This paper investigates the problem of passive controller design for a class of nonlinear systems under variable sampling. The Takagi-Sugeno (T-S) fuzzy modeling method is utilized to represent the nonlinear systems. Attention is focused on the design of passive controller for the T-S fuzzy systems via sampled-data control approach. Under the concept of very-strict passivity, a novel time-dependent Lyapunov functional is constructed to develop passive analysis criteria and passive controller synthesis conditions. A new sampled-data controller is designed to guarantee that the resulting closed-loop system is very-strictly passive. These conditions are formulated in the form of linear matrix inequalities (LMIs), which can be solved by convex optimization approach. Finally, an application example is given to demonstrate the feasibility and effectiveness of the proposed results.
This paper is dedicated to studying the stability of semi-Markov switching stochastic delay systems with unstable subsystems, where delay depends on another semi-Markovian process and transition rates are uncertain. Based on the stochastic analysis theory and the mode-dependent multi-Lyapunov function method, relevant sufficient conditions are established to ensure the asymptotic stability in mean square of the system. In addition, contrary to the existing works, the system under consider is more general and our works are not only applicable to the system with partial unstable subsystems, but also applicable to the system with all unstable subsystems. Finally, one numerical example is provided to illustrate the correctness of the method described in the paper.
We address the problem of downlink throughput improvement for IEEE 802.11a/g systems by using a modified access point (AP) equipped with multiple antennas. The main restriction is that the standard terminals should not be modified in any way. An alternating time-offset space division multiple access (SDMA) solution is considered for a conference room scenario. A simulation is based on channel models approved by the IEEE 802.11 Standard Group and takes into account the main features of the IEEE 802.11a/g standard. It is demonstrated that a near doubling of downlink capacity can be achieved in a conference room environment.
A vehicle stability control approach for four-wheel independently actuated (FWIA) electric vehicles is presented. The proposed control method consists of a higher-level controller and a lower-level controller. An adaptive control-based higher-level controller is designed to yield the vehicle virtual control efforts to track the desired vehicle motions due to the possible modeling inaccuracies and parametric uncertainties. The lower-level controller considering tire force saturation is given to allocate the required control efforts to the four in-wheel motors for providing the desired tire forces. An analytic method is given to distribute the high-level control efforts, without using the numerical-optimization-based control allocation algorithms. Simulations based on a high-fidelity, CarSim, and full-vehicle model show the effectiveness of the control approach.
Unsupervised image segmentation is greatly useful in many vision-based applications. In this paper, we aim at the unsupervised low-key image segmentation. In low-key images, dark tone dominates the background, and gray level distribution of the foreground is heterogeneous. They widely exist in the areas of space exploration, machine vision, medical imaging, etc. In our algorithm, a novel active contour model with the probability density function of gamma distribution is proposed. The flexible gamma distribution gives a better description for both of the foreground and background in low-key images. Besides, an unsupervised curve initialization method is designed, which helps to accelerate the convergence speed of curve evolution. The experimental results demonstrate the effectiveness of the proposed algorithm through comparison with the CV model. Also, one real-world application based on our approach is described in this paper.
Summary This paper is concerned with the observer‐based output tracking problem for a class of linear switched stochastic systems with time delay and disturbance by using repetitive control approach. More precisely, a two‐dimensional hybrid model is incorporated to obtain and optimize the repetitive controller. In particular, the repetitive controller is used to improve the tracking performance through its continuous learning actions. In addition, an equivalent‐input‐disturbance estimator is incorporated into the repetitive control design approach to reduce the effect of the external disturbances. The main aim of the control design is to track the periodic reference signal with the measured output of the system under consideration even in the presence of an unknown bounded disturbance. By constructing a suitable Lyapunov‐Krasovskii functional and using average dwell time approach and Jensen inequality, sufficient conditions are obtained in terms of linear matrix inequalities to guarantee the mean‐square exponential stability of the considered system. Eventually, a numerical example is provided to demonstrate the effectiveness of the developed method.
This paper deals with the implementation of Haar wavelet to the optimal control of linear singularly perturbed systems. The approximated composite control and the slow and fast trajectories with respect to a quadratic cost function by solving only the linear algebraic equations are calculated. The results are illustrated with a simple example.
Compared to the single-source domain adaptation fault diagnosis methods, the multi-source domain adaptation methods can not only take advantage of the rich and diverse diagnostic information of multiple source domains but also draw on the feature alignment of single-source setting to reduce the domain discrepancy. However, forcing the alignment of feature distributions is challenging and may lead to negative transfer. Meanwhile, labeled data are often scarce and difficult to collect in actual production, which can be mitigated by multi-source information, but the diagnostic performance of the model is degraded by large domain differences. To tackle the above issues, a domain attribute and feature transfer network is proposed to model multi-source information domains in a unified deep network and achieve cross-domain fault diagnosis. In the transferable attributes learning section, we adopt an attention mechanism and a domain attribute loss function to extract transferable latent attributes from multi-source information. In the transferable features learning section, we apply the local maximum mean discrepancy metric to adjust the category distribution of single-source information and target domains. Then, intra-class compactness learning and pseudo-labeling learning strategies are utilized to further obtain richer feature representations. Finally, we propose the knowledge fusion module to fuse the results of multi-source information classifiers to yield a more reliable diagnosis result. Extensive experiments on three different multi-source information datasets show the superiority of our method compared to the state-of-the-art (SOTA) methods by comparing indicators from various aspects.
The problem of synchronization is investigated for a class of master-slave systems with time-varying delays and Markovian switching parameters. Using an appropriate Lyapunov-Krasovskii functional, a synchronization law which includes the master-slave parameters is established for designing a mode-dependent output feedback control law in terms of LMIs. A numerical example is given to show the effectiveness of the method.
This brief investigates the problem of passivity-based resilient sampled-data control for Markovian jump systems subject to actuator faults via an adaptive fault-tolerant mechanism. By constructing a proper Lyapunov function, a set of sufficient conditions is obtained in terms of linear matrix inequalities (LMIs), which ensures that the closed-loop system is stochastically passive. In order to reflect the imprecision in controller, the additive gain variations is considered. Then, the resilient sampled-data control parameters can be determined by solving the obtained LMIs. Finally, an illustrative example is presented to show the validity and applicability of the proposed design technique.
The prediction of key variables has great significance to monitor the running status of industrial systems. In this paper, a novel data-driven design of variable predictor is proposed. The basic idea is the realization of prediction observer, which is modified from the observer-based fault diagnose method. Different from the standard data-driven approaches, the proposed scheme is adopted for the dynamic systems due to the superior tracking ability of output observer. Additionally, by introducing an extra design freedom and the estimation of measured value, it can also be used for the case that the key variable is not on-line measurable. Finally, the proposed prediction scheme is applied to the Tennessee-Eastman plant to demonstrate the effectiveness.
