Abstract

Autonomous cars and fixed-wing aerial vehicles have the so-called non-holonomic kinematics which non-linearly maps control input to states. As a result, trajectory optimization with such a motion model becomes highly non-linear and nonconvex. In this paper, we improve the computational tractability of non-holonomic trajectory optimization by reformulating it in terms of a set of bi-convex cost and constraint functions along with a non-linear penalty. The bi-convex part acts as a relaxation for the non-holonomic trajectory optimization while the residual of the penalty dictates how well its output obeys the non-holonomic behavior. We adopt an alternating minimization approach for solving the reformulated problem and show that it naturally leads to the replacement of the challenging nonlinear penalty with a globally valid convex surrogate. Along with the common cost functions modeling goal-reaching, trajectory smoothness, etc., the proposed optimizer can also accommodate a class of non-linear costs for modeling goal-sets, while retaining the bi-convex structure. We benchmark the proposed optimizer against off-the-shelf solvers implementing sequential quadratic programming and interior-point methods and show that it produces solutions with similar or better cost as the former while significantly outperforming the latter. Furthermore, as compared to both off-the-shelf solvers, the proposed optimizer achieves more than 20x reduction in computation time.

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

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

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

Music is appreciated for emotional reasons across cultures, but knowledge on the cross-cultural similarities and differences of music-evoked emotions is still sparse. The current study compared music-evoked emotions in Finland and in India, contextualizing them within the perceived psychological functionality of music in an individualistic versus collectivistic culture. Participants ( N = 230) answered an online survey on music-evoked emotions and related personal meanings. A mixed-method approach using factor analysis and qualitative content analysis was used to identify the concepts for cross-cultural comparison. Results show that both cultures value music for positive emotional experiences, but the prevalence of more detailed emotional nuances and underlying meanings is distinctively different. The highest-scoring emotion factor for Finns was Power-Empowerment while for Indians it was Peaceful-Transcendence. For Finns, the personal relevance of music was distinctively related to self-enhancement, self-reflective insights, and self-expression, while for Indians the relevance was particularly related to using music as a mood management tool for reaching positive, relaxed, and motivated affective states. Both cultures found music important for experiencing social connection. The results partly reflect the individualistic-collectivistic dimensionality of cultures and emphasize the relevance of contextualizing music psychological knowledge of music-evoked emotions in individuals’ culturally bound meaning-making processes.

Abstract

In this paper, we present a multispeaker localization method using the time delay estimates obtained from the spectral features derived from the single frequency filter (SFF) representation. The mixture signals are transformed into SFF domain from which the temporal envelopes are extracted at each frequency. Subsequently, the spectral features such as mean and variance of temporal envelopes across frequencies are correlated for extracting the time delay estimates. Since these features emphasize the high SNR regions of the mixtures, correlation of the corresponding features across the channels leads to robust delay estimates in real acoustic environments. We study the efficacy of the developed approach by comparing its performance with the existing correlation based time delay estimation techniques. Both, a standard data set recorded in real-room acoustic environments and simulated data set are used for …