L2 Controller Design for Nonlinear Systems with Dead- Zone Modification

An adaptive robust L2design method with dead -zone modification is proposed for a class of nonlinear systems with external disturbances and uncertain parameters. With priori knowledge of the bounds of the parametric uncertainties, the smooth projection method and the dead zone modification technology are incorporated into the adaptive laws to prevent parameter drifting and reduce the computation burden. The number of parameter estimates is minimal for the adaptive system. The algorithm can guarantee the tracking errors asymptotically converge to the desired dead zone if the external disturbances are zero. The system satisfies L2 disturbance rejection criterion when the external disturbances are considered.     

AGGREGATION METHOD AND ITS APPLICATION IN SYSTEM DESIGN

A new aggregation method for order reduction of linear continuous or discrete systems is proposed in this paper. This method is easy for system design and it makes the structure of the controller and the state observer simple. It also reduces the computation of the system design.     

The Algorithm of Pole-Assignment Self Tuning Robust Control

An control algorithm with goed robustness has been developed,based on the combination of poletassignment self- tuning control algorithm with robust estimation and control al-gorithms,while the input signal and process parameters are varied on a large scale. The design of controller based on presented algorithm is simple,with good robustness for parameter changes and a strong ability for overcoming determined disturbance.The simulating research results proved the ef-fectiveness of the pole- assienment self-tuning robust control algorithm.     

A Kind of Robust Identification Algorithm Research

A new robust identification algorithm which is based on the estimation of parameter model,i.e.,Optimal Volume Ellipsoid parameter set identification algorithm is presented in this paper.It identifies not only a parameter point(the center of the ellipsoid) but also the error bounds of each parameter.It employs noise bounds in identification algorithm and dynamically modifies coefficients of identification convergent speed and direction in order to attain satisfactory perfomance.Digital simulation results show that is possesses high accuracy,dramatic convergent speed,excellent performance against noise and strong robustness as well as uniform convergent,which is obviously superior to traditional identification schemes.     

Saturation and Parameter Variation Influence Precision of Observer

The authors analyze the influence in vector controlled induction machine caused by parameter variation and main flux saturation affects speed estimation of adaptive state observer. According to simulation of the modified scheme with saturated induction motor model, it is proved that when the vector control scheme operates in field weakening region, the main flux saturation will obviously influence precision of speed observer and bring great error of speed estimation. Thereby, the main flux saturation should be compensated to improve performance of the vector control system.     

The Adaptive Robust Control Based On Synthetical Algorithm

An adaptive robust control based on synthetical algorithm was presented in this paper. The main character is to put the expert knowledge into parameter identification to realize the robust estimation, and combine the generalised minimum variance control with the pole assignment control for improving the robustness of the controller. Numerical simulation has proved that the control scheme suggested by authors quite prevailed over the general adaptive controller in control effects.     

PID Control for Multivariable System Based on Ameliorative RBF Neural Networks

Aiming at systems which are of characteristics of multi-input and multi-output, nonlinearity and time-variation in the industrial control fields, this paper presents a intelligent PID control method based on ameliorative RBF neural networks, which constructs RBF neural networks identifier on-line and identifies a controlled object on-line by means of adopting the nearest neighbor-clustering algorithm, and adjusts parameters of PID controller on-line and realizes decoupiing control of multivariable, nonlinear and time-variation system. The simulation result indicates that the controller can get parameters which are optimal under some control law, it makes the decoupled system, compared to the PID control method based on the conventional RBF neural networks, has perfect dynamic and static performances, possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.     

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.     

Parameter Modifiability Based Transformer Substation State Estimation

The accuracy of transformer substation state estimation is widely low,and one reason of that is inaccurate(parameter) of model.Recently,most of papers about state estimation(SE) focus on improving algorithm.Based on classic SE and parameter estimation(PE),this paper takes advantage of the methods that putting parameters into state vector and putting parameters both into state vector and measurement vector in discussing the influence of parameter on transformer substation SE via simulation.The result of simulation shows: First,the error of parameters will be too high if only putting them into state vector,although the result of SE will be much better.Second,putting parameters both into state vector and measurement vector and giving them appropriate coefficient will lead a better result that can be used by engineering.     

INDUSTRICAL OPTIMAL REGULATOR ADJUSTMENT

This paper presents a new practical method for PI or PID industrial regu-lator optimal adjustments.This method is well feasible for off-line theoreticaladjustment of a control system and provided for closed loop identification withon-line regulator tuning.The proposed algorithm is based on approximationinversion of s-domain disturbance property of a control system by shiftedChebyshev's series.The PI or PID regulator parameter optimization usings-domain approximation representation of time-domain performance index of acontrol system is exclusively discussed with an example of using the algorit-hm.The introduced method is proved convenient to industrial process withhigh-order mathematical model.And it has both theoretical and engineeringsignificance.     

A NEW SELF-TUNING PID CONTROL ALGORITHM BASED ON PROCESS PARMETER IDENTIFICATION

In this paper,the open loop response curve is computed by a computer after the process paramter estimatio The parameters of PI or PID controller are computed by the method of respose curve. Then in order to obtain better control performance, the parameters of PI or PID controller are gradually optimized.This algorithm is with wider application range compared with the other self-tuning PID control algorithms. The computer simulation and real-time control trials shows a good result.     

The design of torque dynamic sliding mode controller in permanent magnet synchronous generator for wind turbine

Permanent magnet synchronous generator for wind turbine is a multivariable nonlinear system with strong coupling. Considering the influence of the volatile parameters of the generator on the performance of vector control, the torque dynamic sliding-mode control strategy is further studied. The proposed first order dynamic sliding-mode control strategy reduces the chattering by using a new switch function constituted with derivative element. With the dynamic sliding-mode controller in the power outer loop, the PMSG vector control system shows excellent robustness and insensitivity to the parameters and disturbance, which is confirmed by simulation.     

Adaptive RBF networks backstepping control of robots with filtering reducer transmission

A mathematic model for filtering reducer transmission robots which have the characteristics of parameter uncertaintiesexternal disturbancestrong couplingnonlinearities and multi-variable is establishedand then an adaptive RBF networks backstepping method is proposed to control the robot.RBF is employed to approach the nonlinear item of the filtering reducer transmission robot on lineand an adaptive RBF network controller is developed based on backstepping method which effectively suppresses the effect of parameter uncertainties and external disturbance etc.It is combined with Lyapunov stabilization analytical method to demonstrate the close loop system convergence.Simulation results verify that the proposed controller not only satisfies high performance position trackingbut also has good accuracy and robustness.     

An Estimating Method of Ultimate Gain and Ultimate Period

At tuning PID controller,ultimate gain Ku and ultimate period Tu of the proceseare usually the desired parameters.To lessen disturbance of tuning action and av oid to drive thesvstem to the verge of instability,a new estimating methed of Ku and Tu is developed,which based onthe charactristic areas of the u nit step response of the procase,By this method,it is easy forauto-tuning PID controller with Ziegler-Nichols like tuning formula. Results of digital simulationshow that this method is effective.     

AN EXPERT CONTROLLER BASED ON PREDICTOR ALGORITHM

This paper presents a new method of expert controller, which is gotten by means of combining the technique of expert system with predictor control. The method adopts the multimode control as well as on-line characteristic identification so that the controller could not only give full play to the expert' s experience but also reflect the accuracy of predictor algorithm in control process. The simulation shows that the method has strong Robustness and good adaptability even when the object varies or the load changes suddenly, or something unexpected happenes with the system.     

Study of Multivariable Adaptive Fuzzy Control Algorithm With Hierarchical Structure

In this paper a class of multivariable fuzzy adaptive con trol algorithm withdecoupling design is propoed,The proposed simplification controller is based on the decompositiontheorem of muItivarinble fuzzy conditional statement andhierarchical multirules structure,Thefuzzy modification of controller's rule is achieved by looking for a medifiable coefficient table forrestraining disturbance.Simulation results demonstrate the effectiveness of the controller.     

Information Fusion Algorithm Based Evidence Combination

This paper applies the evidence combination theory to fuse the information of multi-neural network classifier. In order to make each classifier approach the ideal state, the heredity algorithm is applied to train it. The different capacity of each classifier is caused by different classified feature. Input feature can't be identified by one classifier and may be identified by another, Model identification can be performed by multi-classifier, output result can be thought of evidence, further more,the BPA of each classifier is determined, then the procession of the model identification must be improved.     

Application of state feedback and observer inmulti-axle vehicle steering system

A 3-degrees of freedom multi-axle vehicle dynamic model is established.Taking account of the practical difficulties for measuring the sideslip,rolling angles and rolling angle speed,a Luenberger observer is designed,and the simulation and analysis of the Luenberger observer are performed.The results show that the Luenberger observer has excellent characteristics in the considered speed range in tracking path.Then,a state feedback controller is designed on the basis of the Luenberger observer,and simulation of a multi-axle vehicle is also carried out.The results show that the performances of a multi-axle vehicle with the proposed control strategy is better than a multi-axle vehicle with front wheel steering strategy,and its control system has fine dynamic characteristic and strong robustness,so multi-axle vehicle with the proposed control strategy can improve vehicle handling stability and security.     

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.     

基于超静定方程的橡胶材料本构模型参数识别

由于橡胶材料具有非线性和大变形特性,使得描述橡胶力学特性的本构模型参数的确定比较烦琐和困难。为了提高橡胶本构模型参数识别的准确性,基于超静定方程求解原理推导出一种新的识别方法。以某橡胶衬套为例,识别的参数应用于有限元分析,对比试验数据,结果表明该识别方法可以准确识别橡胶材料参数,并且精度相对于最小二乘法有了明显改善,显示了超静定识别法的有效性和可靠性。