Nonlinear modelling and optimal control via Takagi-Sugeno fuzzy techniques: A quadrotor stabilization

Using the principles of Takagi-Sugeno fuzzy modelling allows the integration of flexible fuzzy approaches and rigorous mathematical tools of linear system theory into one common framework. The rule-based T-S fuzzy model splits a nonlinear system into several linear subsystems. Parallel Distributed Compensation (PDC) controller synthesis uses these T-S fuzzy model rules. The resulting fuzzy controller is nonlinear, based on fuzzy aggregation of state controllers of individual linear subsystems. The system is optimized by the linear quadratic control (LQC) method, its stability is analysed using the Lyapunov method. Stability conditions are guaranteed by a system of linear matrix inequalities (LMIs) formulated and solved for the closed loop system with the proposed PDC controller. The additional GA optimization procedure is introduced, and a new type of its fitness function is proposed to improve the closed-loop system performance.Web of Science71110

A comparison between a two-feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy proportional-integral controller (FPIC). The performance of the presented control scheme was demonstrated through simulations. However, the efficiency of the proposed controller is dependent on the initial values of the parameters of the MRAC controller as well as on the effort required for a human to manually construct fuzzy rules. An alternative solution to the problem might consist of using methods that do not assume a priori knowledge: a solution that derives its properties from a machine learning procedure. In this way, the controller would be able to automatically learn the properties of the elastic actuator to be controlled. In this work, a novel controller for the proposed elastic actuator is presented based on the use of an artificial neural network (ANN) that is trained with reinforcement learning. The newly designed control algorithm is extensively compared with the former approach. Simulation results are presented for both methods. The authors seek to achieve a fair, non-biased, risk-aware and trustworthy comparison

A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators

publishedVersio

Improving Risk Assessment of Miscarriage During Pregnancy with Knowledge Graph Embeddings

Miscarriages are the most common type of pregnancy loss, mostly occurring in the first 12 weeks of pregnancy. Pregnancy risk assessment aims to quantify evidence to reduce such maternal morbidities, and personalized decision support systems are the cornerstone of high-quality, patient-centered care to improve diagnosis, treatment selection, and risk assessment. However, data sparsity and the increasing number of patient-level observations require more effective forms of representing clinical knowledge to encode known information that enables performing inference and reasoning. Whereas knowledge embedding representation has been widely explored in the open domain data, there are few efforts for its application in the clinical domain. In this study, we contrast differences among multiple embedding strategies, and we demonstrate how these methods can assist in performing risk assessment of miscarriage before and during pregnancy. Our experiments show that simple knowledge embedding approaches that utilize domain-specific metadata perform better than complex embedding strategies, although both can improve results comparatively to a population probabilistic baseline in both AUPRC, F1-score, and a proposed normalized version of these evaluation metrics that better reflects accuracy for unbalanced datasets. Finally, embedding approaches provide evidence about each individual, supporting explainability for its model predictions in such a way that humans understand

Digital Wellbeing for All: Expanding Inclusivity to Embrace Diversity in Socio-Emotional Status

The ubiquity of information and communication technology contributed positively in enhancing lives, mainly in increasing productivity and economic growth, while their impact on life satisfaction and wellbeing has been a hidden cost. Digital media shall empower users to maximise their digital wellbeing, i.e. healthy and regulated relationship with technology. Similar to usability, people differ in their needs to achieve and maintain their digital wellbeing. A technology design shall be inclusive in how it helps users to increase their digital wellbeing and reduce possible harm. Typical inclusivity dimensions in Human-Computer Interaction research include gender, race, physical and cognitive abilities, with the aim of making the product usable by the wider possible user set. However, another range of inclusivity dimensions becomes prominent and that is the diversity in users' socio-emotional characteristics such as susceptibility to online pressure (technical and social), resilience and others. Such characteristics can be traits, e.g. introversion, or temporal status, e.g. being in a low mood. In this position paper, we are proposing digital wellbeing as a target for an inclusive design where technology designers need to anticipate and reduce the negative impact of their products and services on the wellbeing of users through considering their diverse socio-emotional status

Hedonic Coalition Formation for Task Allocation with Heterogeneous Robots

Tasks in the real world are complex in nature and often require multiple robots to collaborate in order to be accomplished. However, multiple robots with the same set of sensors working together might not be the optimal solution. In many cases a task might require different sensory inputs and outputs. However, allocating a large variety of sensors on each robot is not a cost-effective solution. As such, robots with different attributes must be considered. In this thesis we study the coalition formation problem for task allocation with multiple heterogeneous (equipped with a different set of sensors) robots. The proposed solution is implemented utilizing a Hedonic Coalition Formation strategy, rooted in game theory, coupled with bipartite graph matching. Our proposed algorithm aims to minimize the total cost of the formed coalitions and to maximize the matching between the required and the allocated types of robots to the tasks. Simulation results show that it produces near-optimal solutions (up to 94%) in a negligible amount of time (0:19 ms. with 100 robots and 10 tasks)

Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings

A framework for aerospace vehicle reasoning (FAVER)

Airliners spend over 9% of their total revenue in Maintenance, Repair, and Overhaul (MRO) and working to bring down the cost and time involved. The prime focus is on unexpected downtime and extended maintenance leading to delays in the flights, which also reduces the trustworthiness of the airliners among the customers. One of the effective solutions to address this issue is Condition based Maintenance (CBM), in which the aircraft systems are monitored frequently, and maintenance plans are customized to suit the health of these systems. Integrated Vehicle Health Management (IVHM) is a capability enabling CBM by assessing the current condition of the aircraft at component/ Line Replaceable Unit/ system levels and providing diagnosis and remaining useful life calculations required for CBM. However, there is a lack of focus on vehicle level health monitoring in IVHM, which is vital to identify fault propagation between the systems, owing to their part in the complicated troubleshooting process resulting in prolonged maintenance. This research addresses this issue by proposing a Framework for Aerospace Vehicle Reasoning, shortly called FAVER. FAVER is developed to enable isolation and root cause identification of faults propagating between multiple systems at the aircraft level. This is done by involving Digital Twins (DTs) of aircraft systems in order to emulate interactions between these systems and Reasoning to assess health information to isolate cascading faults. FAVER currently uses four aircraft systems: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, to demonstrate its ability to provide high level reasoning, which can be used for troubleshooting in practice. FAVER is also demonstrated for its ability to expand, update, and scale for accommodating new aircraft systems into the framework along with its flexibility. FAVER’s reasoning ability is also evaluated by testing various use cases.Transport System

Human and Artificial Intelligence

Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone. Hence, the integration between Human Intelligence and Artificial Intelligence is needed. However, several challenges make this integration complex. The aim of this Special Issue was to provide a large and varied collection of high-level contributions presenting novel approaches and solutions to address the above issues. This Special Issue contains 14 papers (13 research papers and 1 review paper) that deal with various topics related to human–machine interactions and cooperation. Most of these works concern different aspects of recommender systems, which are among the most widespread decision support systems. The domains covered range from healthcare to movies and from biometrics to cultural heritage. However, there are also contributions on vocal assistants and smart interactive technologies. In summary, each paper included in this Special Issue represents a step towards a future with human–machine interactions and cooperation. We hope the readers enjoy reading these articles and may find inspiration for their research activities

Problematic attachment to social media: lived experience and behavioural archetypes.

Social media are widely used by people to help satisfying personal and social needs. Examples include the enhancement of self-image, self-esteem, complementarity, relatedness and popularity. However, the relationship with social media can become problematic and lead to hurt various aspects of life, including wellbeing, psychological and emotional state and sociability. Existing literature provided evidence that obsessive and excessive use of social media can be associated with behavioural addiction symptoms such as conflict, mood modification, salience, tolerance, withdrawal and relapse. Research has also shown that social media can be equipped or augmented with tools to help users who are willing to change their problematic attachment behaviour. Designing such behaviour change tools can be challenging because people differ in their problematic attachment to social media. Unlike existing literature, which focuses on understanding the psychological correlates of social media activity and reasons that facilitate attachment. This thesis aims to explore the real-world experience of people who have a problematic attachment to social media and the role of social media design in such attachment. In order to achieve the goal of the thesis, multi-phase qualitative studies with people who experienced problematic attachment have been conducted. This helped to achieve a deep understanding of the role of social media in facilitating problematic attachment and reveal emotions and psychological states associated with it as well as the social media design features which contribute to triggering such states. The findings emerged through multi-phase qualitative studies helped developing user archetypes characterising how people differ in their problematic attachments to social media. These behavioural archetypes are intended to help the design process of software-assisted solutions to keep a healthy relationship with social media. The thesis evaluates how the archetypes can help a design team communication and engagement and aid a more creative and efficient design process