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Abstract Aiming at meetiing the need to filtering flight trajectory data for aircraft testing, a novel adaptive cubature Kalman filter (CKF) is proposed based on the maximum correntropy and Gaussian‐sum in this paper. Firstly, based on the traditional CKF algorithm, we introduced a Gaussian‐sum method to approximate non‐Gaussian noise to get more accurate filtering results in view of the problem of reduced filtering accuracy caused by the inherent non‐Gaussian nature of the noise and the system non‐linearity. Secondly, the maximum correntropy criterion is introduced to solve further the problem of improving the filtering accuracy of the system in the case of non‐linearity. Simulation results and actual data verification showed that the Square‐root cubature Kalman filter algorithm based on the maximum correntropy and Gaussian‐sum has higher accuracy than traditional filtering algorithms, which verified the algorithm's effectiveness in the application.

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted. [ABSTRACT FROM AUTHOR]

The advent of the Internet of Things (IoT) has triggered an increased demand for sensing devices with multiple integrated wireless transceivers. These platforms often support the advantageous use of multiple radio technologies to exploit their differing characteristics. Intelligent radio selection techniques allow these systems to become highly adaptive, ensuring more robust and reliable communications under dynamic channel conditions. In this paper, we focus on the wireless links between devices equipped by deployed operating personnel and intermediary access-point infrastructure. We use multi-radio platforms and wireless devices with multiple and diverse transceiver technologies to produce robust and reliable links through the adaptive control of available transceivers. In this work, the term 'robust' refers to communications that can be maintained despite changes in the environmental and radio conditions, i.e., during periods of interference caused by non-cooperative actors or multi-path or fading conditions in the physical environment. In this paper, a multi-objective reinforcement learning (MORL) framework is applied to address a multi-radio selection and power control problem. We propose independent reward functions to manage the trade-off between the conflicting objectives of minimised power consumption and maximised bit rate. We also adopt an adaptive exploration strategy for learning a robust behaviour policy and compare its online performance to conventional methods. An extension to the multi-objective state–action–reward–state–action (SARSA) algorithm is proposed to implement this adaptive exploration strategy. When applying adaptive exploration to the extended multi-objective SARSA algorithm, we achieve a 20% increase in the F1 score in comparison to one with decayed exploration policies. [ABSTRACT FROM AUTHOR]

The upcoming generation of power systems would be outlined as a system of systems in which many AC and DC microgrids are interconnected to exchange power in a controlled manner. In this work, a new platform for the interconnection of multiple microgrids using a unified interphase power controller (UIPC) is presented. The networked microgrids operate in an islanded mode where voltage control in DC microgrids, and voltage and frequency control in AC microgrids are demanding. Therefore, we develop a new structure for the UIPC according to a current‐controlled four‐leg power converter configuration which can satisfy voltage and frequency support at the AC side. Due to many power and voltage oscillations, UIPC control is challenging in this heterogeneous environment. Therefore, the UIPC is equipped with a new affine projection‐like optimal control (APLOC) strategy which enables the microgrids to safely exchange power when necessary. Further, each AC or DC microgrid contains a new controllable load model which is commanded by the UIPC according to the power management paradigm. Simulation results proved the sufficiency of the proposed networked microgrids model in both controlling the exchanged power among microgrids and improving power quality problems. [ABSTRACT FROM AUTHOR]

This paper presents reduced‐order methods to study the stability of initialized or Grünwald‐Letnikov fractional nonlinear systems. It is shown that the initialization procedure must be formalized by introducing a class of systems, and the corresponding stability analysis must be established for each element of that class. The main features obtained using this novel approach are (a) the requirements for stability are imposed directly on the equations of the system and involve only finite‐dimensional variables; (b) the conclusions are asserted on the variables of interest; (c) the method can be extended in several ways, including multi‐order systems. Illustrative examples, including an application in adaptive control, are finally presented to convey the usefulness of our approach. [ABSTRACT FROM AUTHOR]

The article discusses the possibility of applying the results of regression analysis [1], fuzzy logic modelling and adaptive systems as such for use in heating control circuits of railway turnouts in order to reduce energy consumption, reduce current surges during on/off switching, and extend the life of heating elements. Based on various researches [2-4] some new solutions are proposed to fulfil shortages of current solutions. As a proposed solution to the described problem, an experimental point electric heating adaptive control system is proposed and described. Experiment of controlling point electric heating system using pulse-width modulation driven by values of regression analysis and fuzzy logic elements. Adaptive methods allowed to dismiss contact temperature sensors, which is associated with a decrease in the reliability of the system, but this fact is prevented by the use of such control methods that can indirectly determine the required rail heating temperature. Conclusions show that the approach of introducing adaptive methods like pulse-width modulation allows to control point electric heating in more efficient way -- using program code, control system will adjust heating by adjusting time intervals when the heating is turned on and off. [ABSTRACT FROM AUTHOR]

Anxiety disorders comprise different clinical conditions that affect individuals in their personal, professional and social domains. The development of new intervention approaches for the treatment of anxiety disorders is crucial. As a step forward into promoting the well-being through adaptive physiological responses, we developed an augmented reality (AR)-based system using bio-cybernetic loops to create an adaptive system for exposure therapy in anxiety disorders. The system was built using open-source software (e.g., NyARToolkit and Unity 3D). AR technology uses computer-generated information to enrich the real world. It can be used with less intrusive devices to collect physiological data (e.g., Bitalino) describing human behavior in a cycle. In this context, our research project aims to study behavior during exposure to biologically relevant stimuli such as snakes. Phobia is described as an irrational fear to an object/stimulus. This fear triggers several physiological responses from sensors as increased heart rate (ECG) and skin conductance (EDA), which are responses from the autonomous nervous system. This approach can be used in several sessions, where the system through machine learning algorithms adapts the thresholds to the individual profile of each participant from historical data. Our study has been carried out in two stages: (1) The participants in a total of 35 students (30 males and 5 females with ages ranging from 19 to 29 years) were invited to fill a snake questionnaire (SNAQ). (2) A subsample was enrolled in an exposure session in AR using a virtual snake while collecting psychophysiological responses from sensors data. The results have shown increased physiological responses in two AR exposure sessions using snakes as stimuli. Therefore we conclude that the system was efficient to detect changes in physiological responses during the exposure sessions. [ABSTRACT FROM AUTHOR]

In Human-Machine Interaction for Artificial Social Companions, we must incorporate features that allow an agent to be capable of delivering a sociable experience to the user. The associated technological challenges include active perception features, mobility in unstructured environments, understanding human actions, detect human behaviours and predict human intentions, access to large repositories of personal and social related data, adapt to changing context. These features are paramount for applications in the field of Active and Assisted Living (AAL), where the primary goal is to provide solutions that help people through ageing, by promoting active and healthy living. The research questions being addressed can be stated as: What strategy could be developed to mitigate low specificity? How can we adopt standards in ASCs - Social Robots implementation? We believe that part of the answer to these questions is to improve the way user's needs and expectations are described and represented in knowledge models used in ASC, and these knowledge models should adhere to flexible, extensible and standardized knowledge representations. In these knowledge models we shall incorporate the representation for decision processes to cope with redundancy and fall-back mechanisms in terms of interaction functionalities that result in the agent's self-adaptation to its context (e.g. user model and environment conditions). To test our hypothesis, we formulated our concept for designing a framework that captures the expected behaviour of the agent into descriptive scenarios, then translates these into the agent's information model and use the resulting representation in probabilistic planning and decision-making to control interaction. Our expectation was that adopting this framework could reduce errors and faults on agent's operation, resulting in an improved performance while interacting with the user. The results, from our experiment, confirmed that our framework is effective to a certain level and can improve agent's performance by improving specificity. Although, we consider that designing and implementing interaction workflows in artificial social companions are still challenging. Taking into consideration the landscape of Artificial Social Companions (i.e. Social Robots) for Active and Assisted Living, and associated barriers for the adoption of such solutions. We believe this study will contribute to this field of application, in particular, contributing to the demonstration of concrete experiments adhering to active standards. [ABSTRACT FROM AUTHOR]

Electromyography-based wearable biosensors are used for prosthetic control. Machine learning prosthetic controllers are based on classification and regression models. The advantage of the regression approach is that it permits us to obtain a smoother and more natural controller. However, the existing training methods for regression-based solutions is the same as the training protocol used in the classification approach, where only a finite set of movements are trained. In this paper, we present a novel training protocol for myoelectric regression-based solutions that include a feedback term that allows us to explore more than a finite set of movements and is automatically adjusted according to real-time performance of the subject during the training session. Consequently, the algorithm distributes the training time efficiently, focusing on the movements where the performance is worse and optimizing the training for each user. We tested and compared the existing and new training strategies in 20 able-bodied participants and 4 amputees. The results show that the novel training procedure autonomously produces a better training session. As a result, the new controller outperforms the one trained with the existing method: for the able-bodied participants, the average number of targets hit is increased from 86% to 95% and the path efficiency from 40% to 84%, while for the subjects with limb deficiencies, the completion rate is increased from 58% to 69% and the path efficiency from 24% to 56%.

Augmentative and alternative communication techniques (AAC) are essential to assist individuals facing communication difficulties. (1) Background: It is acknowledged that dynamic solutions that adjust to the changing needs of patients are necessary in the context of neuromuscular diseases. (2) Methods: In order address this concern, a differential approach was suggested that entailed the prior identification of the disease state. This approach employs fuzzy logic to ascertain the disease stage by analyzing intuitive patterns; it is contrasted with two intelligent systems. (3) Results: The results indicate that the AAC system’s adaptability enhances with the progression of the disease’s phases, thereby ensuring its utility throughout the lifespan of the individual. Although the adaptive AAC system exhibits signs of improvement, an expanded assessment involving a greater number of patients is required. (4) Conclusions: Qualitative assessments of comparative studies shed light on the difficulties associated with enhancing accuracy and adaptability. This research highlights the significance of investigating the use of fuzzy logic or artificial intelligence methods in order to solve the issue of symptom variability in disease staging.

Abstract Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non‐deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting‐based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non‐robust system's performance by up to 13.8 times in a real‐world setting.

Personality is considered as the internal factor that defines a person’s behavior. Therefore, providing adaptive features and personalized support in online learning by considering learners’ personalities can improve their learning experiences and outcomes. In this context, several research studies have investigated the impact of personality differences in online learning. However, little is known about how personality differences affect learners’ behavior while learning. To fill this gap, this study applies a lag sequential analysis (LSA) approach to understand learners’ navigational behavior patterns in an online three-months course of 65 learners based on their personalities. In this context, the five factor model (FFM) model was used to identify learners’ personalities. The findings revealed that learners with different personalities use different strategies to learn and navigate within the course. For instance, learners high in extraversion tend to be extrinsically motivated. They therefore significantly navigated between viewing the course module and their personal achievements. The findings of this study can contribute to the adaptive learning field by providing insights about which personalization features can help learners with different personalities. The findings can also contribute to the field of automatic modeling of personality by providing information about differences in navigational behavior based on learners’ personalities.

The Adaptive and Intelligent Educational Systems area is constantly evolving and aims to create personalized learning environments through the application of recent technologies, including Artificial Intelligence techniques, combined with pedagogical theories. This work aims to contribute to the area of AI in education, using an approach that combines Semantic Web technologies and a bio-inspired algorithm to perform personalized recommendation of learning objects through content-based filtering.In contrast to other approaches, this study combines repositories of Virtual Learning Environments (VLE) with materials available on the Web (YouTube and Wikipedia) to provide educational resources in diverse formats on a specific topic. Web materials are retrieved and structured as learning objects. The approach was tested in the Classroom eXperience (CX) VLE, and an extension resource was also created for Moodle. Experiments were carried out to test the approach. One of the experiments aimed to analyze students' opinions regarding personalized recommendation. Students positively evaluated recommendations that considered their knowledge level and offered additional materials on the topic. Another experiment considered three different recommendation processes to observe students' preferences. Recommendations considered the use and non-use of learning styles in the process. The overall average rating was relatively better when ignoring the use of learning styles, but there was no statistical significance. [ABSTRACT FROM AUTHOR]

Reinforcement learning technique was developed recently as an interesting topic in designing adaptive optimal controllers. This technique explicitly provided a feasible solution to circumvent the “curse of dimensionality” and requiring a system model inherent in the classical dynamic programming algorithm. By virtue of this property, in our work, by introducing this technique into a predictor-based online adaptive neural dynamic surface predictive control architecture, we concentrate on a novel robust predictive control framework subject to system uncertainties. To be specific, in this presented framework, an adaptive dynamic programming control strategy utilizing a critic neural network point of view is developed to learn the optimal control policy. Our modification is able to facilitate the alleviation of performance deterioration caused by system uncertainties and enable the smooth and fast learning, while keeping the merits of the finite control-set model predictive control. Finally, the interest and applicability of the proposed control methodology are verified by performance evaluation. [ABSTRACT FROM AUTHOR]

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem’s Pareto-optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights that reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice, since it simplifies the search via converting the original multi-objective problem into a single-objective one and enables the search to focus on what only the stakeholders are interested in. This article questions such a “weighted search first” belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large-scale empirical study that consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is significantly better than its weighted counterpart the majority of the time (up to 77% of the cases), as long as we allow a sufficient, but not unrealistic search budget. This is a beneficial result, as it discovers a potentially new “rule-of-thumb” for the SBSE community: Even when clear preferences are available, it is recommended to always consider Pareto search by default for multi-objective SBSE problems, provided that solution quality is more important. Weighted search, in contrast, should only be preferred when the resource/search budget is limited, especially for expensive SBSE problems. This, together with other findings and actionable suggestions in the article, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) aims to train object detectors by using only image-level annotations. Many recent works on WSOD adopt multiple instance detection networks (MIDN), which usually generate a certain number of proposals and regard proposal classification as a latent model learning within image classification. However, these methods tend to detect salient object, salient object parts and clustered objects due to lack of instance-level annotations during training. Thus a core issue is how to guarantee that the network learn as many objects with precise bounding boxes as possible. In this paper, we address this issue by exploiting the potential of proposal scores during training. We propose an adaptive instance refinement (AIR) framework with three novel designs, which can be integrated with MIDN into a single network. Specifically, adaptive instance mining attempts to discover all positive instances according to the score distribution of proposals and their spatial similarity. Adaptive score modulation dynamically adjusts proposal scores to make the network focus more on instances with different difficulties in different training iterations. Adaptive knowledge refinement distills important information from all previous stages by the weighted average of proposal scores. The experimental results on the PASCAL VOC 2007 and 2012 benchmarks and the MS COCO benchmark demonstrate that AIR significantly improves the performance of the original MIDN and achieves the state-of-the-art results. [ABSTRACT FROM AUTHOR]

This article is concerned with the consensus formation control problem of a class of networked quadrotor aircraft partially bounded and state-dependent perturbations. A general distributed consensus error model is first developed to formulate the formation behavior of the networked quadrotor aircraft. Then, by using adaptive techniques, virtual position control strategies are proposed to eliminate the impacts of perturbations, so that the following quadrotor aircraft can boundedly track the desired position trajectory with a satisfying pattern. Furthermore, based on the designed virtual position control strategies, the attitude reference angles are constructed and adaptive attitude control strategies are further designed to guarantee that the attitude angles track the reference angles asymptotically. In terms of the asymptotic tracking results of attitude control systems, the bounded consensus formation results are obtained based on the Lyapunov stability theorem. Numerical simulations are carried out to verify the efficiency of the designed position formation as well as attitude tracking control strategies of the networked quadrotor aircraft. [ABSTRACT FROM AUTHOR]

This article constructs two adaptive control laws to achieve deformation reduction and attitude tracking for a rotary variable-length crane arm with system parameter uncertainties and asymmetric input–output constraints. Two auxiliary systems are given to deal with the input constraints, an asymmetric-logarithm-barrier Lyapunov function is established for achieving the asymmetric output constrains, and five adaptive laws are constructed to handle system parameter uncertainties. Besides, the control design is based on a partial differential equation model, and the S-curve acceleration and deceleration method is used for regulating the arm extension speed. Both the system stability and uniform ultimate boundedness of the controlled crane arm are analyzed. Simulation results validate the effectiveness of our established control laws. [ABSTRACT FROM AUTHOR]

Military technologies are typically the product of long-term development efforts. These technologies are often adapted late in the development process for use by other users, including other members of the defense enterprise, foreign militaries, or the commercial sector. The need to adapt a technology can arise from cost over-runs or changing operational requirements. This article describes a value-based methodology for developing a transition plan early in the development process, when the system is still in the conceptual phase. Performing this analysis early in the design phase allows for design choices that will support adapting the system for other users. Moreover, it provides a risk mitigation strategy against cost over-run and changes in operational requirements. The process begins by capturing the functional architecture of the technology and analyzing them against a set of tasks associated with different military positions. From there, the adapted technology is evaluated for its projected adequacy based on the value added and the effort required for adaptation. The end-state of this analysis is a value-based model that identifies a portfolio of possible alternative markets and/or uses, which can be comparatively analyzed. A case study is presented for adapting a military augmented reality system for a different market. [ABSTRACT FROM AUTHOR]

This article presents a saturated adaptive-law-based backstepping control method for a class of uncertain nonlinear systems with external dynamic disturbances. In this approach, a novel adaptive backstepping control method with a new command filter is primarily proposed to design the feedback controllers. In order to address the system unknown nonlinearities caused by external dynamic disturbances, a method named “saturated adaptive law approach” is used to estimate the system unknown nonlinearities online. Meanwhile, a filter called “dual command filter” is applied to estimate the derivative of certain signal such that the saturated adaptive law approach can be well implemented. With the help of Lyapunov stability criteria, it is proved that the target signal can be tracked by the output of the system with small error. Finally, our control method is applied to a quadrotor hover system, and two sets of comparison experiments are given to show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Broad learning system (BLS) is a novel and efficient model, which facilitates representation learning and classification by concatenating feature nodes and enhancement nodes. In spite of the efficient properties, BLS is still suboptimal when facing with imbalance problem. Besides, outliers and noises in imbalanced data remain a challenge for BLS. To address the above issues, in this paper we first propose a weighted BLS, which assigns a weight to each training sample, and adopt a general weighting scheme, which augments the weight of samples from the minority class. To further explore the prior distribution of original data, we design a density based weight generation mechanism to guide the specific weight matrix generation and propose the adaptive weighted broad learning system (AWBLS). This mechanism considers the inter-class and intra-class distance simultaneously in the density calculation. Finally, we propose the incremental weighted ensemble broad learning system (IWEB) by utilizing a progressive mechanism to further improve the stability and robustness of AWBLS. Extensive comparative experiments on 38 real-world data sets verfy that IWEB outperforms most of the imbalance ensemble classification methods. [ABSTRACT FROM AUTHOR]

In this article, vibration abatement problems of a riser system with system uncertainty, input deadzone, and output constraint are considered. For obtaining better control precision, a boundary control law is constructed by employing the backstepping method and Lyapunov’s theory. The output constraint is guaranteed by utilizing a barrier Lyapunov function. Adaptive neural networks are designed to cope with the uncertainty of the riser and compensate for the effect caused by the asymmetric deadzone nonlinearity. With the designed controller, the output constraint is satisfied, and the system stability is guaranteed through Lyapunov synthesis. In the end, numerical simulation results are provided to display the performance of the developed adaptive neural network boundary control law. [ABSTRACT FROM AUTHOR]

An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator (SFRM) with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system. By designing a performance function, the transient performance of the control system is guaranteed. To stabilize the SFRM, a dynamic signal is applied to handle the unmodeled dynamics. To cut down the communication load of the channel, the event-triggered control law is developed based on the switching threshold strategy. The Lyapunov stability theory and backstepping technique are applied coordinately to design the control strategy. The semiglobally ultimately uniformly boundedness can be ensured for all signals in the closed-loop system. The designed control method can also guarantee that the tracking error can converge to a small neighborhood of zero within the prescribed performance boundaries. At the end of the article, two illustrative examples are shown to validate the designed event-triggered controller. [ABSTRACT FROM AUTHOR]

This article studies the adaptive fuzzy output-feedback decentralized control problem for the fractional-order nonlinear large-scale systems. Since the considered strict-feedback systems contain unknown nonlinear functions and unmeasurable states, the fuzzy-logic systems (FLSs) are used to model unknown fractional-order subsystems, and a fuzzy decentralized state observer is established to obtain the unavailable states. By introducing the dynamic surface control (DSC) design technique into the adaptive backstepping control algorithm and constructing the fractional-order Lyapunov functions, an adaptive fuzzy output-feedback decentralized control scheme is developed. It is proved that the decentralized controlled system is stable and that the tracking and observer errors are able to converge to a neighborhood of zero. A simulation example is given to confirm the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The minimum error entropy (MEE) criterion is widely used in distributed estimation, since it is insensitive to many types of non-Gaussian noises. However, the default Gaussian kernel function may not always be a proper kernel function. To solve this problem and further improve the performance of the diffusion recursive MEE (DRMEE) algorithm, a diffusion recursive mixture MEE (DRMMEE) algorithm is proposed by combining the mixture MEE criterion and the diffusion strategy. In addition, a quantized version of DRMMEE called the diffusion quantized recursive mixture MEE (DQRMMEE) algorithm, is proposed to reduce the computational burden of DRMMEE. Simulation results show that DRMMEE has higher filtering accuracy than other recursive least-squares-based algorithms, and DQRMMEE even has similar filtering accuracy to DRMMEE in different non-Gaussian noise environments. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive fuzzy output-feedback predefined-time control of nonlinear switched systems with an admissible edge-dependent average dwell time. Different from most of the existing results on neural or fuzzy adaptive finite/fixed-time tracking control of switched systems, where the tracking error goes to a small neighborhood of the origin within finite time, the proposed one can ensure the output tracking error to reach user-defined accuracy within predefined time, which can be arbitrarily preassigned by the designers. Technically, based on the mode-dependent fuzzy state observer, an adaptive output-feedback predefined-time controller is constructed by incorporating a new state-scale transformation function into the barrier Lyapunov function, ensuring predefined transient behavior and tracking accuracy. Besides, by extending the adaptive control problem to the admissible edge-dependent average dwell time framework, the proposed adaptive control scheme can guarantee that all the closed-loop signals are bounded under switching signals with an admissible edge-dependent average dwell time property. It is less conservative than the average dwell time and mode-dependent average dwell time. Finally, two illustrative examples validate the performance of the method presented. [ABSTRACT FROM AUTHOR]

This article considers the problem of finite-time adaptive fuzzy switching event-triggered control for a class of nonaffine stochastic systems with periodic actuator faults and asymmetric error constraints. First, a framework of semiglobally finite-time stability in probability is established for stochastic systems. Then, unlike the existing observers, an adaptive state observer with faults compensation mechanism is designed to estimate unknown state. In order to balance the systems tracking performance and communication burdens, a switching event-triggered strategy is given to regulate trigger signal and avoids the Zeno behavior effectively. In backstepping process, a nonlinear tracking error-dependent function is constructed to constrain the tracking errors within asymmetric boundaries, and the effects of faults and trigger errors can be compensated completely by the designed finite-time fault-tolerant control strategy. Finally, the effectiveness of presented strategy is further verified by simulation results. [ABSTRACT FROM AUTHOR]

This article investigates the fixed-time adaptive fuzzy containment control problem for nonlinear multiagent systems under the directed communication topologies. The controlled systems have the unknown internal dynamics and mismatched disturbances, and fuzzy logic systems are utilized to identify the unknown internal dynamics. The mismatched disturbances and approximate errors are reconstructed via a disturbance observer. Then, by introducing an adding power integral method, a fixed-time adaptive fuzzy containment DSC scheme is developed to deal with the problem of “computation complexity.” The presented containment control method can not only guarantee that the controlled system is semiglobal practical fixed-time stable, but also avoid the “singular problem” in fixed-time backstepping recursive control technology. Finally, an application of marine surface vehicle is provided to verify the effectiveness of the presented fixed-time fuzzy containment control method. [ABSTRACT FROM AUTHOR]

Objective. The aim of the study is to solve the problems of constructing logicallinguistic (fuzzy) models for controlling the trajectory movement of complex dynamic objects.Method. To build logical-linguistic (fuzzy) control models, theories of fuzzy sets and algorithms of L. Zadeh were used.Result. The process of constructing logical-linguistic (fuzzy) models for controlling the trajectory movement of complex dynamic objects using the theory of fuzzy sets and fuzzy algorithms is given. A marine mobile object (MMO) operating in an uncertain environment was used as a control object in the work. It is shown that logical-linguistic (fuzzy) MPO control models can be built taking into account the rich practical experience of a navigator who does not have special knowledge, expressed by them in a qualitative form for building fuzzy control models.Conclusion. Logicallinguistic control models make it possible to ensure the movement of a marine moving object along a given trajectory with a quality of the controlled process sufficient for practical purposes under conditions of various external disturbances and drift of the parameters of the controlled object. The use of fuzzy control algorithms can significantly reduce the cost of computer time compared to the optimal search algorithm with an acceptable deterioration in the quality of the process and ensure the implementation of the control process in real time.

The article presents a certain investment strategy based on the difference between two moving averages, modified to allow the extraction of patterns. The strategy concept dropped the traditionally considered intersections of two averages and opening positions just after those intersections. Based on the observation of changes happening in the moving averages difference, it has been noticed that for some values of this difference and some values of additional strategy parameters, an interesting pattern appears that allows short-term prediction. These patterns also depended on the first derivative of the moving averages difference and the location of the current price relative to certain thresholds of the difference. Therefore, the strategy uses five parameters, including Stop Loss, adapted to the properties of the time series through machine learning. The importance of machine learning is highlighted by comparing simulation results with and without it. The strategy effectiveness was tested in the Matlab environment on the time series of the WIG20 (primary index of the Warsaw Stock Exchange) historical data. Satisfactory results were obtained considered in terms of minimizing investment risk measured by the Calmar indicator.

The world is moving toward a new connected world in which millions of intelligent processing devices communicate with each other to provide services in transportation, telecommunication, and power grids in the future’s smart cities. Distributed computing is considered one of the efficient platforms for processing and management of massive amounts of data collected by smart devices. This can be implemented by utilizing multi-agent systems (MASs) with multiple autonomous computational entities by memory and computation capabilities and the possibility of message-passing between them. These systems provide a dynamic and self-adaptive platform for managing distributed large-scale systems, such as the Internet-of-Things (IoTs). Despite, the potential applicability of MASs in smart cities, very few practical systems have been deployed using agent-oriented systems. This research surveys the existing techniques presented in the literature that can be utilized for implementing adaptive multi-agent networks in smart cities. The related literature is categorized based on the steps of designing and controlling these adaptive systems. These steps cover the techniques required to define, monitor, plan, and evaluate the performance of an autonomous MAS. At the end, the challenges and barriers for the utilization of these systems in current smart cities, and insights and directions for future research in this domain, are presented.

Stability results for extremum seeking control in R n have predominantly been restricted to local or, at best, semi-global practical stability. Extending semi-global stability results of extremum-seeking systems to unbounded sets of initial conditions often demands a stringent global Lipschitz condition on the cost function, which is rarely satisfied by practical applications. In this paper, we address this challenge by leveraging tools from higher-order averaging theory. In particular, we establish a novel second-order averaging result with global (practical) stability implications. By leveraging this result, we characterize sufficient conditions on cost functions under which uniform global (i.e., under any initialization) practical asymptotic stability can be established for a class of extremum-seeking systems acting on static maps. Our sufficient conditions include the case when the gradient of the cost function, rather than the cost function itself, satisfies a global Lipschitz condition, which covers quadratic cost functions. Our results are also applicable to vector fields that are not necessarily Lipschitz continuous at the origin, opening the door to non-smooth Lie-bracket ES dynamics. We illustrate all the results via different analytical and/or numerical examples. [ABSTRACT FROM AUTHOR]

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Runtime validation techniques are required because complex self-adaptive systems must consistently offer acceptable behavior for important services. The runtime testing can offer further confidence that a self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for self-adaptive systems. The objective is to ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithm facilitates both the execution and adaptation of runtime testing operations.