Time-varying formation control of a collaborative heterogeneous multi agent system

This paper considers the time-varying formation problem of tracking a reference for a class of networked systems consisting of multiple nonlinear subsystems with unknown parameters and non-identical nonlinear dynamics. The communication status among the subsystems is represented by a directed graph. A portion of subsystems have no access to the information of reference. Distributed adaptive controllers are proposed by employing backstepping technique. It is proved that, with the proposed scheme, all the closed-loop signals are globally bounded and all the subsystems can track the reference while building and keeping the prescribed time-varying formation shape. Simulation results are given to illustrate the effectiveness of the proposed scheme.

Distributed adaptive control for time-varying formation tracking of a class of networked nonlinear systems

This paper considers the adaptive time-varying formation tracking control of unmanned aerial vehicles (UAVs) with quantized input. Uncertainties and nonholonomic constraint are involved in the UAV model. With a novel transformation of the final control signal, a very coarse quantization can be achieved. Adaptive quantized controllers are proposed by employing backstepping technique. It is proved that, with our proposed strategy, all signals of the closed-loop system are globally uniformly bounded, and the formation tracking error converges to an arbitrarily small residual set. Simulation results are given to illustrate the effectiveness of the proposed strategy.

Adaptive time-varying formation tracking control of unmanned aerial vehicles with quantized input.

Achieving consensus over a class of high-order nonlinear multiagent systems in the Brunovsky form under directed networks is studied. By introducing coupled sliding-mode controllers, a robust protocol is addressed under which agents achieve agreement upon their positions in the presence of model uncertainties. The main contribution of this correspondence paper compared with similar existing studies is to guarantee achieving consensus in networks of agents in the presence of uncertain nonlinearities in agents models. Moreover, it is shown that consensus is achieved in the presence of any bounded constant communication delays. The results are validated by a numerical example.

Stationary Consensus Control of a Class of High-Order Uncertain Nonlinear Agents With Communication Delays

Objective Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem. Approach The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification. Main results Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data. Significance Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

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Early classification of motor tasks using dynamic functional connectivity graphs from EEG.

Time varying Formation Control Using Differential Game Approach

This paper considers the output consensus problem in non-minimum phase nonlinear multi-agent systems. The main contribution of the paper is to guarantee achieving consensus in the presence of unsta...

Output consensus control of multi-agent systems with nonlinear non-minimum phase dynamics

In this paper, we consider the problem of early decoding of EEG signals associated with tongue-hand motor execution and imagery tasks. A two-step feature extraction strategy is proposed. In the first step, the EEG data is segmented into sequences of quasi-stationary intervals, during which functional networks sustain their connectivity. The second step localizes the functional networks during each segment, to generate the dynamic functional connectivity graphs. Next, the common spatial pattern (CSP) algorithm is employed to select features for the classification problem. To take advantage of the dynamic nature of the generated graphs, a long short term memory (LSTM) classifier is used for classification. Using the first 500 ms of EEG recordings, the proposed framework is capable of classifying different tongue-hand motor execution and imagery tasks, with an average accuracy of 79%.

Early Decoding of Tongue-Hand Movement from EEG Recordings Using Dynamic Functional Connectivity Graphs

Purpose Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal ganglion cells (RGCs) are known to be involved in contrast coding, it still remains unknown whether the retinal layers containing RGCs are linked to a person's contrast sensitivity (e.g., Pelli-Robson contrast sensitivity) and, if so, to what extent the retinal layers are related to behavioral contrast sensitivity. Thus the current study aims to identify the retinal layers and features critical for predicting a person's contrast sensitivity via deep learning. Methods Data were collected from 225 subjects including individuals with either glaucoma, age-related macular degeneration, or normal vision. A deep convolutional neural network trained to predict a person's Pelli-Robson contrast sensitivity from structural retinal images measured with optical coherence tomography was used. Then, activation maps that represent the critical features learned by the network for the output prediction were computed. Results The thickness of both ganglion cell and inner plexiform layers, reflecting RGC counts, were found to be significantly correlated with contrast sensitivity (r = 0.26 ∼ 0.58, Ps < 0.001 for different eccentricities). Importantly, the results showed that retinal layers containing RGCs were the critical features the network uses to predict a person's contrast sensitivity (an average R2 = 0.36 ± 0.10). Conclusions The findings confirmed the structure and function relationship for contrast sensitivity while highlighting the role of RGC density for human contrast sensitivity.

Foroogh Shamsi

Papers

Early classification of motor tasks using dynamic functional connectivity graphs from EEG.

Time varying Formation Control Using Differential Game Approach

Output consensus control of multi-agent systems with nonlinear non-minimum phase dynamics

Early Decoding of Tongue-Hand Movement from EEG Recordings Using Dynamic Functional Connectivity Graphs

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning