turbine generator system, it is necessary to analyze the overall control strategies and operation performances of the wind turbine generator system. According to the type of a doubly fed wind turbine generator system, the mathematical models of wind turbine, drive train and doubly fed induction generator (DFIG) are presented. From the viewpoints of the maximum wind energy capture and safe operation of the wind turbine, a maximum power output control strategy is proposed by considering the DFIG loss minimization, and a variable pitch control strategy is also presented by considering generator rotational speed restriction and the output power restriction. Combined with a power decoupling strategy of DFIG, the overall operational performances of the doubly fed wind turbine generator system are simulated. By comparing the simulation, theoretical analysis and actual operational data, the results show that the presented model and overall control strategies of the doubly fed wind turbine generator system are valid.     

A SELF-TUNING CONTROL FOR A PARALLEL AC/DC POWER SYSTEM

Based on a non-linear system model, an adaptive control methodology is presented for a parallel AC/DC power system by means of on-line recursive parameter identifications. Taking the advantage of rapid power regulatiing ability of the DC power system and combining with the generalized minimum variance self-tuning control principles, the dynamic qualities of the controlled system has been greatly improved. Using SISO form makes the control system very simple to implement. Through the design, simulation and analysis of the self-tuning control system for a practical example, it shows that the self-tuning control strategy is very promising for AC/DC power systems.     

A Fuzzy Controller with Simulating Human Intelligent Control and Its Simulation Analysis

In the intelligent control system with microprocessor controlling, the development of embedded software requires some simple and practical intelligent control algorithms. The algorithms can not only make the control system's performance good, but also meet the demands of real time control. Aim at the problem, it is presented that a fuzzy controller with simulating human intelligent control. The structure , control algorithms and the simulation results of the controller are given. The simulation and application show that the fuzzy controller with simulating human intelligent control has many advantages such as fast response, less overshoot, good robust. The design of the controller does not need the accuracy math model of controlled object.     

Analysis on Correlated Problem with Control of Uncertainty System

The majority difficulty for uncertainty system control is that agreements haven't been arrived in concept of uncertainty information and its characteristics, uncertainty system and its properties. Therefore some control problems have discussed based on some confused concepts, and the result shows that the study on uncertainty system control hasn't made great progress yet. In addition, problems concerning uncertainty system control are very wide in the practical engineering. Aimed at above problems the concept of uncertainty information and its classification, basic properties of uncertainty system and desired control strategy based on own practical experience are studied. Finally, that intelligent control strategy is wise choice for uncertainty system control based on successful engineering application.     

DIGITAL POWER SYSTEM STABILIZER ( DPSS )

A digital power system stabilizer ( DPSS ) and its design method are presented in this paper. The mathematical model of a single machine infinite bus power system is obtained using off-line parameter identifications, and on the base of the identified model the control system is synthesized by generalized minimum variance control principles. Through feedback combination of several signals this control system has double functions of damping low frequency oscillation and automatic voltage regulating. In order to avoid static errors the very effective way is to combine discrete generalized minimum variance control with continuous integral control. Besides the proposed control strategy is very simple in structures and very easy to realize. Simulation results under several cases have shown the correctness and effectiveness of the proposed control strategy.     

The Mathematical Model and Heuristic Algorithm of the Load Balance in the Parallel Processing System

Based on the algorithms proposed before,a model and new heuristic algorithmfor solving the load balance in a parallel processing system (cell) is put forward by authors in thispeper. The optimal goal of the propsed model benefits the minimum of the load of equipment withlargest load and the difference between and two equipments in the system. The complexity ofalogrithm is not higher than O(n2). The computational experiments show that it is a practical algorithmof engineering.     

Control strategy and parameters optimization outsourcing production inventory system under uncertainty

This paper proposes a control strategy for the optimal control of the outsourcing production inventory system based on production point and production batch. The statistical probability distribution is used to describe the uncertainty of the production capacity of cooperative enterprises and the transportation time. The optimization model of the control parameters is developed with random production capacity for cooperative enterprises and random transportation time in order to minimize the holding cost,setup cost as well as the cost of production capacity waste. A simulation-based control parameters optimization algorithm is designed to solve the proposed model. The control strategy of production inventory system and the control parameters optimization algorithm proposed are validated by an example. We analyze the influence of the variation of the production point and production batch to the performance of the outsourcing production inventory system.     

Low complexity antenna selection algorithmsfor base-station coordination system

Two kinds of antenna selection algorithm one proposed according to the characteristics of base-station coordination system. One is called aggregate channel frobenius norm antenna selection (ACFAS), another one is individual channel frobenius norm antenna selection (ICFAS). The object function of antenna selection in ACFAS is based on the aggregate channel frobenius norm of all of the coordinating base-stations and users, while the object function of ICFAS is based on the individual channel frobenius norm of every base-station and user. With the complexity analysis and system simulations, it is proved that the antenna selection algorithms proposed are capable of reducing the complexity, yet at a cost of system capacity lost. So they are suboptimal algorithms.     

THE OVERLAPPING DECOMPOSITION DECENTRALIZED CONTRAL FOR GENERATOR''S EXCITER/GOVERNOR SYSTEMS

In this paper the generator's exciter/governor system are decomposed by using the overlapping decomposition method, and an overlapping decomposition decentralized control (ODDC) system is designed for the exciter /governor systems. In or.dar to enhance the dynamic qualities of the decentralized control system, the concept of the second feedback system is introduced to modify the first feedback gain matrix on the condition of the structure of the original decentralized control system maintaining unchanged. The practical decentralized control system design and its; simulation results show that this method greatly improves the control effect of the ODDC system and also holds all the advantages of the ODDC system. Besides, this paper indicates that it is a simple and effective method to control excitor/governor system by overlapping decomposition decentralized control strategy.