1,955 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Decentralised State Feedback Tracking Control for Large-Scale Interconnected Systems Using Sliding Mode Techniques
A class of large-scale interconnected systems with matched and unmatched uncertainties is studied in this thesis, with the objective of proposing a controller based on diffeomorphisms and some techniques to deal with the tracking problem of the system. The main research developed in this thesis includes: 1. Large-scale interconnected system is a complex system consisting of several semi-independent subsystems, which are typically located in distinct geographic or logical locations. In this situation, decentralised control which only collects the local information is the practical method to deal with large-scale interconnected systems. The decentralised methodology is utilised throughout this thesis, guaranteeing that systems exhibit essential robustness against uncertainty. 2. Sliding mode technique is involved in the process of controller design. By introducing a nonsingular local diffeomorphism, the large-scale system can be transformed into a system with a specific regular form, where the matched uncertainty is completely absent from the subspace spanned by the sliding mode dynamics. The sliding mode based controller is proposed in this thesis to successfully achieve high robustness of the closed-loop interconnected systems with some particular uncertainties. 3. The considered large-scale interconnected systems can always track the smooth desired signals in a finite time. Each subsystem can track its own ideal signal or all subsystems can track the same ideal signal. Both situations are discussed in this thesis and the results are mathematically proven by introducing the Lyapunov theory, even when operating under the presence of disturbances. At the end of each chapter, some simulation examples, like a coupled inverted pendulums system, a river pollution system and a high-speed train system, are presented to verify the correctness of the proposed theory. At the conclusion of this thesis, a brief summary of the research findings has been provided, along with a mention of potential future research directions in tracking control of large-scale systems, like more general boundedness of interconnections, possibilities of distributed control, collaboration with intelligent control and so on. Some mathematical theories involved and simulation code are included in the appendix section
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Dual Design PID Controller for Robotic Manipulator Application
This research introduces a dual design proportional–integral–derivative (PID) controller architecture process that aims to improve system performance by reducing overshoot and conserving electrical energy. The dual design PID controller uses real-time error and one-time step delay to adjust the confidence weights of the controller, leading to improved performance in reducing overshoot and saving electrical energy. To evaluate the effectiveness of the dual design PID controller, experiments were conducted to compare it with the PID controller using least overshoot tuning by Chien–Hrones–Reswick (CHR) technique. The results showed that the dual design PID controller was more effective at reducing overshoot and saving electrical energy. A case study was also conducted as part of this research, and it demonstrated that the system performed better when using the dual design PID controller. Overshoot and electrical energy consumption are common issues in systems that can impact performance, and the dual design PID controller architecture process provides a solution to these issues by reducing overshoot and saving electrical energy. The dual design PID controller offers a new technique for addressing these issues and improving system performance. In summary, this research presents a new technique for addressing overshoot and electrical energy consumption in systems through the use of a dual design PID controller. The dual design PID controller architecture process was found to be an effective solution for reducing overshoot and saving electrical energy in systems, as demonstrated by the experiments and case study conducted as part of this research. The dual design PID controller presents a promising solution for improving system performance by addressing the issues of overshoot and electrical energy consumption
Set-based state estimation and fault diagnosis using constrained zonotopes and applications
This doctoral thesis develops new methods for set-based state estimation and
active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii)
discrete-time nonlinear systems whose trajectories satisfy nonlinear equality
constraints (called invariants), (iii) linear descriptor systems, and (iv)
joint state and parameter estimation of nonlinear descriptor systems. Set-based
estimation aims to compute tight enclosures of the possible system states in
each time step subject to unknown-but-bounded uncertainties. To address this
issue, the present doctoral thesis proposes new methods for efficiently
propagating constrained zonotopes (CZs) through nonlinear mappings. Besides,
this thesis improves the standard prediction-update framework for systems with
invariants using new algorithms for refining CZs based on nonlinear
constraints. In addition, this thesis introduces a new approach for set-based
AFD of a class of nonlinear discrete-time systems. An affine parametrization of
the reachable sets is obtained for the design of an optimal input for set-based
AFD. In addition, this thesis presents new methods based on CZs for set-valued
state estimation and AFD of linear descriptor systems. Linear static
constraints on the state variables can be directly incorporated into CZs.
Moreover, this thesis proposes a new representation for unbounded sets based on
zonotopes, which allows to develop methods for state estimation and AFD also of
unstable linear descriptor systems, without the knowledge of an enclosure of
all the trajectories of the system. This thesis also develops a new method for
set-based joint state and parameter estimation of nonlinear descriptor systems
using CZs in a unified framework. Lastly, this manuscript applies the proposed
set-based state estimation and AFD methods using CZs to unmanned aerial
vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most
of the research work has already been published in DOIs
10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353,
10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484,
10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638,
10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems
The recurrent neural network has been greatly developed for effectively
solving time-varying problems corresponding to complex environments. However,
limited by the way of centralized processing, the model performance is greatly
affected by factors like the silos problems of the models and data in reality.
Therefore, the emergence of distributed artificial intelligence such as
federated learning (FL) makes it possible for the dynamic aggregation among
models. However, the integration process of FL is still server-dependent, which
may cause a great risk to the overall model. Also, it only allows collaboration
between homogeneous models, and does not have a good solution for the
interaction between heterogeneous models. Therefore, we propose a Distributed
Computation Model (DCM) based on the consortium blockchain network to improve
the credibility of the overall model and effective coordination among
heterogeneous models. In addition, a Distributed Hierarchical Integration (DHI)
algorithm is also designed for the global solution process. Within a group,
permissioned nodes collect the local models' results from different
permissionless nodes and then sends the aggregated results back to all the
permissionless nodes to regularize the processing of the local models. After
the iteration is completed, the secondary integration of the local results will
be performed between permission nodes to obtain the global results. In the
experiments, we verify the efficiency of DCM, where the results show that the
proposed model outperforms many state-of-the-art models based on a federated
learning framework
Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation
Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.
One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers.
A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks.
A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation
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