LQ suboptimal decentralised controllers with disturbance rejection property for hierarchical systems

This article is concerned with decentralised output regulation of hierarchical systems subject to input and output disturbances. It is assumed that the disturbance can be represented as the output of an autonomous linear time invariant (LTI) system with an unknown initial state. The primary objective is to design a decentralised controller with the property that not only does it reject the degrading effect of the disturbance on the output (to achieve a satisfactory steady-state performance), it also results in a small linear quadratic (LQ) cost function (implying a good transient behaviour). To this end, the underlying problem is treated in two phases. In the first step, a number of modified systems are defined in terms of the original system. The problem of designing an LQ centralised controller which stabilises all the modified systems and rejects the disturbance in the original system is considered, and it is shown that this centralised controller can be found efficiently by solving a linear matrix inequality (LMI) problem. In the second step, a method recently presented in the literature is exploited to decentralise the designed centralised controller. It is shown that the obtained controller satisfies the prescribed design specifications including disturbance rejection. Finally, in a more pragmatic context, the system is assumed to be subject to input delay, and a robustness analysis is carried out accordingly. Simulation results elucidate the efficacy of the proposed control law.     

An intelligent agent-based architecture for strategic information system applications

Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.     

A fast expert system for electrocardiogram arrhythmia detection

Abstract: A fast expert system for electrocardiogram (ECG) arrhythmia detection has been designed in this study. Selecting proper wavelet details, the ECG signals are denoised and beat locations are detected. Beat locations are later used to locate the peaks of the individual waves present in each cardiac cycle. Onsets and offsets of the P and T waves are also detected. These are considered as ECG features which are later used for arrhythmia detection utilizing a novel fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the proposed procedure. We have evaluated the algorithm on the MIT–BIH arrhythmia database. Application of the wavelet filter with the scaling function which closely resembles the shape of the ECG signal has been shown to provide precise results in this study.     

Integrated damping parameter and control design in structural systems for \bf{\mathcal{H}^2} and {\mathcal{H}^{\infty}} specifications

The paper presents a linear matrix inequality (LMI)-based approach for the simultaneous optimal design of output feedback control gains and damping parameters in structural systems with collocated actuators and sensors. The proposed integrated design is based on simplified $\mathcal{H}^2$ and $\mathcal{H}^{\infty}$ norm upper bound calculations for collocated structural systems. Using these upper bound results, the combined design of the damping parameters of the structural system and the output feedback controller to satisfy closed-loop $\mathcal{H}^2$ or $\mathcal{H}^{\infty}$ performance specifications is formulated as an LMI optimization problem with respect to the unknown damping coefficients and feedback gains. Numerical examples motivated from structural and aerospace engineering applications demonstrate the advantages and computational efficiency of the proposed technique for integrated structural and control design. The effectiveness of the proposed integrated design becomes apparent, especially in very large scale structural systems where the use of classical methods for solving Lyapunov and Riccati equations associated with $\mathcal{H}^2$ and $\mathcal{H}^{\infty}$ designs are time-consuming or intractable.     

RAID-RMS: A fault tolerant stripped mirroring RAID architecture for distributed systems

Disk arrays, or RAIDs, have become the solution to increase the capacity, bandwidth and reliability of most storage systems. In spite of its high redundancy level, disk mirroring is a popular RAID paradigm, because replicating data also doubles the bandwidth available for processing read requests, improves the reliability and achieves fault tolerance. In this paper, we present a new RAID architecture called RAID-RMS in which a special hybrid mechanism is used to map the data blocks to the cluster. The main idea behind the proposed algorithm is to combine the data block striping and disk mirroring technique with a data block rotation. The resulting architecture improves the parallelism reliability and efficiency of the RAID array. We show that the proposed architecture is able to serve many more disk requests compared to the other mirroring-based architectures. We also argue that a more balanced disk load is attained by the given architecture, especially when there are some disk failures.     

Robust Minimum Variance Lower Bound Estimation by Uncertainty Modeling Using Interval Type‐2 Fuzzy set

The Minimum Variance Lower Bound (MVLB) represents the best achievable controller capability in a variance sense. Estimation of the MVLB for nonlinear systems confronts some difficulties. If one simply ignores these nonlinearities, there is the danger of over‐estimating the performance of the control loop in rejecting uncertainties. Assuming that almost all models have uncertainties, in this paper, the MVLB has been estimated considering three types of uncertainties: structural, parametric, and algorithmic. To achieve accurate estimation of the MVLB an interval type‐2 fuzzy set has been utilized. This paper utilizes a strategy for modeling of symmetric interval type‐2 fuzzy sets using their uncertainty degrees. Then, based on this uncertainty measure, one method to construct interval type‐2 fuzzy set models using the uncertain interval data is introduced. Finally, simulation studies demonstrate the effectiveness of the proposed control scheme.     

A learning approach to the bandwidth multicolouring problem

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1–?) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.     

A new algorithm for forest height estimation based on the varied extinction random volume over ground (VERVoG) model using PolInSAR data

ABSTRACT

This paper examines a simple geometrical method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The suggested method estimates the forest biophysical parameters based on the varied extinction random volume over ground (VERVoG) model with top layer extinction greater than zero. We approach the problem using a geometrical method without the need for any auxiliary data or prior information. The biophysical parameters, i.e. top layer extinction value, forest height and extinction gradient are estimated in two separate stages. In this framework, the offset value of the extinction is estimated in an independent procedure as a function of a geometrical index based on the signal penetration in the volume layer. As a result, two remaining biophysical parameters can be calculated in a geometrical way based on the observed volume coherence. The proposed algorithm was evaluated using the L-band PolInSAR data of the European Space Agency (ESA) BioSAR 2007 campaign. A pair of experimental SAR (ESAR) images was acquired over the Remningstorp test site in southern Sweden. The selected images were employed for the performance analysis of the proposed approach in the forest height estimation application based on the VERVoG model. The experimental result shows that the proposed inversion method based on the VERVoG model with top layer extinction greater than zero estimates the volume height with an average root mean square error (RMSE) of 2.08 m against light detection and ranging (LiDAR) heights. It presents a significant improvement of forest height accuracy, i.e. 4.1 m compared to the constant extinction RVoG model result, which ignores the forest heterogeneity in the vertical direction.     

Degree constrained minimum spanning tree problem: a learning automata approach

Degree-constrained minimum spanning tree problem is an NP-hard bicriteria combinatorial optimization problem seeking for the minimum weight spanning tree subject to an additional degree constraint on graph vertices. Due to the NP-hardness of the problem, heuristics are more promising approaches to find a near optimal solution in a reasonable time. This paper proposes a decentralized learning automata-based heuristic called LACT for approximating the DCMST problem. LACT is an iterative algorithm, and at each iteration a degree-constrained spanning tree is randomly constructed. Each vertex selects one of its incident edges and rewards it if its weight is not greater than the minimum weight seen so far and penalizes it otherwise. Therefore, the vertices learn how to locally connect them to the degree-constrained spanning tree through the minimum weight edge subject to the degree constraint. Based on the martingale theorem, the convergence of the proposed algorithm to the optimal solution is proved. Several simulation experiments are performed to examine the performance of the proposed algorithm on well-known Euclidean and non-Euclidean hard-to-solve problem instances. The obtained results are compared with those of best-known algorithms in terms of the solution quality and running time. From the results, it is observed that the proposed algorithm significantly outperforms the existing method.