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In this paper, a model-free high-order terminal sliding mode controller (TSMC) is developed for single input–single output Lipschitz nonlinear systems in presence of external disturbances. The proposed method is data-driven, i.e. it is based on online input and output information. In another word, the method is not model-based and can be applied to systems with unknown dynamics. The proposed method employs a high-order switching surface to mitigate the chattering problem. The disturbance estimation technique is also applied to overcome external disturbances. Rigorous theoretical analysis is carried out to verify convergence of states to the switching surface. To demonstrate the effectiveness and applicability of the proposed approach in d
This paper considers a network of agents, where each agent is assumed to take actions optimally with respect to a predefined payoff function involving the latest actions of the agent's neighbors. Neighborhood relationships stem from payoff functions rather than actual communication channels between the agents. A principal is tasked to optimize the network's performance by controlling the information available to each agent with regard to other agents' latest actions. The information control by the principal is done via a partial observability approach, which comprises a static partitioning of agents into blocks and making the mean of agents' latest actions within each block publicly available. While the problem setup is general in terms of
Brain-computer interface (BCI) systems are usually designed specifically for each subject based on motor imagery. Therefore, the usability of these networks has become a significant challenge. The network has to be designed separately for each user, which is time-consuming for the user. Therefore, this study proposes a method by which the calibration time is significantly reduced while the classification accuracy is increased. In this method, we calibrated the features extracted from the motor imagery task by dividing the features extracted from the resting-state into both open-eye and closed-eye modes and the state in which the subject moves his eyes. The best classification accuracy was obtained using the SVM classifier using the resting-
Brain-computer interface (BCI) systems are usually designed specifically for each subject based on motor imagery. Therefore, the usability of these networks has become a significant challenge. The network has to be designed separately for each user, which is time-consuming for the user. Therefore, this study proposes a method by which the calibration time is significantly reduced while the classification accuracy is increased. In this method, we calibrated the features extracted from the motor imagery task by dividing the features extracted from the resting-state into both open-eye and closed-eye modes and the state in which the subject moves his eyes. The best classification accuracy was obtained using the SVM classifier using the resting-
One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks. The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then the three models which are the most comprehensive and vastly accepted in the literature, are cons
Opinion dynamics modeling has long been interesting to scientists because of its applications in the marketing industry as well as elections. Agreement, e.g. on a special product, has an affective influence on its manufacturing process. However, most of the existing opinion dynamics models are Linear Time Invariant (LTI). In this paper, eight Non-Linear Time Variant (NLTV) models for opinion dynamics are proposed and are put to the test on subjective synthetic networks. Despite all the efforts, the issues of stability and convergence in time-varying networks, which can be found in industrial contexts, have not been resolved yet and only sufficient conditions have been derived. In the proposed models, unlike in the classical ones such as the
Objectives: In this work, we propose a unique non-invasive treatment method based on neuroplasticity and Transcranial Alternating Current Stimulation (tACS) combined with traditional physical therapy where we aim to match the tACS frequency with the mechanical movement frequency of the rehabilitation. This method is durable and reproducible and at the same time, provides faster recovery for stroke patients and ultimately reduces the medical costs.Method: In this study, we recruited and examined a total of 3 persons (72, 68, 59 years old) who were the subject of stroke within the past 6 months. We applied 3 types of therapy, the first patient only received traditional physical therapy. The second patient received tACS with arbitrary frequenc
BackgroundThe mechanism of glucose regulation in human blood is a nonlinear complicated biological system with uncertain parameters and external disturbances which cannot be imitated accurately by a simple mathematical model. So to achieve an artificial pancreas, a method that does not need a model is necessary.MethodsIn this paper, a model free third order terminal sliding mode controller is developed and applied to blood glucose regulation system. So in this paper, a data driven control method is proposed which doesn't need a pre specified mathematical model of the system. The proposed method uses a third order terminal sliding mode controller to overcome the problem in finite time without chattering. It also uses a disturbance estimation
A principled approach to modeling sociocognitive networks is fundamental to understanding the network interrelations which in turn can be used in many applications such as human behavior analysis or team performance assessment. More specifically, in the opinion domain, learning the cognitive links and making a proper model for causal relationships between individuals is necessary for both analysis and control purposes. There are several mathematical models for opinion dynamics. However, few of them have been tested to be consistent with real-world data. In this article, a new hybrid model for opinion dynamics is proposed and is put to test with subjective experiments. It is imperative that a realistic model considers two cognitive facts: 1)
Lower extremity exoskeletons have been developed as a motion assistive technology in recent years. Walking pattern generation is a fundamental topic in the design of these robots. The usual approach with most exoskeletons is to use a pre-recorded pattern as a look-up table. There are some deficiencies with this method, including data storage limitation and poor regulation relating to the walking parameters. In addition, the walking parameters can be taken in hand very hard. Therefore modeling the human walking pattern is required. The few existing models provide piece by piece walking patterns, only generating at the beginning of each stride. In this paper, a real-time walking pattern generation method is provided which enables changing the
Assistive robots have been grown in recent years and joints’ moving pattern is one of the important issues in these robots. The predefined trajectory for the robot brings some stability difficulties for the users. This paper introduces a systematic way to produce an optimal gait for the controller of the lower extremity exoskeleton. This optimal gait producing strategy releases us from the exhausting procedure of walking pattern capture in different paces which requires various devices and motion analysis lab. A feedback-controlled system is defined which enables us to change walking parameters during steps with a smooth walking pattern which is of vital importance for the patients in these kinds of robots. This goal has been achieved by
One of the interesting topics in the field of social networks engineering is opinion change dynamics in a discussion group and how to use real experimental data in order to identify an interaction pattern among individuals. In this paper, we propose a method that utilizes experimental data in order to identify the influence network between individuals in social networks. The employed method is based on convex optimization and can identify interaction patterns precisely. This technique considers individuals’ opinions in multiple dimensions. Moreover, the opinion dynamics models that have been introduced in the literature are investigated. Then, the three models which are the most comprehensive and vastly accepted in the literature, are con
Aims: One of the most important areas in medical research is the identification of disease-causing genes, which helps the identification of mechanisms underlying disease and as a result helps the early diagnosis of disease and the better treatment. In recent years, microarray technology has assisted biologists to gain a better understanding of cellular processes. To this end, the application of efficient methods in microarray data analysis is very important. The aim of this study was the introduction of GRAP Gene as Alzheimer’s disease candidate gene using microarray data analysis.Materials and Methods: In the present bioinformatic study, which was conducted on an Alzheimer's microarray data set containing 12990 genes, 15 patients, and 16
In this article, a novel data-driven sliding mode controller for a single-input single-output nonlinear system is designed from a new perspective. The proposed controller is model-free, that is, it is based on just input and output data. Therefore, it is suitable for systems with unknown models. The approach to design the controller is based on an optimization procedure. First, a linear regression estimation is assumed to exist for the system behavior. Then an optimal controller is designed for this estimated model. The cost function is proposed in a way that minimization of it, could guarantee that the sliding function and its first derivative converge to zero. Based on rigorous theoretical analysis, boundedness of the tracking error is th
We present comparator-based gene network designs for feedback control of the concentration of gene products. Two gene circuit designs are proposed, each of which compares concentrations of an input transcription factor (the reference input) with the protein product of a target gene (the process output). These designs employ a genetic relay switch and a shifted subtractor as components of a discrete comparator circuit and a bistable comparator circuit, respectively. Numerical simulation results illustrate the potential effectiveness of the designed controllers.
Lower extremity exoskeleton has been developed as a motion assistive technology in recent years. Walking pattern generation is a fundamental topic in the design of these robots. The usual approach with most exoskeletons is to use a pre-recorded pattern as a look-up table. There are some deficiencies with this method, including data storage limitation and poor regulation relating to the walking parameters. Therefore modeling human walking patterns to use in exoskeletons is required. The few existing models provide piece by piece walking patterns, only generating at the beginning of each stride cycle in respect to fixed walking parameters. In this paper, we present a real-time walking pattern generation method which enables changing the walki
In this paper a model free sliding mode controller (SMC) based on a novel reaching law for SISO nonlinear systems is provided. The novelty of the work lies in the approach used to achieve the reaching law. The proposed reaching law is more general than those proposed in recent works. Naturally, it is consistent with the proposed control law, which is composed of an optimal term and an exponential switching term. A proper cost function forces the sliding variable and its derivative to vanish. The switching part consists of an exponential term, which decreases the quasi-sliding domain. It is shown analytically that the sliding function enters and remains in the quasi-sliding bound. In addition, the UUB stability of error is rigorously proved.
Walking and standing are the two fundamental problems in patients with spinal cord injury. Gait disorders in neurologically disabled people can be treated by various techniques available today. Exoskeleton robots and functional electrical stimulation (FES) are the two important solutions in this field. However, each of them has its own drawback. The patient using an exoskeleton doesn't participate in walking, in addition, battery consumption is a limiting problem. On the other hand, the muscles fatigue and complexity of joints control should be noticed in functional electrical stimulation. In this paper, in order to solve these problems, combining exoskeleton and FES system is considered. The Exoped ? exoskeleton robot which consists of fo