Control of Autonomous Underwater Vehicles

In this thesis an overview of Autonomous Underwater Vehicles (AUV) is presented which covers the advancements in AUV technology in last two decades, different components of AUV and the applications of AUVs. A glimpse on AUV research in India is presented. A nonlinear model of AUV is obtained through kinematics and dynamics equation which is linearized about an operating point to get linearized pitch & depth plane model. A two loop controller (PI control) is used to control the pitch and in turn the depth of the AUV. After having developed, simulated and analyzed the pitch and depth controller for a single AUV, we focus our attention towards developing formation control of three AUVs. The formation control for multiple Autonomous Underwater Vehicles (AUVs) is considered in spatial motions.The objective is to drive a leader AUV along a desired trajectory, and make the follower AUVs keep a desired formation with respect to the leader’s configuration in 3-dimensional spaces (leader-follower formation control). Also, an obstacle avoidance scheme, using pitch and depth control, is used to avoid static obstacles in the path of AUV. The results of the above three control objectives such as tracking control of AUV, controller for avoiding obstacles and formation control of multiple AUVs are presented and discussed in the thesis

Logistic Regression with Tangent Space based Cross-Subject Learning for Enhancing Motor Imagery Classification

Brain-computer interface (BCI) performance is often impacted due to the inherent non-stationarity in the recorded EEG signals coupled with high variability across subjects. This study proposes a novel method using Logistic Regression with Tangent Space-based Transfer Learning (LR-TSTL) for motor imagery (MI)-based BCI classification problems. The single-trial covariance matrix (CM) features computed from the EEG signals are transformed into a Riemannian geometry frame and tangent space features are computed by considering the lower triangular matrix. These are then further classified using the logistic regression model to improve classification accuracy. The performance of LR-TSTL is tested on healthy subjects’ dataset as well as on stroke patients’ dataset. As compared to existing within-subject learning approaches the proposed method gave an equivalent or better performance in terms of average classification accuracy (78.95 11.68%), while applied as leave one-out cross-subject learning for healthy subjects. Interestingly, for the patient dataset LR-TSTL significantly (p <0.05) outperformed the current benchmark performance by achieving an average classification accuracy of 81.75 6.88%. The results show that the proposed method for cross-subject learning has the potential to realize the next generation of calibration-free BCI technologies with enhanced practical usability especially in the case of neurorehabilitative BCI designs for stroke patients

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain computer interface (BCI) systems. In this study two sliding window techniques are proposed to enhance binary classification of motor imagery (MI). The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows which is named as SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a dataset of healthy individuals and on a stroke patients dataset. As compared to the existing state-of-the-art the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients dataset for left vs. right hand MI with lower standard deviation. For both the datasets the classification accuracy (CA) was approximately 80% and kappa (&#x03BA;) was 0.6. The results show that the sliding window based prediction of MI using SW-LCR and SW-Mode is robust against inter-trial and inter-session inconsistencies in the time of activation within a trial and thus can lead to reliable performance in a neurorehabilitative BCI setting

Sliding Window along with EEGNet based Prediction of EEG Motor Imagery

The need for repeated calibration and accounting for inter subject variability is a major challenge for the practical applications of a brain-computer interface. The problem becomes more severe when it is applied for the neurorehabilitation of stroke patients where the brain-activation pattern may differ quite a bit as compared to healthy individuals due to the altered neurodynamics because of a lesion. There were several approaches to handle this problem in the past that depend on creating customized features that can be generalized among the individual subjects. Recently, several deep learning architectures came into the picture although they often failed to produce superior accuracy as compared to the traditional approaches and mostly do not follow an end-to-end architecture as they depend on custom features. However, a few of them have the promising ability to create more generalizable features in an end-to-end fashion such as the popular EEGNet architecture. Although EEGNet was applied for decoding stroke patient’s motor imagery (MI) data with limited success it failed to achieve superior performance over the traditional methods. In this study, we have augmented the EEGNet based decoding by introducing a post-processing step called the longest consecutive repetition (LCR) in a sliding window-based approach and named it EEGNet+LCR. The proposed approach was tested on a dataset of 10 hemiparetic stroke patients’ MI data set yielding superior performance against the only EEGNet and a more traditional approach such as common spatial pattern (CSP)+support vector machine (SVM) for both within- and cross-subject decoding of MI signals. We also observed comparable and satisfactory performance of the EEGNet+LCR in both the within- and cross-subject categories which are rarely found in literature making it a promising candidate to realize practically feasible BCI for stroke rehabilitation