Identification and PID Control for a Class of Delay Fractional-order Systems

In this paper, a new model identification method is developed for a class of delay fractional-order system based on the process step response. Four characteristic functions are defined to characterize the features of the normalized fractional-order model. Based on the time scaling technology, two identification schemes are proposed for parameters U+02BC estimation. The scheme one utilizes three exact points on the step response of the process to calculate model parameters directly. The other scheme employs optimal searching method to adjust the fractional order for the best model identification. The proposed two identification schemes are both applicable to any stable complex process, such as higher-order, under-damped U+002F over-damped, and minimum-phase U+002F nonminimum-phase processes. Furthermore, an optimal PID tuning method is proposed for the delay fractional-order systems. The requirements on the stability margins and the negative feedback are cast as real part constraints U+0028 RPC U+0029 and imaginary part constraints U+0028 IPC U+0029. The constraints are implemented by trigonometric inequalities on the phase variable, and the optimal PID controller is obtained by the minimization of the integral of time absolute error U+0028 ITAE U+0029 index. Identification and control of a Titanium billet heating process is given for the illustration. © 2014 Chinese Association of Automation.

Continuous-time System Identification with Nuclear Norm Minimization and GPMF-based Subspace Method

To improve the accuracy and effectiveness of continuous-time (CT) system identification, this paper introduces a novel method that incorporates the nuclear norm minimization (NNM) with the generalized Poisson moment functional (GPMF) based subspace method. The GPMF algorithm provides a simple linear mapping for subspace identification without the timederivatives of the input and output measurements to avoid amplification of measurement noise, and the NNM is a heuristic convex relaxation of the rank minimization. The Hankel matrix with minimized nuclear norm is used to determine the model order and to avoid the over-parameterization in subspace identification method (SIM). Furthermore, the algorithm to solve the NNM problem in CT case is also deduced with alternating direction methods of multipliers (ADMM). Lastly, two numerical examples are presented to evaluate the performance of the proposed method and to show the advantages of the proposed method over the existing methods. © 2014 Chinese Association of Automation.

Identification of Biocontrol Strain HJX1 and Determination of Its Antifungal Spectrum

A biocontrol strain HJX1,which had good inhibitory effect against Fusarium oxysporum f. sp. cubense,was identified as Bacillus subtilis based on traditional morphology,16 S rRNA gene sequence and fatty acid profiles. The sequence of 16 S rRNA gene had 99% homology to B. subtilis according to the DNA fragment of 1 521 bp amplified with a pair of universal primers,P0 and P6,of bacteria. Confront culture with pathogenic fungi showed that this strain had good inhibitory effect against 10 pathogenic fungi.

Identification and Control of Several Leaf Diseases of Poplar

This paper introduces the identification characteristics,occurrence regularity and control methods of several leaf diseases of poplar,including poplar angular leaf spot,poplar large spot,poplar leaf blight and poplar rust,in order to provide the basis for the healthy growth of poplar and the technical support for the sustainable development of forestry industry.

Identification of Quantitative Trait Loci Controlling Seed Physical and Nutrient Traits in Cotton

Cotton(Gossypium spp.) is the leading fiber crop,and an important source of the important edible oil and protein meals in the world.Complex genetics and strong environmental effects hinder

Hybrid force control of astronaut rehabilitative training robot under active loading mode

In order to mitigate the effects of space adaptation syndrome(SAS) and improve the training efficiency of the astronauts, a novel astronaut rehabilitative training robot(ART) was proposed. ART can help the astronauts to carry out the bench press training in the microgravity environment. Firstly, a dynamic model of cable driven unit(CDU) was established whose accuracy was verified through the model identification. Secondly, to improve the accuracy and the speed of the active loading, an active loading hybrid force controller was proposed on the basis of the dynamic model of the CDU. Finally, the actual effect of the hybrid force controller was tested by simulations and experiments. The results suggest that the hybrid force controller can significantly improve the precision and the dynamic performance of the active loading with the maximum phase lag of the active loading being 9° and the maximum amplitude error being 2% at the frequency range of 10 Hz. The controller can meet the design requirements.

Thruster direction controlling of assembled spacecraft based on gimbal suspension

The attitude control system design and its control effect are affected considerably by the mass-property parameters of the spacecraft. In the mission of on-orbit servicing, as fuel is expended, or the payloads are added or removed, the center of mass will be changed in certain axe; consequently, some thrusters＇ directions are deviated from the center of mass（CM） in certain plane. The CM of assembled spacecraft estimation and thruster direction control are studied. Firstly, the attitude dynamics of the assembled spacecraft is established based on the Newton-Euler method. Secondly, the estimation can be identified by the least recursive squares algorithm. Then, a scheme to control the thrusters＇ directions is proposed. By using the gimbal installed at the end of the boom, the angle of the thruster is controlled by driving the gimbal; therefore, thrusters can be directed to the CM again. Finally, numerical simulations are used to verify this scheme. Results of the numerical simulations clearly show that this control scheme is rational and feasible.

Analysis of Multiple Annular Pressure in Gas Storage Well and High-Pressure Gas Well

In gas storage or high-pressure gas wells,annular pressure is an unavoidable threat to safe,long-term resource production.The more complex situation,however,is multiple annular pressure,which means annular pressure happens in not only one annulus but two or more.Such a situation brings serious challenges to the identification of well integrity.However,few researches analyze the phenomenon of multiple annular pressure.Therefore,this paper studies the mechanism of multi-annular pressure to provide a foundation for its prevention and diagnosis.Firstly,the multi-annular pressure is classified according to the mechanism and field data.Then the failure mechanism and function of the wellbore safety barriers in the process of passage formation are analyzed.Finally,some suggestions are put forward for identifying and controlling multi-annular pressure.The results show that gas storage wells and high-pressure gas wells have the conditions to generate pressure channels,which leads to the expansion of annular pressure from a single annulus to multiple annuli.The pressure channel is composed of the tubing string,casing string,and a cement mantle,and the failures among the three have causal and hierarchical relationships.According to the channel direction,it can be divided into two types:tubing-casing annulus to casing annulus and casing annulus to the tubing-casing annulus,of which the former is more harmful.Some measures can be considered to prevent pressure channeling,including improvement of cementing quality,revision of maximum allowable annular pressure,and suitable frequency of pressure relief.

An Iterative Learning Approach to Identify Fractional Order KiBaM Model

This paper discusses the parameter and differentiation order identification of continuous fractional order KiBaM models in ARX (autoregressive model with exogenous inputs) and OE (output error model) forms. The least squares method is applied to the identification of nonlinear and linear parameters, in which the Grünwald-Letnikov definition and short memory principle are applied to compute the fractional order derivatives. An adaptive P-type order learning law is proposed to estimate the differentiation order iteratively and accurately. Particularly, a unique estimation result and a fast convergence speed can be arrived by using the small gain strategy, which is unidirectional and has certain advantages than some state-of-art methods. The proposed strategy can be successfully applied to the nonlinear systems with quasi-linear characteristics. The numerical simulations are shown to validate the concepts. © 2017 Chinese Association of Automation.

Analysis of external noise spectrum of high-speed railway

A schematic to make the spectra of the exterior noise of high speed railway was put forward. The exterior noise spectrum was defined based on the characteristics of the high-speed train exterior noise. Its characteristics considered here include identifying the exterior main sources and their locations, their frequency components including the Doppler effect due to the noise sources moving at high speed, the sound field intensity around the train in high-speed operation, the sound radiation path out of the train, and the pressure level and frequency components of the noise at the measuring points specified by the International Organization for Standardization(ISO). The characteristics of the high-speed train exterior noise of the high speed railways in operation were introduced. The advanced measuring systems and their principles for clearly indentifying the exterior noise sources were discussed in detail. Based on the concerned noise results measured at sites, a prediction model was developed to calculate the sound level and the characteristics of the exterior noise at any point where it is difficult to measure and to help to make the exterior noise spectrums. This model was also verified with the test results. The verification shows that there is a good agreement between the theoretical and experimental results.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

Landmarks in the application of electrical tomography in particle science and technology

Selected milestones in the development and use of electrical tomography in powder conveying, slurry processing and multi-phase flow are highlighted. The ability to map concentration in opaque mixtures under process-realistic conditions was a major innovation for the method and has had far reaching implications. Subsequent developments have enabled velocity information to be abstracted resulting in the ability to measure component flux and motion.