International Science Index

International Journal of Control and Information Engineering

Design of Explicit Model Predictive Controller for Hybrid Systems: Application to Dual Active Bridge Converter
The advancements in the technology of solid state switching devices have led to an increase in their applications across many fields. However, as these devices are discontinuous, they cause difficulties in modeling the system. Moreover, control of such a class of hybrid systems is also challenging. Though model predictive controllers (MPC) have been widely used for constrained optimization problems, this paper highlights their limitations for control of hybrid systems. The paper proposes an explicit MPC which solves an optimal control problem off-line, thereby resulting in reduction of computational efforts as well as time complexity. The proposed algorithm is implemented on a dual active bridge (DAB) converter, which is a prime component of the solid state transformer (SST). The control strategy is validated for scenarios such as reference tracking, input disturbance, load rejection and the results prove the effectiveness of the proposed controller.
Assessing Effects of an Intervention on Bottle-Weaning and Reducing Daily Milk Intake from Bottles in Toddlers Using Two-Part Random Effects Models
Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-log-normal random effects model, a two-part logit-truncated normal random effects model, a two-part logit-gamma random effects model, and a two-part logit-skew normal random effects model were used to examine effects of a bottle-weaning intervention on reducing bottle use and daily milk intake from bottles in toddlers aged 11 to 13 months in a randomized controlled trial. We show in all four two-part models that the intervention promoted bottle-weaning and reduced daily milk intake from bottles in toddlers drinking from a bottle. We also show that there are no differences in model fit using either the logit link function or the probit link function for modeling the probability of bottle-weaning in all four models. Furthermore, prediction accuracy of the logit or probit link function is not sensitive to the distribution assumption on daily milk intake from bottles in toddlers not off bottles.
A High Time Resolution Digital Pulse Width Modulator Based on FPGA's PLL Megafunction
The digital pulse width modulator (DPWM) is the crucial building block for digitally controlled DC-DC switching converter, which converts the digital duty ratio signal into its analog counterpart to control the power MOSFET transistors on or off. With the increase of switching frequency of digitally controlled DC-DC converter, the high time resolution DPWM is needed in order to avoid the limit cycle phenomenon which occurs when the resolution of DPWM is less than that of ADC. In this paper, a high time resolution DPWM with three-level hybrid structure is proposed, the first level is composed of an nc-bits counter and a comparator, the second one is an nl-bits delayline, and the third one is an nd-bits digital dither. The proposed DPWM is designed and implemented using the PLL megafunction of FPGA(Field Programmable Gate Arrays), and the required frequency of clock signal is 2nc times of switching frequency. In order to demonstrate the effectiveness of above DPWM structure, a 15-bits DPWM is designed and implemented, in which the parameter of nc, nl, and nd are chosen to be 7, 5, 3, respectively, and a "7-bits Counter + 5-bits PLL-delayline + 3-bits Dither" hybrid DPWM is designed. The second level is a traditional tapped delayline, five PLL blocks are used to realize 25 (nl=5) delay-cell. The third level is a digital dither, in which the least significant 3-bits are converted into a 1-bit signal and added to the most significant 12-bits of original digital input signal, thus the effective time resolution of DPWM can be increased. For a switching frequency of 2MHz and the duty cycle of 0.78%~100%, the time resolution of 15ps can be obtained with a maximum clock frequency of 256 MHz.
Digital Control Algorithm Based on Delta-Operator for High-Frequency DC-DC Switching Converters
For the digitally-controlled DC-DC switching converters, traditionally the z-domain discrete model is used to describe their digital control algorithms, and the transfer function of digital compensator is obtained by z-transform from its continuous model. However, with the switching frequency of DC-DC switching converters is increased to several MHz or more, the z-domain discrete model does not work very well, the following issues will occur. Firstly the z-domain discrete model does not converge to the corresponding s-domain continuous model, which will decrease the controlling accuracy of system; secondly, the poles of the transfer function in z-domain are closing to the unit circle in z-plane, which will degrade the stability of the system; and thirdly, the distance between the pole and zero of the transfer function in z-domain is becoming small, which will increase the sensitivity of poles to the finite word length of controlling coefficients, and in turn will degrade the stability of the system. In order to overcome above issues existing in the high-frequency digitally-controlled DC-DC switching converters, a novel digital control algorithm based on delta-operator is proposed in this paper. Since the relation between delta-operator and z-operator is delta=(z-1)/T, where T is the switching period (reciprocal of switching frequency), with the increase of the switching frequency the delta-domain discrete model converges to its s-domain continuous model, thus the stability and the controlling accuracy of feedback system are improved at high frequency. In this paper, the design method of digital voltage compensator in delta-domain using PID control algorithm is given, and its effectiveness is verified using Simulink platform simulation. In addition, the digital control algorithm based on delta-operator is easily implemented by hardware circuit. Compared with z-domain discrete model, both the stability and the controlling accuracy of the DC-DC switching converters are improved by using the digital control algorithm based on delta-operator without increasing the hardware circuit scale. Therefore it can be used for the design of digital voltage compensator especially in the high-frequency digitally-controlled DC-DC switching converters.
Bifurcation and Chaos of the Memristor Circuit
In this paper, a magnetron memristor model based on hyperbolic sine function is presented and proved the correctness by studying the trajectory of its voltage and current phase, and then a memristor chaotic system with the memristor model is proposed. The phase trajectories and the bifurcation diagrams and Lyapunov exponent spectrum of the magnetron memristor system are plotted by numerical simulation, and the chaotic evolution with changing the parameters of the system is also given. The paper includes numerical simulations and mathematical model, which confirming that the system has a wealth of dynamic behavior.
Detection of Abnormal Process Behavior in Copper Solvent Extraction by Principal Component Analysis
Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.
GPU Based Real-time Floating Object Detection System
A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme.
Chemical Reaction Algorithm for ExpectationMaximization Clustering
Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, the research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. Therefore, the property embedded in CRO is used to solve optimation problems. This paper proposes an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm: kmeans and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.
Parameterized Lyapunov Function Based Robust Diagonal Dominance Pre-Compensator Design for Linear Parameter Varying Model
For dynamic decoupling of linear parameter varying system, a robust dominance pre-compensator design method is given. The parameterized pre-compensator design problem is converted into optimal problem constrained with parameterized linear matrix inequalities (PLMI); To solve this problem, firstly, this optimization problem is equivalently transformed into a new form with elimination of coupling relationship between parameterized Lyapunov function (PLF) and pre-compensator. Then the problem was reduced to a normal convex optimization problem with normal linear matrix inequalities (LMI) constraints on a newly constructed convex polyhedron. Moreover, a parameter scheduling pre-compensator was achieved, which satisfies robust performance and decoupling performances. Finally, the feasibility and validity of the robust diagonal dominance pre-compensator design method are verified by the numerical simulation of a turbofan engine PLPV model.
An Adaptive Controller Method Based on Full-State Linear Model of Variable Cycle Engine
Due to the more variable geometry parameters of VCE (variable cycle aircraft engine), presents an adaptive controller method based on the full-state linear model of VCE and has simulated to solve the multivariate controller design problem of the whole flight envelops. First, analyzes the static and dynamic performances of bypass ratio and other state parameters caused by variable geometric components, and develops nonlinear component model of VCE. Then based on the component model, through small deviation linearization of main fuel (Wf), the area of tail nozzle throat (A8) and the angle of rear bypass ejector (A163), setting up multiple linear model which variable geometric parameters can be inputs. Second, designs the adaptive controllers for VCE linear models of different nominal points. Among them, considering of modeling uncertainties and external disturbances, derives the adaptive law by lyapunov function. The simulation results showed that, the adaptive controller method based on full-state linear model used the angle of rear bypass ejector as input and effectively solved the multivariate control problems of VCE. The performance of all nominal points could track the desired closed-loop reference instructions. The adjust time was less than 1.2s, and the system overshoot was less than 1%, at the same time, the errors of steady states were less than 0.5% and the dynamic tracking errors were less than 1%. In addition, the designed controller could effectively suppress interference and reached the desired commands with different external random noise signals.
Experimenting with Error Performance of Systems Employing Pulse Shaping Filters on a Software-Defined-Radio Platform
This paper presents experimental results on testing the symbol-error-rate (SER) performance of quadrature amplitude modulation (QAM) systems employing symmetric pulse-shaping square-root (SR) filters designed by minimizing the roughness function and by minimizing the peak-to-average power ratio (PAR). The device used in the experiments is the 'bladeRF' software-defined-radio platform. PAR is a well-known measurement, whereas the roughness function is a concept for measuring the jitter-induced interference. The experimental results show that the system employing minimum-roughness pulse-shaping SR filters outperforms the system employing minimum-PAR pulse-shaping SR filters in the sense of SER performance.
Technical and Economic Analysis Effects of Various Parameters on the Performance of Heat Recovery System on Gas Complex Turbo Generators
This paper deals with the technical and economic effects of various parameters on the performance of heat recovery system on gas complex turbo generator. Given the importance of this issue, that is the main goal of economic efficiency and reduces costs; this project has been implemented similar plans in which the target is the implementation of specific patterns. The project will also help us in the process of gas refineries and the actual efficiency of the process after adding a system to analyze the turbine and predict potential problems and fix them and take appropriate measures according to the results of simulation analysis and results of the process gain. The results of modeling and the effect of different parameters on this line, have been done using Thermo Flow.
On the Use of Analytical Performance Models to Design a High-Performance Active Queue Management Scheme
One of the open issues in Random Early Detection (RED) algorithm is how to set its parameters to reach high performance for the dynamic conditions of the network. Although original RED uses fixed values for its parameters, this paper follows a model-based approach to upgrade performance of the RED algorithm. It models the routers queue behavior by using the Markov model and uses this model to predict future conditions of the queue. This prediction helps the proposed algorithm to make some tunings over RED's parameters and provide efficiency and better performance. Widespread packet level simulations confirm that the proposed algorithm, called Markov-RED, outperforms RED and FARED in terms of queue stability, bottleneck utilization and dropped packets count.
Dynamic Fault Diagnosis for Semi-Batch Reactor Under Closed-Loop Control via Independent RBFNN
In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.
A Novel Combination Method for Computing the Importance Map of Image
The importance map is an image-based measure and is a core part of the resizing algorithm. Importance measures include image gradients, saliency and entropy, as well as high level cues such as face detectors, motion detectors and more. In this work we proposed a new method to calculate the importance map, the importance map is generated automatically using a novel combination of image edge density and Harel saliency measurement. Experiments of different type images demonstrate that our method effectively detects prominent areas can be used in image resizing applications to aware important areas while preserving image quality.
Adaptive Backstepping Control of Uncertain Nonlinear Systems with Input Backlash
In this paper a generic model of perturbed nonlinear systems is considered which is affected by hard backlash nonlinearity at the input. The nonlinearity is modelled by a dynamic differential equation which presents a more precise shape as compared to the existing linear models and is compatible with nonlinear design technique such as backstepping. Moreover, a novel backstepping based nonlinear control law is designed which explicitly incorporates a continuous-time adaptive backlash inverse model. It provides a significant flexibility to control engineers, whereby they can use the estimated backlash spacing value specified on actuators such as gears etc. in the adaptive Backlash Inverse model during the control design. It ensures not only global stability but also stringent transient performance with desired precision. It is also robust to external disturbances upon which the bounds are taken as unknown and traverses the backlash spacing efficiently with underestimated information about the actual value. The continuous-time backlash inverse model is distinguished in the sense that other models are either discrete-time or involve complex computations. Furthermore, numerical simulations are presented which not only illustrate the effectiveness of proposed control law but also its comparison with PID and other backstepping controllers.
Developing NAND Flash-Memory SSD-Based File System Design
This paper focuses on I/O optimizations of N-hybrid (New-Form of hybrid), which provides a hybrid file system space constructed on SSD and HDD. Although the promising potentials of SSD, such as the absence of mechanical moving overhead and high random I/O throughput, have drawn a lot of attentions from IT enterprises, its high ratio of cost/capacity makes it less desirable to build a large-scale data storage subsystem composed of only SSDs. In this paper, we present N-hybrid that attempts to integrate the strengths of SSD and HDD, to offer a single, large hybrid file system space. Several experiments were conducted to verify the performance of N-hybrid.
Feature Extraction and Classification Based on the Bayes Test for Minimum Error
Classification with a dimension reduction based on Bayesian approach is proposed in this paper . The first step is to generate a sample (parameter) of fault-free mode class and faulty mode class. The second, in order to obtain good classification performance, a selection of important features is done with the discrete karhunen-loeve expansion. Next, the Bayes test for minimum error is used to classify the classes. Finally, the results for simulated data demonstrate the capabilities of the proposed procedure.
Modeling and Control Design of a Centralized Adaptive Cruise Control System
A vehicle driving with an Adaptive Cruise Control System (ACC) is usually controlled decentrally, based on the information of radar systems and in some publications based on C2X-Communication (CACC) to guarantee stable platoons. In this paper, we present a Model Predictive Control (MPC) design of a centralized, server-based ACC-System, whereby the vehicular platoon is modeled and controlled as a whole. It is then proven that the proposed MPC design guarantees asymptotic stability and hence string stability of the platoon. The Networked MPC design is chosen to be able to integrate system constraints optimally as well as to reduce the effects of communication delay and packet loss. The performance of the proposed controller is then simulated and analyzed in an LTE communication scenario using the LTE/EPC Network Simulator LENA, which is based on the ns-3 network simulator.
A Sub-Scalar Approach to the MIPS Architecture
The continuous researches in the field of computer architecture basically aims at accelerating the computational speed and to gain enhanced performance. In this era, the superscalar, sub-scalar concept has not gained enough attention for improving the computation performance. In this paper, we have presented a sub-scalar approach to utilize the parallelism present with in the data while processing. The main idea is to split the data into individual smaller entities and these entities are processed with a defined known set of instructions. This sub-scalar approach to the MIPS architecture can bring out significant improvement in the computational speedup. MIPS-I is the basic design taken in consideration for the development of sub-scalar MIPS64 for increasing the instruction level parallelism (ILP) and resource utilization.
Two Wheels Differential Type Odometry for Robot
This paper proposes a new type of two wheels differential type odometry to estimate the next position and orientation of mobile robots. The proposed odometry is composed for two independent wheels with respective encoders. The two wheels rotate independently, and the change is determined by the difference in the velocity of the two wheels. Angular velocities of the two wheels are measured by rotary encoders. A mathematical model is proposed for the mobile robots to precisely move towards the goal. Using measured values of the two encoders, the current displacement vector of a mobile robot is calculated by kinematics of the mathematical model. Using the displacement vector, the next position and orientation of the mobile robot are estimated by proposed odometry. Result of simulator experiment by the developed odometry is shown.
Generalized Mean-Field Theory of Phase Unwrapping via Multiple Interferograms
On the basis of Bayesian inference using the maximizer of the posterior marginal estimate, we carry out phase unwrapping using multiple interferograms via generalized mean-field theory. Numerical calculations for a typical wave-front in remote sensing using the synthetic aperture radar interferometry, phase diagram in hyper-parameter space clarifies that the present method succeeds in phase unwrapping perfectly under the constraint of surface- consistency condition, if the interferograms are not corrupted by any noises. Also, we find that prior is useful for extending a phase in which phase unwrapping under the constraint of the surface-consistency condition. These results are quantitatively confirmed by the Monte Carlo simulation.