Abstract

Adaptive Sliding Mode Control (ASMC) aims to adapt the switching gain in such a way to cope with possibly unknown uncertainty. In state-of-the-art ASMC methods, a priori boundedness of the uncertainty is crucial to ensure boundedness for the switching gain and uniformly ultimately boundedness. A priori bounded uncertainty might impose a priori bounds on the system state before obtaining closed-loop stability. A design removing this assumption is still missing in literature. A positive answer to this quest is given by this note where a novel ASMC methodology is proposed which does not require a priori bounded uncertainty. An illustrative example is presented to highlight the main features of the approach, after which a general class of Euler–Lagrange systems is taken as a case study to show the applicability of the proposed design.

Abstract

This brief proposes a new adaptive-robust formulation for time-delay control (TDC) under a less-restrictive stability condition. TDC relies on estimating the unknown system dynamics via the artificial introduction of a time delay, often referred to as time-delay estimation (TDE). In conventional TDC, the estimation error, called TDE error, is taken to be upper bounded by a constant under the assumption of small time delay and, most importantly, of a priori bounded states. We highlight the issues of such a conventional methodology via an unstable the upper bound of the TDE error is formulated, which has an explicit dependence on system states and is valid for any chosen time delay. This insight leads to a new TDC design, namely, time-delayed adaptive-robust control (TDARC). The effectiveness of TDARC is substantiated via a multiple-degrees-of-freedom robot.

Abstract

Available control methods for underactuated Euler–Lagrange (EL) systems rely on structure-specific constraints that may be appropriate for some systems, but restrictive for others. A generalized (structure-independent) control framework is to a large extent missing, especially in the presence of uncertainty. This paper introduces an adaptive-robust control framework for a quite general class of uncertain underactuated EL systems. Compared to existing literature, the important attributes of the proposed solution are: (i) avoiding structure-specific restrictions, namely, symmetry condition property of the mass matrix, and a priori bounds on non-actuated states or state derivatives; (ii) considering Coriolis, centripetal, friction and gravity terms to be unknown, while only requiring the knowledge of maximum perturbation around a nominal value of the mass matrix; (iii) handling state-dependent uncertainties irrespective of their linear or nonlinear in parameters structure. These features significantly widen the range of underactuated EL systems the proposed solution can handle in comparison to the available methods. Stability is studied analytically and the performance is verified in simulation using offshore boom crane dynamics.

Abstract

The literature has proven that attaining good transient behavior in leakage-based robust adaptive control of uncertain switched systems is intrinsically challenging. In fact, because the gains of the inactive subsystems must exponentially vanish during inactive times as an effect of leakage action, new learning transients will repeatedly arise at each switching instant. In this paper, a new leakage-based mechanism is designed for robust adaptive control of uncertain switched systems: in contrast to the available designs, the key innovation of the proposed one is that the adaptive gains of the inactive subsystems can be kept constant to their switched-off values, thus preventing vanishing gains. Bounded stability of the closed-loop switched system is guaranteed thanks to the introduction of an auxiliary gain playing the role of leakage. A benchmark example commonly adopted in adaptive switched literature shows that the proposed strategy can consistently improve the transient behavior under various families of switching signals.

Abstract

Frequent pattern mining is one among the popular data mining techniques. Frequent pattern mining approaches extract interesting associations among the items in a given transactional database. The items of the transactional database can be organized as a concept hierarchy. Notably, frequent pattern mining does not distinguish the patterns by analyzing the categories of the items in a given concept hierarchy. In several applications, it is often useful to distinguish among the frequent patterns by analyzing how the items of the pattern are mapped to different categories of the concept hierarchy. In this paper, we propose a new interestingness measure, designated as diversity rank (drank), for capturing the diversity of a given pattern by analyzing the extent to which the items of the pattern are associated with the categories of the corresponding concept hierarchy. Given a transactional database over a set I of items and the corresponding concept hierarchy on I, we propose a methodology to compute the drank of the given pattern. Furthermore, by extending the notion of drank, we propose an approach to improve the diversity and accuracy of association rule-based recommender system. The results of our performance evaluation on the real-world MovieLens dataset demonstrate that the proposed diversity model extracts different kinds of patterns as compared to frequent patterns. Furthermore, our proposed recommender system approach improves the diver sit y performance w.r.t. the existing association rule-based recommender system without significantly compromising the accuracy. Overall, the proposed concept hierarchy-based diverse pattern model provides a scope to develop new approaches for improving the performance of frequent pattern mining-based applications.

Abstract

Due to advances in technology for better output/production, there has been an increased demand for technology with optimized resource utilization in order to conserve resources. Advancements in sensors have improved resource optimization in many fields such as electric power consumption, water management and quality management. In this regard, air conditioner (AC) management is an important aspect of electric power consumption. In this paper, we address the issue of reducing power consumption for air conditioning of a given room. In previous works, the whole room is maintained at a fixed required temperature by inherently assuming that all of the users prefer the same temperature; this is generally not true in practice. In this paper, we propose a sensor-based framework to place and manage ACs for maintaining diverse temperature zones in a given layout to reduce power consumption. Through a simulation study, we demonstrate that the proposed framework indeed has the potential to reduce power consumption significantly as compared to the naive approach by achieving user satisfaction.

Abstract

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large databases. However, developing fast and efficient parallel algorithms for handling large volumes of data is a challenging task. The MapReduce framework enables the distributed processing of huge amounts of data in large-scale distributed environment with robust fault-tolerance. In this paper, we propose a parallel algorithm for extracting coverage patterns. The results of our performance evaluation with real-world and synthetic datasets demonstrate that it is indeed feasible to extract coverage patterns effectively under the MapReduce framework.

Abstract

Fine-grained object recognition refers to a subordinate level of object recognition such as recognition of bird species and car models. It has become crucial for recognition of previously unknown classes. While fine-grained object recognition has seen unprecedented progress with the advent of neural networks, many of the existing works are cost-sensitive as they are acutely picture-dependent and fail without the adequate number of quality pictures. Efforts have been made in the literature for a picture-independent recognition with hybrid human-computer recognition methods via single question answering with a human-in-the-loop. To this end, we propose an improved batch-based local question answering method for making the recognition efficient and picture-independent. When pictures are unavailable, at each time-step, the proposed method mines a batch of binary cluster-centric local questions to pose to a human-in-the-loop and incorporates the responses received to the questions into the model. After a preset number of time-steps, the most probable class of the target object is returned as the final prediction. When pictures are available, our model facilitates the plug-in of computer vision algorithms into the framework for better performance. Experiments on three challenging datasets show significant performance improvement with respect to accuracy and computation time as compared to the existing schemes.

Abstract

Weighted Frequent Itemset (WFI) mining is an important model in data mining. It aims to discover all itemsets whose weighted sum in a transactional database is no less than the user-specified threshold value. Most previous works focused on finding WFIs in a transactional database and did not recognize the spatiotemporal characteristics of an item within the data. This paper proposes a more flexible model of Weighted Frequent Neighborhood Itemsets (WFNI) that may exist in a spatiotemporal database. The recommended patterns may be found very useful in many real-world applications. For instance, an WFNI generated from an air pollution database indicates a geographical region where people have been exposed to high levels of an air pollutant, say PM2.5. The generated WFNIs do not satisfy the anti-monotonic property. Two new measures have been presented to effectively reduce the search space and the computational cost of finding the desired patterns. A pattern-growth algorithm, called Spatial Weighted Frequent Pattern-growth, has also been presented to find all WFNIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also describe a case study in which our model has been used to find useful information in air pollution database.

Abstract

In this paper, we present a machine learning approach to move a group of robots in a formation. We model the problem as a multi-agent reinforcement learning problem. Our aim is to design a control policy for maintaining a desired formation among a number of agents (robots) while moving towards a desired goal. This is achieved by training our agents to track two agents of the group and maintain the formation with respect to those agents. We consider all agents to be homogeneous and model them as unicycle [1]. In contrast to the leader-follower approach, where each agent has an independent goal, our approach aims to train the agents to be cooperative and work towards the common goal. Our motivation to use this method is to make a fully decentralized multi-agent formation system and scalable for a number of agents.