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The performance of the existing frequency control strategies for MT-HVDC systems relies on the accuracy of the mathematical models. However, it is hard to obtain the exact mathematical models of MT-HVDC systems. To deal with this challenge, this paper presents a distributed cooperative frequency control strategy for MT-HVDC systems by using a reinforcement learning method, i.e., value iteration algorithm. Specifically, the proposed control strategy is data driven and hence model free. Besides, the proposed control strategy is distributed in the sense that each AC area only requires the local and neighboring information, rather than the information of all the AC areas. Moreover, the proposed control strategy makes the connected AC areas compensate load disturbances together by sharing their power reserves via HVDC grids, which greatly decreases the operation costs of MT-HVDC systems. The performance of the proposed control strategy is validated by cases study. [ABSTRACT FROM AUTHOR]

Abstract This paper focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of iterative learning control algorithm for a class of multi-agent systems with distributed parameter models. And the considered distributed parameter models are governed by the parabolic or hyperbolic partial differential equations. Based on the framework of network topologies, a consensus-based iterative learning control protocol is proposed by using the nearest neighbor knowledge. When the iterative learning control law is applied to the systems, the consensus errors between any two agents on L 2 space are bounded, and furthermore, the consensus errors on L 2 space can converge to zero as the iteration index tends to infinity in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: The generalized difference methods (GDM) have been successfully used in numerical simulations of unsaturated soil water flow problems. The object of this paper is to extend applications of the GDM, that is, apply GDM to numerically solve one-dimensional coupled transport models of unsaturated soil water flow and solute. The semi-discrete and fully discrete generalized difference schemes are established, and the detailed algorithm of numerical approximation schemes is provided. Moreover, some numerical examples illustrate that the generalized difference methods are feasible and efficient to find the numerical solutions of one-dimensional coupled transport models in this paper. Finally, satisfactory results and some other significant and valuable conclusions are obtained from studies. [Copyright &y& Elsevier]

In this paper, a fault-tolerant control allocation scheme is proposed for a class of over-actuated discrete-time systems. Firstly, an indirect adaptive controller is designed which consists of a parameter updating law to estimate unknown model parameters and a control law based on the estimated model parameters to achieve a desired tracking performance. Meanwhile, the control law is treated as the input of control allocation module to carry out an effective distribution to the physical actuators. Then, a control allocation algorithm is developed, which includes a fixed allocation law and an adaptive allocation law to compensate for actuator faults, uncertainties and actuator saturations. Finally, a numerical example and an aircraft model are used to illustrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper describes a digital circuit architecture which implements a recently proposed algorithm for the solution of the point location problem in the evaluation of piecewise affine functions. The circuit is suitable for FPGA implementation of explicit Model Predictive Control. The performances of the architecture are tested in a case study through hardware-in-the-loop simulation. Results show that the proposed circuit can be implemented on limited hardware resources also for quite complex, possibly discontinuous control functions, thus representing a good spare solution when other existing circuit architectures (generally faster but more resource-demanding) cannot be deployed. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a constructive method to design a cooperative state and output feedback to steer a group of nonholonomic mobile robots in chained form to form a desired geometric formation shape. The control methodology divides the resulting tracking error dynamics into a cascaded of linear and time-varying subsystems. A basic consensus algorithm is first applied to the linear subsystem which makes the states synchronize exponentially to zero. Once this first linear subsystem has converged, the second cascade can be treated as a linear time-varying subsystem perturbed by a vanishing term from its cascade. A dynamic state and output feedback is constructed to achieve synchronization of the rest of the states. The proof of stability is given using a result from cascade systems. Since time delay appears in many interconnection networks and particularly in cooperative control, its effect on the stability of the closed-loop system is analyzed using Razumikhim theorem. It is shown that the established cooperative controller work well even in the presence of time delay. Numerical simulations are performed on models of car-like mobile robots to show the effectiveness of the proposed cooperative state and output-feedback controllers. [Copyright &y& Elsevier]

Abstract: This paper focuses on how to efficiently find the global Approximate Closed Frequent Itemsets (ACFIs) over streams. To achieve this purpose over a multiple, continuous, rapid and time-varying data stream, a fast, incremental, real-time and little-memory-cost algorithm should be regarded. Based on the max-frequency window model, a Max-Frequency Pattern Tree (MFP-Tree) structure is established to maintain summary information over the global stream. Subsequently, a novel algorithm Generating Global Approximate Closed Frequent Itemsets on Max-Frequency Window model (GGACFI-MFW) is proposed to update the MFP-Tree with high efficiency. The case studies show the efficiency and effectiveness of the proposed approach. [Copyright &y& Elsevier]