Volume 11, Number 1, 2014
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The high redundancy actuator (HRA) concept is a novel approach to fault tolerant actuation that uses a high number of small actuation elements, assembled in series and parallel in order to form a single actuator which has intrinsic fault tolerance. Whilst this structure affords resilience under passive control methods alone, active control approaches are likely to provide higher levels of performance. A multiple-model control scheme for an HRA applied through the framework of multi-agent control is presented here. The application of this approach to a 1010 HRA is discussed and consideration of reconfiguration delays and fault detection errors are made. The example shows that multi-agent control can provide tangible performance improvements and increase fault tolerance in comparison to a passive fault tolerant approach. Reconfiguration delays are shown to be tolerable, and a strategy for handling false fault detections is detailed.
Cooperative adaptive cruise control (CACC) vehicles are intelligent vehicles that use vehicular ad hoc networks (VANETs) to share traffic information in real time. Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration. Since reaching a high CACC market penetration level is not occurring in the near future, this study presents a progressive deployment approach that demonstrates to have a great potential of reducing traffic congestions at low CACC penetration levels. Using a previously developed microscopic traffic simulation model of a freeway with an on-ramp created to induce perturbations and trigger stop-and-go traffic, the CACC system's effect on the traffic performance is studied. The results show significance and indicate the potential of CACC systems to improve traffic characteristics which can be used to reduce traffic congestion. The study shows that the impact of CACC is positive and not only limited to a high market penetration. By giving CACC vehicles priority access to high-occupancy vehicle (HOV) lanes, the highway capacity could be significantly improved with a CACC penetration as low as 20%.
This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system. First, db3 wavelet is used to decompose and reconstruct time-delay sequence, and the approximation component and detail components of time-delay sequences are figured out. Next, one step prediction of time-delay is obtained through echo state network (ESN) model and auto-regressive integrated moving average model (ARIMA) according to the different characteristics of approximate component and detail components. Then, the final predictive value of time-delay is obtained by summation. Meanwhile, the parameters of echo state network is optimized by genetic algorithm. The simulation results indicate that higher accuracy can be achieved through this prediction method.
The bi-conjugate gradients (Bi-CG) and bi-conjugate residual (Bi-CR) methods are powerful tools for solving nonsymmetric linear systems Ax=b. By using Kronecker product and vectorization operator, this paper develops the Bi-CG and Bi-CR methods for the solution of the generalized Sylvester-transpose matrix equation i=1p(AiXBi+CiXTDi=E (including Lyapunov, Sylvester and Sylvester-transpose matrix equations as special cases). Numerical results validate that the proposed algorithms are much more efficient than some existing algorithms.
In this paper, an analysis for ill conditioning problem in subspace identification method is provided. The subspace identification technique presents a satisfactory robustness in the parameter estimation of process model which performs control. As a first step, the main geometric and mathematical tools used in subspace identification are briefly presented. In the second step, the problem of analyzing ill-conditioning matrices in the subspace identification method is considered. To illustrate this situation, a simulation study of an example is introduced to show the ill-conditioning in subspace identification. Algorithms numerical subspace state space system identification (N4SID) and multivariable output error state space model identification (MOESP) are considered to study, the parameters estimation while using the induction motor model, in simulation (Matlab environment). Finally, we show the inadequacy of the oblique projection and validate the effectiveness of the orthogonal projection approach which is needed in ill-conditioning; a real application dealing with induction motor parameters estimation has been experimented. The obtained results proved that the algorithm based on orthogonal projection MOESP, overcomes the situation of ill-conditioning in the Hankel's block, and thereby improving the estimation of parameters.
This paper presents an integrated approach based on dynamic inversion (DI) and active disturbance rejection control (ADRC) to the entry attitude control of a generic hypersonic vehicle (GHV). DI is firstly used to cancel the nonlinearities of the GHV entry model to construct a basic attitude controller. To enhance the control performance and system robustness to inevitable disturbances, ADRC techniques, including the arranged transient process (ATP), nonlinear feedback (NF), and most importantly the extended state observer (ESO), are integrated with the basic DI controller. As one primary task, the stability and estimation error of the second-order nonlinear ESO are analyzed from a brand new perspective: the nonlinear ESO is treated as a specific form of forced Linard system. Abundant qualitative properties of the Linard system are utilized to yield comprehensive theorems on nonlinear ESO solution behaviors, such as the boundedness, convergence, and existence of periodic solutions. Phase portraits of ESO estimation error dynamics are given to validate our analysis. At last, three groups of simulations, including comparative simulations with modeling errors, Monte Carlo runs with parametric uncertainties, and a six degrees-of-freedom reference entry trajectory tracking are executed, which demonstrate the superiority of the proposed integrated controller over the basic DI controller.
The performance of smart structures in trajectory tracking under sub-micron level is hindered by the rate-dependent hysteresis nonlinearity. In this paper, a Hammerstein-like model based on the support vector machines (SVM) is proposed to capture the rate-dependent hysteresis nonlinearity. We show that it is possible to construct a unique dynamic model in a given frequency range for a rate-dependent hysteresis system using the sinusoidal scanning signals as the training set of signals for the linear dynamic subsystem of the Hammerstein-like model. Subsequently, a two-degree-of-freedom (2DOF) H robust control scheme for the rate-dependent hysteresis nonlinearity is implemented on a smart structure with a piezoelectric actuator (PEA) for real-time precision trajectory tracking. Simulations and experiments on the structure verify both the effectiveness and the practicality of the proposed modeling and control methods.
Video surveillance is an active research topic in computer vision. In this paper, humans and cars identification technique suitable for real time video surveillance systems is presented. The technique we proposed includes background subtraction, foreground segmentation, shadow removal, feature extraction and classification. The feature extraction of the extracted foreground objects is done via a new set of affine moment invariants based on statistics method and these were used to identify human or car. When the partial occlusion occurs, although features of full body cannot be extracted, our proposed technique extracts the features of head shoulder. Our proposed technique can identify human by extracting the human head-shoulder up to 60%-70% occlusion. Thus, it has a better classification to solve the issue of the loss of property arising from human occluded easily in practical applications. The whole system works at approximately 16-29fps and thus it is suitable for real-time applications. The accuracy for our proposed technique in identifying human is very good, which is 98.33%, while for cars' identification, the accuracy is also good, which is 94.41%. The overall accuracy for our proposed technique in identifying human and car is at 98.04%. The experiment results show that this method is effective and has strong robustness.
According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process, a combustion working condition recognition method based on the generalized learning vector (GLVQ) neural network is proposed. Firstly, the numerical flame image is analyzed to extract texture features, such as energy, entropy and inertia, based on grey-level co-occurrence matrix (GLCM) to provide qualitative information on the changes in the visual appearance of the flame. Then the kernel principal component analysis (KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly. Finally, the GLVQ neural network is trained by using the normalized texture feature data. The test results show that the proposed KPCA-GLVQ classifier has an excellent performance on training speed and correct recognition rate, and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.
We present two haze removal algorithms for single image based on haziness analysis. One algorithm regards haze as the veil layer, and the other takes haze as the transmission. The former uses the illumination component image obtained by retinex algorithm and the depth information of the original image to remove the veil layer. The latter employs guided filter to obtain the refined haze transmission and separates it from the original image. The main advantages of the proposed methods are that no user interaction is needed and the computing speed is relatively fast. A comparative study and quantitative evaluation with some main existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods. On the top of haze removal, several applications of the haze transmission including image refocusing, haze simulation, relighting and 2-dimensional (2D) to 3-dimensional (3D) stereoscopic conversion are also implemented.
In this paper, we study the problem of optimal resource allocation for lifetime maximization in an orthogonal-frequency-division multiplexing (OFDM) system with decode-and-forward relay. The goal is to minimize total energy cost of the system by jointly optimizing power allocation, subcarrier pairing and relay selection. We present a heuristic solution that is composed of two parts. The first part is an optimal power allocation approach to allocate power to a subcarrier pair of the source and the relay. The second part is a modified Hungarian algorithm to make subcarrier pairing and relay selection. Evaluations show that the presented scheme outperforms other schemes in the total transmitted data and the network lifetime.
An efficient hop count route finding approach for mobile ad hoc network is presented in this paper. It is an adaptive routing protocol that has a tradeoff between transmission power and hop count for wireless ad hoc networks. During the route finding process, the node can dynamically assign transmission power to nodes along the route. The node who has received route request message compares its power with the threshold power value, and then selects a reasonable route according to discriminating algorithms. This algorithm is an effective solution scheme to wireless ad hoc networks through reasonably selected path to reduce network consumption. Simulation results indicate that the proposed protocol can deliver better performances with respect to energy consumption and end-to-end delay.
In this paper, the problem of time varying telecommunication delays in passive teleoperation systems is addressed. The design comprises delayed position, velocity and position-velocity signals with the local position and velocity signals of the master and slave manipulators. Nonlinear adaptive control terms are employed locally to cope with uncertain parameters associated with the gravity loading vector of the master and slave manipulators. Lyapunov-Krasovskii function is employed for three methods to establish asymptotic tracking property of the closed loop teleoperation systems. The stability analysis is derived for both symmetrical and unsymmetrical time varying delays in the forward and backward communication channel that connects the local and remote sites. Finally, evaluation results are presented to illustrate the effectiveness of the proposed design for real-time applications.
Currently, the cloud computing systems use simple key-value data processing, which cannot support similarity search effectively due to lack of efficient index structures, and with the increase of dimensionality, the existing tree-like index structures could lead to the problem of the curse of dimensionality. In this paper, a novel VF-CAN indexing scheme is proposed. VF-CAN integrates content addressable network (CAN) based routing protocol and the improved vector approximation file (VA-file) index. There are two index levels in this scheme: global index and local index. The local index VAK-file is built for the data in each storage node. VAK-file is the k-means clustering result of VA-file approximation vectors according to their degree of proximity. Each cluster forms a separate local index file and each file stores the approximate vectors that are contained in the cluster. The vector of each cluster center is stored in the cluster center information file of corresponding storage node. In the global index, storage nodes are organized into an overlay network CAN, and in order to reduce the cost of calculation, only clustering information of local index is issued to the entire overlay network through the CAN interface. The experimental results show that VF-CAN reduces the index storage space and improves query performance effectively.
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