Research of UD2UU with human simulated intelligent control for a double inverted pendulum

Double pendulum has four equilibrium points (down down, down up, up down, and up up). Twelve transfer actions and eight spin actions can be formed with these equilibrium points. One of the transfer action, up down to up up (UD2UU) with human simulated intelligent control (HSIC) theory based on sensor motor intelligent schema is studied. Control system designed by HSIC has following advantages: hierarchic control architecture with multi controller and multi control mode; hybrid control law combining close loop and open loop control as well as positive feedback and negative feedback control; associating schema based on characteristic model which takes charge of switch between each control mode. The design process of HSIC controller for UD2UU is discussed. The real time control results demonstrate the validation of the proposed theory and method.     

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.     

Analysis on Control Strategy for a Kind of Uncertain Complexity System

In practical industrial fields, it is very difficult to control uncertain complexity object effectively, because there are various uncertainties from internal dynamic characteristic and external, surroundings disturbance for controlled object. Its complexity is not only shown in structure, but also in information relation, so it is uneasy to implement it on technology. Aimed at selecting puzzle of control strategy for uncertain complexity system control, the authors describe cybernetics model based on analysis on some kinds of control strategy and contrastive research, and the model is applied in physical engineering system successfully. Finally, the authors point out that intelligent control strategy should be selected for uncertain complexity system control, and that Human Simulation Intelligent Control is a wise choice.     

Human simulated intelligent control for dynamic walking of biped robot

A human simulated intelligent control scheme based on sensory-motor schema for dynamic walking of biped robot is presented to solve the complex controller design.The dynamics model of a five-link planar biped is established. By imitating human walking movement and apprehending the posture change of the legs, the complex process of dynamic walking for biped robot is divided by four sequence phases. The multi-mode control law and the group of sensory-motor schema are designed based on the analysis of quality-quantity combination to the phase plane. Simulation results show that the proposed control strategy is effective to ensure continuous dynamic walking of biped robot and the control method is simple and easy to be realized.     

ENERGY-SAVING AND OPTIMAL HIERARCHICAL CONTROL OF WATER PLANT

In this paper, on condition that the production processes in water plant are related with each other, a new coordinative algorithm is presented, using a hierarchical control structure and taking the equivalent efficiency of the pump station as coordinative variable. In this way, the problem of optimal control of the complex delay system is solved. Simulation shows that great energy-saving efficiency may be obtained by the above method.     

Technologies of Intelligent Vehicle in Intelligent Transportation System (ITS)

Intelligent Transportation System (ITS) is the most effective method to solve the modern traffic problems. Intelligent vehicle plays a very important role on ITS, and it influences the success of ITS directly. Intelligent Transportation System (ITS) is briefly introduced. Then it is given that advanced vehicle control system and electrics stability program (ESP) in the key technology of intelligent transportation system. The control algorithm for vehicle movement is discussed and the test result is given.     

A novel control method for controllable reactor of transformer type

Conventional control methods for controllable reactor of transformer type generally depend on the parameters of transformer and cannot control capacitor voltage effectively. A novel control method for controllable reactor of transformer type is proposed. Based on a novel algorithm for fundamental extraction, the proposed approach can control the reactance accurately in real time mode by monitoring the condition of primary voltage and current. It also can control the capacitor voltage effectively and keep the capacitor voltage stable by introducing the feedback of capacitor voltage to control the phase of secondary current. Simulation has been carried out to proof the effectiveness of the method.     

Improvement Methods of Fuzzy Control with Intelligent Integration

Fuzzy controller can achieve satisfactory dynamic performance even for complicated process without mathematical model of the process.Its main imperfection is the existence of steady state error and ultimate oscillatory in the process output. This limits its application area. How to eliminate the steady state error while keep its good dynamic performance has been paid much attention. The strategy to introduce integral function to the conventional fuzzy control system is put forward in this paper to overcome the above problem. After expound two key factors on introducing integral action,an intelligent integral algorithm has been employed,and two methods on introduction of the intelligent integral to the fuzzy control system are proposed. Their characteristics and applied situations are summarized.     

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.     

Using a genetic algorithm to optimize secondary cooling water distribution in slab casting

A genetic algorithm was used for coding the volume of cooling water in the secondary cooling zone based on the heat transfer model in real time production. This was done to improve the dynamic control of the secondary cooling water in high efficiency continuous casting. The fitness function of multi objective optimization in the algorithm is in accordance with the distribution of metallurgical criteria. The genetic algorithm was integrated with the metallurgical criteria and the heat transferring model to optimize the water distribution. These steps increase the distribution adaptability and improve its efficiency compared to the traditional optimization methods of solving multi objective optimization and other non linear problems. Simulation using the process data of the No.2 slab caster in the Steelmaking Plant of Panzhihua Iron and Steel and on site testing were carried out. The results show that the optimized distribution saves 2% of water than without optimization, while water distribution along the slab to the water gradually decreases in accordance with requirements for slab quality control.     

A Kind of Adaptive Generalized Predictive Control for a Nonlinear System

This paper,using Hammerstein model to express a kind of nonlinear system,presents a new kind of Adaptive Generalized Predictive control for Nonlinear systems(NAGPC). This method divides the whole system into two parts: linear subsystem and nonlinear one. In the first part,a new algorithm ,whose computation quantity is less and speed is faster,is developed according to the characteristics of gain controlling Matrix F in the Generalized Predictive Control (GPC). Mean while, the interpolation method is used to develop one step root solving procedure and the result can be used as the whole system input in the second part. Simulation studies show that the general algorithm NAGPC is strong in robustness,good in stability and fast in computation.     

Option Pricing Using Quasi-Monte Carlo Simulation

This paper firstly introduces the method of option pricing using Monte Carlo,then,proposes one kind of Quasi-Monte Carlo Simulation,which uses Halton sequences to improve Monte Carlo Simulation.This paper also introduces generated rule of Halton Low Discrepancy Sequences and Moro algorithm.Finally,the performances of three kinds of Quasi-Monte Carlo method are compared.     

A Fault Location Algorithm for Transmission Lines in Circle Network

A fault location algorithm for transmission lines in circle network is presented.The algorithm uses local data. Its location accuracy is independent of power # flow, transient resistance and variation of remote system impedance. Simulation results are satisfactory.     

Using a Neural Network with Nonlinear Self-feedback to Solve the Maximum Clique Problem

As the MCP is NP-hard, an efficient approach to treating this problem is to design appropriate recurrent neural networks. We develop a new algorithm for the MCP, which can, to a certain extent, prevent the associated neural network from falling into local optimal points. The proposed algorithm incorporates nonlinear self-feedback into the SLDN algorithm and has distinguished dynamical characteristics. Simulation results show that the performance of proposed algorithm is statistically superior to the SLDN algorithm.     

Worst and Best Coverage Based on Probability Model of Wireless Sensor Network

The worst and best-case coverage algorithm is presented based on probability mode of wireless sensor network. The main idea is that choose these lattice points possibly with lowest detection probability by nodes to form the most breach path and choose those lattice points possibly with maximal detection probability by nodes to form the most support path. Simulation experiment validates the algorithm.     

Application of improved mean shiftalgorithm in rocket target tracking

An improved Mean shift based rocket target tracking algorithm is presented. The traditional Mean shift tracker is difficult in tracking fast moving rocket target and suffers in cumulating error. To overcome the limitation of the traditional Mean shift tracking algorithm, three frame difference method is employed to detect the rough position of rocket target. Based on the rough position, Mean shift algorithm is used to achieve precise tracking. Simulation experiments show that the proposed algorithm can effectively track fast moving rocket target and overcome the tracking error cumulating problems.     

Energy-aware scheduling of reward maximum in real-time systems

A novel algorithm is proposed for energy-aware scheduling of periodic tasks on variable voltage processors in the real-time systems. Based on the combined idea of artificial bee colony algorithm with greedy algorithm, different speed is allocated for different tasks to ensure the execution of tasks with higher value density. Simulation results show that the system reward of energy per unit is increased with the decreasing of energy constraint, therefore the scheduling algorithm is of the energy awareness. Compared with the greedy algorithm, the rewards of the real-time system using the greedy artificial bee colony algorithm are averagely improved by 78% while energy constraint factor is 0.1. Mmore system rewards can be obtained with the increasing number of tasks or energy constraints.     

基于智能算法优化SVM的横向通风过程中温度场预测方法探究

将改进的智能预测技术应用于储粮横向通风过程中的粮堆温度预测,为粮食通风智能预测与决策提供了一种新思路。选取河北清苑国家粮食储备库冬季横向通风的实时监测数据,在分析主要影响因素的基础上,应用三种智能优化算法——网格寻优算法、GA遗传算法寻优、PSO粒子群算法,结合回归支持向量机理论,对粮堆的通风过程进行建模。结果表明,优化过的回归预测模型能较好地拟合粮食温度与其他变量之间的非线性关系,尤其是当样本数量较为有限时,该方法具有更高的拟合精度,更适合对储粮通风这一强非线性过程的预测研究,对于人工干预操作具有一定的现实指导意义。     

Channel estimation for MB OFDM UWB system based on tap detecting

A novel channel estimation algorithm is proposed for MB OFDM Ultra Wide Band systems based on tap detecting by exploiting the sparse property of channel impulse response. The channel parameters are estimated using the DFT algorithm based on cyclic convolution property of the receive signals. The nonzero taps are detected by exploiting the matching pursuit(MP) algorithm. The new estimated channel parameters are obtained by forcing the zero taps to zeros. Simulation results demonstrate that it has better MSE (mean square error) performance for the four channels proposed by IEEE. Especially for CM1 and CM2 channels, the performance of the algorithm has been greatly improved.     

A cooperative localization algorithm for wireless sensor networks

Aiming at the shortcomings of the existing RSS(received signal strength) based localization algorithm for wireless sensor networks (WSN), a cooperative localization algorithm (CLA) is proposed. A reference anchor node is introduced to tolerant some minor error including the node position error. Dixon detection method is applied to remove abnormal RSS values, while the standard deviation threshold of RSS and learning model are introduced to reduce the RSS ranging error and effectively improve the precision. Simulation experiments are performed to evaluate the performance of the proposed algorithm. The results demonstrate that the localization accuracy is improved effectively, while the stability and robustness are better.