Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

[[abstract]]Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Intelligent Second-Order Sliding-Mode Control for Chaotic Tracking Problem

[[conferencetype]]國際[[conferencedate]]20140909~20140912[[booktype]]紙本[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Sapporo, Japa

UAV Model-based Flight Control with Artificial Neural Networks: A Survey

Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Neural correlates of true and false memory in mild cognitive impairment

The goal of this research was to investigate the changes in neural processing in mild cognitive impairment. We measured phase synchrony, amplitudes, and event-related potentials in veridical and false memory to determine whether these differed in participants with mild cognitive impairment compared with typical, age-matched controls. Empirical mode decomposition phase locking analysis was used to assess synchrony, which is the first time this analysis technique has been applied in a complex cognitive task such as memory processing. The technique allowed assessment of changes in frontal and parietal cortex connectivity over time during a memory task, without a priori selection of frequency ranges, which has been shown previously to influence synchrony detection. Phase synchrony differed significantly in its timing and degree between participant groups in the theta and alpha frequency ranges. Timing differences suggested greater dependence on gist memory in the presence of mild cognitive impairment. The group with mild cognitive impairment had significantly more frontal theta phase locking than the controls in the absence of a significant behavioural difference in the task, providing new evidence for compensatory processing in the former group. Both groups showed greater frontal phase locking during false than true memory, suggesting increased searching when no actual memory trace was found. Significant inter-group differences in frontal alpha phase locking provided support for a role for lower and upper alpha oscillations in memory processing. Finally, fronto-parietal interaction was significantly reduced in the group with mild cognitive impairment, supporting the notion that mild cognitive impairment could represent an early stage in Alzheimer’s disease, which has been described as a ‘disconnection syndrome’

Load forecast on a Micro Grid level through Machine Learning algorithms

As Micro Redes constituem um sector em crescimento da indústria energética, representando uma mudança de paradigma, desde as remotas centrais de geração até à produção mais localizada e distribuída. A capacidade de isolamento das principais redes elétricas e atuar de forma independente tornam as Micro Redes em sistemas resilientes, capazes de conduzir operações flexíveis em paralelo com a prestação de serviços que tornam a rede mais competitiva. Como tal, as Micro Redes fornecem energia limpa eficiente de baixo custo, aprimoram a coordenação dos ativos e melhoram a operação e estabilidade da rede regional de eletricidade, através da capacidade de resposta dinâmica aos recursos energéticos. Para isso, necessitam de uma coordenação de gestão inteligente que equilibre todas as tecnologias ao seu dispor. Daqui surge a necessidade de recorrer a modelos de previsão de carga e de produção robustos e de confiança, que interligam a alocação dos recursos da rede perante as necessidades emergentes. Sendo assim, foi desenvolvida a metodologia HALOFMI, que tem como principal objetivo a criação de um modelo de previsão de carga para 24 horas. A metodologia desenvolvida é constituída, numa primeira fase, por uma abordagem híbrida de multinível para a criação e escolha de atributos, que alimenta uma rede neuronal (Multi-Layer Perceptron) sujeita a um ajuste de híper-parâmetros. Posto isto, numa segunda fase são testados dois modos de aplicação e gestão de dados para a Micro Rede. A metodologia desenvolvida é aplicada em dois casos de estudo: o primeiro é composto por perfis de carga agregados correspondentes a dados de clientes em Baixa Tensão Normal e de Unidades de Produção e Autoconsumo (UPAC). Este caso de estudo apresenta-se como um perfil de carga elétrica regular e com contornos muito suaves. O segundo caso de estudo diz respeito a uma ilha turística e representa um perfil irregular de carga, com variações bruscas e difíceis de prever e apresenta um desafio maior em termos de previsão a 24-horas A partir dos resultados obtidos, é avaliado o impacto da integração de uma seleção recursiva inteligente de atributos, seguido por uma viabilização do processo de redução da dimensão de dados para o operador da Micro Rede, e por fim uma comparação de estimadores usados no modelo de previsão, através de medidores de erros na performance do algoritmo.Micro Grids constitute a growing sector of the energetic industry, representing a paradigm shift from the central power generation plans to a more distributed generation. The capacity to work isolated from the main electric grid make the MG resilient system, capable of conducting flexible operations while providing services that make the network more competitive. Additionally, Micro Grids supply clean and efficient low-cost energy, enhance the flexible assets coordination and improve the operation and stability of the of the local electric grid, through the capability of providing a dynamic response to the energetic resources. For that, it is required an intelligent coordination which balances all the available technologies. With this, rises the need to integrate accurate and robust load and production forecasting models into the MG management platform, thus allowing a more precise coordination of the flexible resource according to the emerging demand needs. For these reasons, the HALOFMI methodology was developed, which focus on the creation of a precise 24-hour load forecast model. This methodology includes firstly, a hybrid multi-level approach for the creation and selection of features. Then, these inputs are fed to a Neural Network (Multi-Layer Perceptron) with hyper-parameters tuning. In a second phase, two ways of data operation are compared and assessed, which results in the viability of the network operating with a reduced number of training days without compromising the model's performance. Such process is attained through a sliding window application. Furthermore, the developed methodology is applied in two case studies, both with 15-minute timesteps: the first one is composed by aggregated load profiles of Standard Low Voltage clients, including production and self-consumption units. This case study presents regular and very smooth load profile curves. The second case study concerns a touristic island and represents an irregular load curve with high granularity with abrupt variations. From the attained results, it is evaluated the impact of integrating a recursive intelligent feature selection routine, followed by an assessment on the sliding window application and at last, a comparison on the errors coming from different estimators for the model, through several well-defined performance metrics

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

Intelligent Controller Based on Artificial Neural Network and INC Based MPPT for Grid Integrated Solar PV System

Solar photovoltaic (PV) systems have become an integral part of today's advanced energy infrastructure due to its low kinetic energy, its abundance availability, and its freedom from human interference. Solar PV systems have the potential to greatly reduce our reliance on fossil fuels, but their intermittent nature means they cannot provide a constant source of electricity. The system's security should be well thought out, and it should be able to withstand a lot of abuse. The current energy system faces a significant difficulty in ensuring continuous supply. In this study, a three-phase, two-stage photovoltaic system that is managed by artificial neural networks (ANN). A DC-DC boost converter with maximum power point tracking (MPPT) based on the incremental conductance (INC) method is incorporated in the first stage. In the next step, an ANN-based controller optimizes the performance of a three-phase switching PWM inverter that is connected to the grid by controlling currents along the d-q axis. Comprehensive simulations were carried out using MATLAB or Simulink to evaluate the system's performance under various illumination and temperature conditions. Results show that the suggested approach outperforms the baseline in a number of areas. Better dynamic reactions, accurate tracking of reference currents within permissible bounds, and quick settling periods after startup are all displayed by it. These findings show that our method has the potential to greatly improve the efficiency and dependability of solar PV systems. The results of this study have implications for renewable energy in general and present a viable path toward enhancing the resilience and sustainability of energy infrastructure

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions