Model Reference Tracking Control Solutions for a Visual Servo System Based on a Virtual State from Unknown Dynamics

This paper focuses on validating a model-free Value Iteration Reinforcement Learning (MFVI-RL) control solution on a visual servo tracking system in a comprehensive manner starting from theoretical convergence analysis to detailed hardware and software implementation. Learning is based on a virtual state representation reconstructed from input-output (I/O) system samples under nonlinear observability and unknown dynamics assumptions, while the goal is to ensure linear output reference model (ORM) tracking. Secondary, a competitive model-free Virtual State-Feedback Reference Tuning (VSFRT) is learned from the same I/O data using the same virtual state representation, demonstrating the framework’s learning capability. A model-based two degrees-of-freedom (2DOF) output feedback controller serving as a comparisons baseline is designed and tuned using an identified system model. With similar complexity and linear controller structure, MFVI-RL is shown to be superior, confirming that the model-based design issue of poor identified system model and control performance degradation can be solved in a direct data-driven style. Apart from establishing a formal connection between output feedback control, state feedback control and also between classical control and artificial intelligence methods, the results also point out several practical trade-offs, such as I/O data exploration quality and control performance leverage with data volume, control goal and controller complexity.

MIGRANT AS A WORLD CITIZEN: (IM)POSSIBLE POLITICAL PERSPECTIVES

In one of its many forms, migrations are an intrinsic characteristic of human civilization from its very beginnings until today, when they even exceed the limits of the habitat of their subject. Even with the already established laws of migration processes, it is very perilous to predict their outcome in an increasingly complex and unpredictable modern world that requires a comprehensive approach, usually empirically unverifiable. One such outcome is the model of a post-national global world whose World Government creates its extraterritorial population in a virtual state, providing it with identity and rights in reality.

A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability

To accurately classify the stability of surrounding rock masses, a novel method (VSV-BDA) based on virtual state variables (VSVs) and Bayesian discriminant analysis (BDA) is proposed. The factors influencing stability are mapped by an artificial neural network (ANN) capable of recognizing the model of rock mass classification, and the obtained output vector is treated as VSVs, which are verified as obeying a multinormal distribution with equal covariance matrixes by normal distribution testing and constructed statistics. The prediction variance ratio test method is introduced to determine the optimal dimension of the VSVs. The VSV-BDA model is constructed through the use of VSVs and the optimal dimension on the basis of the training samples, which are divided from the collected samples into three situations having different numbers. ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering.

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable systems with unknown dynamics. For the observable system, a new state representation in terms of input/output (IO) data is derived. Consequently, the Virtual State Feedback Tuning (VRFT)-based solution is redefined to accommodate virtual state feedback control, leading to an original stability-certified Virtual State-Feedback Reference Tuning (VSFRT) concept. Both VSFRT and AI-VIRL use neural networks controllers. We find that AI-VIRL is significantly more computationally demanding and more sensitive to the exploration settings, while leading to inferior LRMO tracking performance when compared to VSFRT. It is not helped either by transfer learning the VSFRT control as initialization for AI-VIRL. State dimensionality reduction using machine learning techniques such as principal component analysis and autoencoders does not improve on the best learned tracking performance however it trades off the learning complexity. Surprisingly, unlike AI-VIRL, the VSFRT control is one-shot (non-iterative) and learns stabilizing controllers even in poorly, open-loop explored environments, proving to be superior in learning LRMO tracking control. Validation on two nonlinear coupled multivariable complex systems serves as a comprehensive case study.

Virtual State: Scoping Review of Russian Political and Social Sciences

This review focuses on analysis and determination of problematic field based on phenomena and related concepts such as micronation, cyber-state, virtual state and others. Actualization of this topic is carried out due to the technological development of modern society, the emergence of new social, political, economic and other actors, the concentration of a large amount of resources by them which could make them an influential power or an alternative of social and political organization. Using scoping methods, comparative and content analysis, as well as services of network and statistical analysis the article examines the level of virtual states embeddedness into Russian political and social sciences, the formation of the corresponding research field, direction or school. Special attention is paid to the Russian segment of world science in the framework of the study of virtual states and problems are faced by Russian researchers. This will allow researchers to see the context, the most relevant areas of work, help to correctly use the concepts in Russian and English academic texts.

The Avatar as a Self-Representation Model for Expressive and Intelligent Driven Visualizations in Immersive Virtual Worlds

One could argue that the “self” as a human entity will be affected by the desire for more intelligence-driven products and creations. The avatar acts as an agent that unlocks the pathway to a better version of the human self. The true power of the avatar is beyond the comprehension of those that see it as a replica or a clone of a human. Rather, for those that look through the lens of the future, the avatar has the power(s) that mankind ultimately desires in their fulfilment of selfhood. To this end, the perception of the avatar needs to be reconsidered in order to appropriately recognise the avatar's multi-dimensional advantages and opportunities it holds for society and how it enhances the human condition. This paper creates a backdrop for understanding the avatar in the connected modalities of the real and virtual state of environments. This paper will also attempt to tackle the connected conditions that emerge as avatar-to-avatar interaction happens through the works of current research to understand the avatar more in-depth.