No abstract is provided for this article.
We present a new design strategy that makes it possible to synthesize decentralized output-feedback controllers by solving two successive optimization problems with linear matrix inequality (LMI) constraints. In the initial LMI optimization problem, two auxiliary elements are computed: a standard state-feedback controller, which can be taken as a reference in the performance assessment, and a matrix that facilitates a proper definition of the main LMI optimization problem. Next, by solving the second optimization problem, the output-feedback controller is obtained. The proposed strategy extends recent results in static output-feedback control and can be applied to design complex passive-damping systems for vibrational control of large structures. More precisely, by taking advantages of the existing link between fully decentralized velocity-feedback controllers and passive linear dampers, advanced active feedback control strategies can be used to design complex passive-damping systems, which combine the simplicity and robustness of passive control systems with the efficiency of active feedback control. To demonstrate the effectiveness of the proposed approach, a passive-damping system for the seismic protection of a five-story building is designed with excellent results.
No abstract is provided for this article.
No abstract is provided for this article.
This paper addresses the problem of fault detection for switched systems with time-varying delay using delta operator approach. By means of the delta operator systems, a fault detection observer is used as the residual generator. Based on the switched Lyapunov function approach, sufficient conditions for the existence of the fault detection observer are given in terms of linear matrix inequalities (LMIs). An example is provided to show the effectiveness and applicability of the proposed method.
In this study, a cooperative game model is presented to schedule the day-ahead operation of multi-microgrid (MMG) systems. In the proposed model, microgrids are scheduled to achieve a global optimum for the cost of the multi-microgrid system. The minimum cost is achieved by transactions of microgrids with each other. Also, price-based demand response is implemented in the model to build a cost-reducing opportunity for consumers. Applying Shapley value, the optimum cost of the MMG system is fairly allocated between microgrids. To enhance the confidence level of results, data uncertainties are incorporated into the model. The uncertainties of renewable outputs, demand, and prices of trading with the main grid are applied in the model. The presented model is developed as a mixed-integer linear programming problem, and its efficiency is evaluated on a standard test system containing three microgrids. The cost of the MMG system when microgrids form a cooperative game is compared with the isolated status that microgrids do not transact energy with each other. The results indicate that the cost of the MMG system is declined using the proposed cooperative model in comparison with the isolated mode. Also, the cost of microgrid 1, microgrid 2, and microgrid 3 are improved by 2.4, 2.7, and 11.8%, respectively. Therefore, all the microgrids have an incentive to participate in the cooperative game, and both the total cost and each microgrid cost are improved in the cooperative game.
Using Haar wavelets, a computational method is presented to determine the piecewise constant feedback controls for a finite-time linear optimal control problem of a time-varying state-delayed system. The method is simple and computationally advantageous. The approximated optimal trajectory and optimal control are calculated using Haar wavelet integral operational matrix, Haar wavelet product operational matrix and Haar wavelet delay operational matrix. An illustrative example is included to demonstrate the validity and applicability of the technique
Playing games between humans and robots have become a widespread human-robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence degree of the robot and the anti-interference ability of the motion capture system still need to be solved. In this paper, we present an adaptive reinforcement learning (RL) based multimodal data fusion (AdaRL-MDF) framework teaching the robot hand to play Rock-Paper-Scissors (RPS) game with humans. It includes an adaptive learning mechanism to update the ensemble classifier, an RL model providing intellectual wisdom to the robot, and a multimodal data fusion structure offering resistance to interference. The corresponding experiments prove the mentioned functions of the AdaRL-MDF model. The comparison accuracy and computational time show the high performance of the ensemble model by combining k-nearest neighbor (k-NN) and deep convolutional neural network (DCNN). In addition, the depth vision-based k-NN classifier obtains a 100% identification accuracy so that the predicted gestures can be regarded as the real value. The demonstration illustrates the real possibility of HRC application. The theory involved in this model provides the possibility of developing HRC intelligence.
The manifold of a planar array in a direction finding system may be considered as two families of azimuth θ and elevation ø curves, where the ø-parameter curves are hyperhelical as well as geodesic while the θ-parameter curves are neither. Since the θ-curves are not hyperhelical, their curvatures depend on θ and so analytical evaluation of curvatures of order greater than two can become exceedingly laborious and impractical. The advantages of having hyperhelical parameter curves are numerous. For one thing, all the curvatures of a hyperhelix may be evaluated recursively (since they do not vary from point to point) as a function of lower-order curvatures. This has been demonstrated in (1) for the case of the single-parameter manifold of a linear array. Furthermore the convenient nature of a hyperhelix's geometry has proven invaluable in array design (2), in investigating the detection and resolution thresholds (3) and in identifying ambiguities inherent in array configurations (4). In view of the above facts, it seems logical that an alternative parametrization of the manifold surface, which results in two sets of hyperhelical parameter curves, can provide a great deal of additional insight into the nature of planar array behaviour and design. In this investigation, such a parametrization is identified and its significance is demonstrated by a number of examples/applications. Furthermore properties, such as Gaussian and geodesic curvatures, are defined and their implications with regards to isometric mappings are discussed.
The guaranteed cost control problem is investigated for a class of nonlinear discrete-time systems with Markovian jumping parameters and mixed time delays. The mixed time delays involved consist of both the mode-dependent discrete delay and the distributed delay with mode-dependent lower bound. The associated cost function is of a quadratic summation form over the infinite horizon. The nonlinear functions are assumed to satisfy sector-bounded conditions. By introducing new Lyapunov-Krasovskii functionals and developing some new analysis techniques, sufficient conditions for the existence of guaranteed cost controllers are derived with respect to the given cost function. Moreover, a convex optimization approach is applied to search for the optimal guaranteed cost controller by minimizing the guaranteed cost of the closed-loop system. Numerical simulation is further carried out to demonstrate the effectiveness of the proposed methods.
The last few decades have witnessed the rapid growth of research & development on large-scale systems (LSSs) due to the increasing complexity and the growing demand of modern engineering systems. The LSSs can normally be viewed as interconnections of multiple subsystems. Many practical systems can be described by the LSSs such as power systems, multi-robot systems, communication networks, transportation networks, and supply chains. In such large-scale systems, the centralized control framework may become impossible for its implementation. The decentralized and distributed controls have emerged as the attractive control methodologies to handle the scale and interactions of large-scale complex systems. However, the interactions among different subsystems introduce many challenges in the analysis and synthesis of such systems. Therefore, it is of significance to address several fundamental problems regarding real-time analysis, estimation and control of these systems.
No abstract is provided for this article.
The mechatronics systems are widely used in modern society. This paper presents a novel data-driven scheme which can be used for fault diagnose of mechatronics systems. The proposed method is based on the subspace identification of parity vector. By constructing the output observer, critical variables can be acquired by soft sensors. This makes the fault diagnoses free from the limitation of online measurement. A diagnose observer is designed directly from the parity vector. Finally, the proposed scheme is tested by the Simulink benchmark of vehicle suspension and shows its good performance.
