Volume 12, Number 5, 2015
Constructive nonlinear control design has undergone rapid and significant progress over the last three decades. In this paper, a review of recent results in this important field is presented with a focus on interdisciplinary topics at the interface of control, computing and communications. In particular, it is shown that the nonlinear small-gain theory provides a unified framework for solving problems of quantized feedback stabilization and event-triggered control for nonlinear systems. Some open questions in quantized and networked nonlinear control systems are discussed.
To overcome the conflict between the global robustness and the local accuracy in the dense nonrigid image registration, we propose a union registration approach using a l1-norm based term to couple the parametric transformation and the non-parametric transformation. On one hand, we take the parametric deformation field as a constraint for the non-parametric registration, which is a strong constraint that guarantees the robustness of the non-parametric registration result. On the other hand, the non-parametric deformation field is taken as a force to improve the accuracy of the parametric registration. Then, an alternating optimization scheme is carried out to improve the accuracy of both the parametric registration and the non-parametric registration. Moreover, accounting for the effect of spatially-varying intensity distortions and the sparse gradient prior of the deformation field, we adopt the residual complexity (RC) as the similarity metric and the total variation (TV) as the regularization. Because of the TV-l1-l2 composite property of the objective function in our union image registration, we resort to the split Bregman iteration for the complex problem solving. Experiments with both synthetic and real images in different domains illustrate that this approach outperforms the separately performed parametric registration or non-parametric registration.
This paper introduces a recursive identification methods toolbox (called RIM) running under Matlab environment for dynamic system identification from available data. The RIM includes many methods which are generally used. The RIM helps users to validate the theoretical results and to carry out comparison between identifications methods without the need of algorithms programming. Furthermore, the RIM can be used as an education platform to study the identification parameters effect on model validity and results accuracy. To show its performance and capability, the RIM is evaluated through many application examples.
In a low energy moon return mission, due to the weak stability of the orbit, it is necessary to implement an accurate orbital maneuver to guarantee a successful return. During the process of getting the optimal thrust control with these two kinds of methods, it is hard to guess the initial value of the co-states in the indirect method while a large amount of calculation is needed to insure the precision in the direct method. To solve the problem, in this paper a combined method is given which has the merit of both direct and indirect methods. In this method, the virtual satellite method (VSM) and the Gauss pseudo-spectral method (GPM) are applied, while the fuel optimal control strategy is computed with GPM to carry out a soft rendezvous between the spacecraft and a hypothetical virtual satellite running on the nominal low energy return orbit so that the spacecraft will enter the return orbit accurately. Compared with the direct and indirect methods, this combined method can avoid guessing the initial value of the co-states and the complexity of calculation is acceptable. According to the simulation results, the spacecraft is inserted to the target return orbit with a high accuracy and also the optimization is very effective.
Based on a nonlinear flight dynamic model with aerodynamic coefficients and external disturbance uncertainties, which is a typical cyber physical system, a filtering backstepping terminal sliding mode control method is proposed for a robust controller. The tracking differentiator can provide the capability of solving the problem of "complexity explosion" in backstepping controllers to simplify the backstepping implementation. Nonlinear disturbance observers are used to observe the uncertainties of the nonlinear flight dynamic system. The terminal sliding mode controller is designed to improve its convergence rate and the tracking accuracy. Finally, nonlinear 6-degree-of-freedom simulation results for an F-16 aircraft model elaborate the effectiveness of the proposed control system.
A naïve solver is one approach that can be used to identify prospective solutions based on data on (or projected to be on) a Blackboard Architecture's blackboard. The naïve solver approach doesn't implement heuristics or other techniques to determine what solution paths to attempt first. Instead, it runs the blackboard forward (simulating what would occur if data were gradually added to the blackboard at a faster-than-real time rate). The approach doesn't guarantee that an optimal solution will be found and will need to be run repetitively to create multiple solutions for comparison. This paper assesses the effect of pre-pruning the blackboard's facts and rules to remove those that are not relevant (e.g., facts that cannot be asserted, rules that cannot be triggered) or which produce irrelevant facts and pruning actions that produce irrelevant facts (and/or trigger other similarly useless actions). It describes the Blackboard implementation and its utility, explains the pruning process used and presents quantitative and qualitative assessment of the utility of pruning to a naïve solver's operations. This value is extrapolated to facilitate consideration of a more robust pruning process which also removes low-value facts, actions and rules in addition to those being removed due to their uselessness.
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy, efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical, finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB), instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.
In this paper, we study the propagation of road hazard information to vehicles which enter the hazard segment of a highway in a sparse 1D vehicular ad hoc network (VANET) with store-and-forward mechanism. Store-and-forward is an option for message propagation in sparse vehicular networks where connectivity is intermittent. Upon receiving the message, the vehicle becomes an informed vehicle, it carries the message for a while and then forwards it to the approaching vehicles which are about to enter the highway segment. In this way, a platoon of informed vehicles is formed. We establish an analytical model to obtain the probability that a vehicle receives the message and joins the informed platoon. Moreover, we prove that traffic dynamics increase the reception probability of messages. We find the expected message propagation delay in the platoon using the store-and-forward policy. We also show that the propagation delay in store-and-forward inter-vehicle communications is tightly related to traffic parameters such as traffic flow rate and vehicle speeds on the highway. Results show that for smaller transmission ranges, smaller platoons are formed, the expected message propagation delay in the platoon is low, and it increases very slightly as the traffic flow rate increases. But for larger transmission ranges, larger platoons are formed, the expected delay is high, and it increases remarkably with a small increase in the traffic flow rate. The impacts of some network and traffic parameters such as transmission range, speed of vehicles, and highway speed limits on the message propagation are investigated as well. Finally, the accuracy of the analytical results is evaluated by an extensive simulation study.
Direct perfusion of three-dimensional cell-seeded biological scaffolds is known to enhance osteogenesis, which can be partly attributed to mechanical stimuli affecting cell proliferation and differentiation in the process of bone tissue regeneration. This study aimed to compare the hydrodynamic environment, including the distributions of fluid flow velocity, wall shear stress and pressure in pores filled with liquid, designed scaffold (DS), porous and biodegradable β-TCP (β-tricalcium phosphate) based on freeze-drying scaffold (FS) and dog's femora scaffold (NS). Gravity condition, inlet velocities of 1, 10, 100 and 1000, μm/s and medium viscosities of 1.003, 1.45 and 2.1, mPas were applied as the initial conditions. With an inlet fluid velocity of 100 m/s and a viscosity of 1.45 (10-3, Pas, the simulation results of maximal and average wall shear stress were 15.675, mPas and 3.223, mPas for DS, 67.126, mPas and 5.949, mPas for FS, and 20.190, mPas and 1.629, mPas for NS. Variations of inlet fluid velocity and fluid viscosity produced corresponding proportional changes in fluid flow velocity, wall shear stress and pressure. DS and FS were evaluated in terms of simulation results and microstructure using NS as a reference standard. This methodology allows a greater insight into the complex concept of tissue engineering and will likely help in understanding and eventually improving the fluid-mechanical aspects.
Reduction of error due to the influence of temperature on the quartz flexible accelerometer without any heating device is a difficult task, and is also a tendency for research and application. In this paper, static and dynamic temperature compensation models are established in order to reduce the temperature influence on accelerometer measurement accuracy. Combined with the experiment data, the relationship between the accelerometer output accuracy, temperature and the magnitude of acceleration is analyzed. The data collected from the temperature experiment show that output value of the accelerometer varies with temperature. The method of uniaxial quadrature experiment is adopted and the accelerometer output value is gauged at temperature ranging from -20℃ to 50℃. Having used the static and the dynamic temperature compensation models, the accelerometer temperature error compensation experiment is conducted and the compensated errors by the two models are analyzed. The result shows that the compensated value meets the technical requirements. Two technical indicators, the zero bias K0 and the scaling factor K1, which are used to measure the degree of accelerometers, are both improved and their fluctuation ranges are reduced.
The enhancement technique for color medical images is conductive to improve the resolution and accuracy of the original image. A new enhancement method combining the Young-Helmholtz (Y-H) transformation with the adaptive equalization of intensity numbers matrix histogram is proposed in this paper. The adaptive histogram equalization method is applied to strengthen the details, enhance the contrast, and suppress the noise of the original image effectively. The enhanced image can be displayed in the red-greenblue (RGB) color space through inverse Y-H transformation with the same hue and saturation. The experiment results demonstrate that the method has the enhancement effect with low computational complexity, which provides the foundation for the medical diagnosis and further processing of medical images.
The adaptive tracking problem for uncertain flexible joint robot system is studied in this paper. By utilizing the adaptive backstepping method, an adaptive controller is constructed at the beginning. By utilizing the modified adaptive dynamic surface control technique, a new adaptive controller is presented afterwards to avoid the overparametrization problem and the explosion of complexity problem existing in the adaptive backstepping method. All the signals of the closed-loop system are rendered globally/semi-globally uniformly ultimately bounded, and the tracking error can be made arbitrarily small by tuning the designed parameters. A simulation example is given to show the validity of the control algorithm.
IJAC CiteScore keeps raising in 2018
IJAC receives a CiteScore as high as 2.34 in 2018 which is 1.37 times higher than that in 2017. Being in the top 15%, it ranks #69 among 460 journals in respective categories.
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During the past two years, IJAC has published a series of high-quality papers by famous scholars around the world, including professor Tomaso Poggio from MIT, professor Brian Anderson from Australian National University, professor Yike Guo from Imperial College London, etc.. All the papers are open access, covering topics of Deep Learning, Artificial Intelligence, Neural Networks, and so on. You’ll never miss it!
2019 International Academic Conference List
International Journal of Automation and Computing (IJAC) maintains this list of conferences at the beginning of each year that are highly relevant to current hot research topics, including artificial intelligence, machine learning, computer vision, pattern recognition, robotics and automatic control.