基于直流输电系统稳定性的反馈控制

在直流输电系统中,换流器速度的提高可以快速调整直流线路上的功率,从而提高交流系统的稳定性,增强输电能力.本文用动力系统的方法研究了直流输电系统的稳定性.在自治与非自治两种情况下对直流输电系统的数学模型作了理论上的分析,设计出了相应的反馈控制,该控制可以对直流换流器中的触发滞后角与触发越前角进行快速有效的调节,进而让换流器中直流电流指数地趋于稳定.     

基于电压电流反馈的调速系统稳定性分析

本文简要介绍了基于电压负反馈和电流正反馈的直流调速控制原理，分析了带电压负反馈和电流补偿控制调速系统的数学模型，着重分析和讨论了系统的稳定性，证明了全补偿的条件就是系统临界稳定的条件。并且就参数变化对控制系统的影响作了全面分析。     

A microfluidic chip for heterogeneous immunoassay using electrokinetical control

This article presents the development of a novel, automated, electrokinetically controlled heterogeneous immunoassay on a poly(dimethylsiloxane) (PDMS) microfluidic chip. A numerical method has been developed to simulate the electrokinetically driven, time-dependent delivery processes of reagents and washing solutions within the complex microchannel network. Based on the parameters determined from the numerical simulations, fully automated on-chip experiments to detect Helicobacter pylori were accomplished by sequentially changing the applied electric fields. Shortened assay time and much less reagent consumptions are achieved by using this microchannel chip while the detection limit is comparable to the conventional assay. There is a good agreement between the experimental result and numerical prediction, demonstrating the effectiveness of using CFD to assist the experimental studies of microfluidic immunoassay.     

GH 2 control for uncertain discrete-time-delay fuzzy systems based on a switching fuzzy model and piecewise Lyapunov function

Generalized H2 （GH2） stability analysis and controller design of the uncertain discrete-time Takagi-Sugeno （T-S） fuzzy systems with state delay are studied based on a switching fuzzy model and piecewise Lyapunov function. GH2 stability sufficient conditions are derived in terms of linear matrix inequalities （LMIs）. The interactions among the fuzzy subsystems are considered. Therefore, the proposed conditions are less conservative than the previous results. Since only a set of LMIs is involved, the controller design is quite simple and numerically tractable. To illustrate the validity of the proposed method, a design example is provided.     

Analytical decoupling control strategy using a unity feedback control structure for MIMO processes with time delays

An analytical decoupling control method is proposed for multiple-input–multiple-output (MIMO) processes with multiple time delays. The desired diagonal system transfer matrix is proposed first in terms of the H₂ optimal performance specification, resulting in the ideal desired decoupling controller matrix derived within the framework of a unity feedback control structure. It is demonstrated that dead-time compensators must be enclosed in the decoupling controller matrix to realize absolute decoupling for MIMO processes with multiple time delays. To alleviate the difficulties associated with the implementation, the ideal desired decoupling controller matrix is transformed into a practical form using an analytical approximation approach. Correspondingly, the stability of the resultant control system is assessed, together with its robust stability in the presence of process uncertainties. An on-line tuning rule for the single adjustable parameter of each column controllers in the decoupling controller matrix is given to cope with the process unmodeled dynamics. Finally, illustrative examples are given to show the superiority of the proposed method over the recently improved decoupling control methods.     

Active disturbance rejection based load frequency control and voltage regulation in power systems

An active disturbance rejection controller (ADRC) is developed for load frequency control (LFC) and voltage regulation respectively in a power system. For LFC, the ADRC is constructed on a three area interconnected power system. The control goal is to maintain the frequency at nominal value (60Hz in North America) and keep tie line power flow at scheduled value. For voltage regulation, the ADRC is applied to a static var compensator (SVC) as a supplementary controller. It is utilized to maintain the voltages at nearby buses within the ANSI C84.1 limits (or ±5% tolerance). Particularly, an alternative ADRC with smaller controller gains than classic ADRC is originally designed on the SVC system. From power generation and transmission to its distribution, both voltage and frequency regulating systems are subject to large and small disturbances caused by sudden load changes, transmission faults, and equipment loss/malfunction etc. The simulation results and theoretical analyses demonstrate the effectiveness of the ADRCs in compensating the disturbances and achieving the control goals.     

Full information and full control in a behavioral setting

In this paper, the concepts of Full Information and Full Control which arise in standard and control theory are extended to the behavioral framework. It is shown that the behavioral definitions are more fundamental than those given for the standard input/output case; in particular, the concept of state is not required and no a priori partition of the system variables into inputs and outputs needs to be performed. The interpretation of Full Information is to be able to reconstruct the error and disturbance variables from the control variables. The interpretation of Full Control is to be able to fully affect all the equations involving the error and the disturbance variables.     

Speech Detection in Non-Stationary Noise Based on the 1/f Process

In this paper,an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments.The Gaussian 1/f process ,a mathematical model for statistically self-similar radom processes based on fractals,is selected to model the speech and the background noise.An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties.Multiple templates are trained for the speech signal,and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise.In our experiments,a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method.A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB.     

Nonlinear diffusion process modeling using response surface methodology and variable transformation

The nonlinear process modeling is investigated using statistical design method and response surface methodology. Three input factors are examined with respect to the response factor. In order to minimize the joint confidence region of fabrication process with varying conditions, D-optimal experimental design technique is performed and diffusion rate is characterized by response model. Then, the statistical results are used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can be optimized process condition for semiconductor manufacturing.     

The use of representations of H in placing classical control problems in a modern control framework

Motivated by Zames' work on optimal sensitivity, it is shown that there exists in functional analysis a foundation, based upon the theory of representations of H^∞, upon which results may be developed to enable a range of classical control problems to be placed in a modern control framework.     

Output tracking control of a parallel-flow heat exchange process

In this paper, we consider an output tracking problem of a parallel-flow heat exchange process with distributed and boundary inputs. As the distributed inputs to the system, the output feedback control is first applied. Under zero boundary inputs, it is shown that the C₀-semigroup describing the closed-loop system satisfies the spectrum determined growth condition. Next, we apply a backstepping method to the design of the boundary inputs for output tracking. Our main result shows that the output of the system reaches a reference signal in finite time under both the output feedback control and the boundary control law derived by the backstepping method.     

Intrusion detection using text processing techniques with a kernel based similarity measure

This paper focuses on intrusion detection based on system call sequences using text processing techniques. It introduces kernel based similarity measure for the detection of host-based intrusions. The k-nearest neighbour (kNN) classifier is used to classify a process as either normal or abnormal. The proposed technique is evaluated on the DARPA-1998 database and its performance is compared with other existing techniques available in the literature. It is shown that this technique is significantly better than the other techniques in achieving lower false positive rates at 100% detection rate.     

Choice of approximator and design of penalty function for an approximate dynamic programming based control approach

This paper investigates the choice of function approximator for an approximate dynamic programming (ADP) based control strategy. The ADP strategy allows the user to derive an improved control policy given a simulation model and some starting control policy (or alternatively, closed-loop identification data), while circumventing the ‘curse-of-dimensionality’ of the traditional dynamic programming approach. In ADP, one fits a function approximator to state vs. ‘cost-to-go’ data and solves the Bellman equation with the approximator in an iterative manner. A proper choice and design of function approximator is critical for convergence of the iteration and the quality of final learned control policy, because an approximation error can grow quickly in the loop of optimization and function approximation. Typical classes of approximators used in related approaches are parameterized global approximators (e.g. artificial neural networks) and nonparametric local averagers (e.g. k-nearest neighbor). In this paper, we assert on the basis of some case studies and a theoretical result that a certain type of local averagers should be preferred over global approximators as the former ensures monotonic convergence of the iteration. However, a converged cost-to-go function does not necessarily lead to a stable control policy on-line due to the problem of over-extrapolation. To cope with this difficulty, we propose that a penalty term be included in the objective function in each minimization to discourage the optimizer from finding a solution in the regions of state space where the local data density is inadequately low. A nonparametric density estimator, which can be naturally combined with a local averager, is employed for this purpose.     

Rapid differentiation between E. coli and Salmonella Typhimurium using metal oxide sensors integrated with pattern recognition

A rapid method to differentiate between E coli and Salmonella Typhimurium was developed. E. coli and S. Typhimurium were separately grown in super broth and incubated at 37 °C. Super broth without inoculation of E. coli or S. Typhimurium was used as control. Numbers of E. coli and S. Typhimurium were followed using a colony counting method. Identification of the volatile metabolites produced by E. coli and S. Typhimurium was determined using solid-phase microextraction coupled with gas chromatography/mass spectrometry. An electronic nose with 12 non-specific metal oxide sensors was used to monitor the volatile profiles produced by E. coli and S. Typhimurium. Principal component analysis (PCA) and back-propagation neural network (BPNN) were used as pattern recognition tools. PCA was used for data exploration and dimensional reduction. PCA could visualize class separation between sample subgroups. The BPNN was shown to be capable of predicting the number of E. coli and S. Typhimurium. Good prediction was possible as measured by a regression coefficient (R² = 0.96) between true and predicted data. Using metal oxide sensors and pattern recognition techniques, it was possible to discriminate between samples containing E. coli from those containing S. Typhimurium.     

Design method for a new control system for an autonomous underwater vehicle using linear matrix inequalities

The independent administrative corporation Japan Agency for Marine–Earth Science and Technology (JAMSTEC) has developed a small light autonomous underwater vehicle (AUV) named marine robot experimental 1 (MR-X1).¹ The motion control of MR-X1 is considered in this article. Since the dynamics of MR-X1 mainly depends on its own speed, the motion control is a nonlinear control system. We propose a new controller design method for this system using linear matrix inequalities (LMIs). This algorithm gives a solution as a linear matrix inequality, and can be adapted to solve many LMIs simultaneously. LMIs can be obtained by substituting several speeds into the dynamics of the MR-X1. The proposed controller, which can be derived from the solution of the LMIs, was adapted to MR-X1 and showed good performance in experiments. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Dynamic features of flexible spacecraft control in process of its transformation into a large space structure

Consideration was given to dynamics of angular motion control of the flexible spacecraft reconstructed into a large space structure. In formal terms, this transformation lies in gradual reduction of the constructive rigidity to small values giving rise to low-frequency ( $ \tilde f Consideration was given to dynamics of angular motion control of the flexible spacecraft reconstructed into a large space structure. In formal terms, this transformation lies in gradual reduction of the constructive rigidity to small values giving rise to low-frequency ( ≤ 0.05 Hz) oscillations which represent one of the attributes of the class of large space structures. The existing quantitative definition of the large space structure was specified. It was demonstrated that as the frequencies of structure’s elastic oscillations approach those of the control of object “rigid” motion, a new kind of interrelations between the motions of both types, the so-called “capture” of the controller frequency by that of the elastic oscillations, arises which impairs control efficiency to the point of losing system stability. Analytical (for the linear control systems) and computer-aided (for the discrete systems) methods for determination of the boundaries separating the two qualitatively different forms of existence of the transformed elastic object were proposed. Some results of computer simulation of the orientation control of variable objects such as flexible spacecraft and large space structure were presented. Original Russian Text ? I.N. Krutova, V.M. Sukhanov, 2008, published in Avtomatika i Telemekhanika, 2008, No. 5, pp. 41–56. This work was supported by the Russian Foundation for Basic Research, project no. 05-08-18175.     

H∞ control design in bilinear systems: a tensor formal series approach

In this paper, the H_∞ disturbance attenuation problem of bilinear system is discussed. Dynamic game theory is used to solve this bilinear minimax problem. The solvability of H_∞ disturbance attenuation in bilinear system is also discussed. The techniques of tensor products and formal power series are employed to solve the nonlinear Bellman-Isaac differential equation. Furthermore, the convergence for the tensor formal series approach of this H_∞ control problem is discussed, and the radius of convergence for this H_∞ control to be well defined is also obtained.     

On the solution of coupled Riccati equations used in mixed H2 and H∞ optimal control

This paper considers a set of three coupled Riccati equations. The solution of these equations constitutes necessary conditions for mixed H₂ and H_∞ control problems. The main contributions of the paper are related to the conditions under which the solution of these equations is unique as well as to characterize necessary and sufficient conditions for the existence of solutions.     

Comparative study of black-box and hybrid estimation methods in fed-batch fermentation

A neural network based softsensor is proposed for a PHB fed-batch fermentation process. The softsensor is designed to estimate the biomass concentration on-line. The design is based on the following model structures: 1. a feedforward neural network, 2. a RBFN (radial basis function neural network) and 3. hybrid models composed of either feedforward or RBFN neural network and the a priori known dilution term of the mass balance equations. The different designs are experimentally implemented and compared using Alcaligenes eutrophus as a model fed-batch system. Additionally, the possibility of directly inferring the substrate (glucose) concentration from the estimated biomass was investigated by assessing the variability of the corresponding yield coefficient. The combination of the neural network model and mechanistic differential equation provided the best results. Because of the variability in the yield coefficient, substrate concentration could not be inferred directly.     

Architecture of electrospun carbon nanofibers–hydroxyapatite composite and its application act as a platform in biosensing

A biofunctional hybrid composite was constructed by assembling hydroxyapatite (HA) onto carboxylic group-functionalized carbon nanofibers (FCNFs). The FCNFs was obtained from acid treatment of carbon nanofibers (CNFs) which were synthesized by the combination of electrospinning and thermal treatment processes. The obtained carbon nanofibers–hydroxyapatite composite (FCNFs–HA) was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and FTIR spectroscopy. Cytochrome c (Cyt c) was successfully immobilized in this three-dimensional FCNFs–HA composite and the electron transfer rate constant (k_s) was evaluated to be 3.66 s⁻¹ according to Laviron's equation. And the surface coverage (Γ*) was estimated to be 8.1 × 10⁻¹⁰ mol cm⁻². Cyt c immobilized in FCNFs–HA composite exhibited a good electrocatalytic activity towards the reduction of hydrogen peroxide (H₂O₂). The catalytic current is linear to the H₂O₂ concentration in the range of 2.0 μM to 8.7 mM (r = 0.9996; n = 28), and the detection limit was 0.3 μM based on the criterion of a signal-to-noise ratio of 3.