The complexity of problems related to the formation of cognitive models of complexsystems based on methods of econometric analysis of available time series of domain parameters is considered. An approach is proposed ...
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The complexity of problems related to the formation of cognitive models of complexsystems based on methods of econometric analysis of available time series of domain parameters is considered. An approach is proposed to solve the problem of representing a complex system in the form of an understandable and practical model that includes a limited number of factors, but as adequately as possible reflects the dynamic processes occurring in this system. An example of the application of the proposed approach is considered, exploring the possibilities of improving the efficiency of the functioning of financial markets. The vector autoregression model, which is a model of the dynamics of several time series, is chosen as the basis for the formation of the model. Options for improving the model by adding virtual factors and cointegrating relations are proposed, and the results of the study of the developed model are also presented. Copyright (C) 2021 The Authors.
This document discusses the relation between H 2 / H ∞ control and Nash game problem for infinite Markov jump stochastic systems (MJSSs). Via a countably infinite set of coupled generalized differential Riccati equa...
This document discusses the relation between H 2 / H ∞ control and Nash game problem for infinite Markov jump stochastic systems (MJSSs). Via a countably infinite set of coupled generalized differential Riccati equations (ICGDREs), H 2 / H ∞ optimal controller and Nash equilibrium strategies are solved, respectively. Through our research, we find that the equivalence between these two problems hinge on if the diffusion term contains disturbance. At last, the validity of the proposed method has been proved by a numerical example.
This paper investigates the trajectory tracking problem of autonomous underwa-ter vehicles (AUV).In order to solve the problems of underdrive,non-completeness,strong cou-pling and modeling errors with random perturbat...
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The analysis of approaches to assessing the vulnerability of complex network systems and indicators used to measure the effect of external influences on the nodes and connections of the network is carried out. The pro...
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The paper considers the simplest model of an export enterprise operating under uncertainty. The set of possible values of uncertain factors is specified using a scenario tree. Optimization problems for finding guarant...
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ISBN:
(数字)9798350375718
ISBN:
(纸本)9798350375725
The paper considers the simplest model of an export enterprise operating under uncertainty. The set of possible values of uncertain factors is specified using a scenario tree. Optimization problems for finding guaranteeing controls are stated. Their solution is demonstrated using a numerical example.
Within the scope of this work, a format for a knowledge base of typical and atypical interaction scenarios between objects in a shared workspace is presented. The most complex class of human-robot interaction, collabo...
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ISBN:
(数字)9798331512194
ISBN:
(纸本)9798331512200
Within the scope of this work, a format for a knowledge base of typical and atypical interaction scenarios between objects in a shared workspace is presented. The most complex class of human-robot interaction, collaboration, has been chosen for investigation. It is demonstrated that to achieve high safety performance in collaborative robotic technological processes, it is advisable to integrate multiple sensor systems into a single complex. Given that this approach places a high demand on computational systems, a set of undesirable (atypical) scenarios of the technological process has been identified, and their mathematical and functional descriptions have been provided. Based on the synthesized functions, a discrete-event model has been developed, visually illustrating the changes in the states of the collaborative technological process and enabling the identification of the cause of an undesirable situation.
Improving the control methods of rare earth production lines is an important measure to improve the automation level of our country's rare earth industry. In order to improve the automation of the calcium saponifi...
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Tendon-driven with bending tip (TDBT) mechanisms were extensively applied in the field of surgery and rescue robotics. TDBT has exposed many complicated characteristics, such as complex hysteresis, motion backlash and...
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ISBN:
(纸本)9781665431538
Tendon-driven with bending tip (TDBT) mechanisms were extensively applied in the field of surgery and rescue robotics. TDBT has exposed many complicated characteristics, such as complex hysteresis, motion backlash and rate-dependent behavior, etc., which bring great challenges to the modeling and control subject of the bending angle. In this research, the TDBT's dynamics model was established with the Euler-Lagrange scheme. Then, a robust adaptive control strategy was proposed to achieve satisfactory tracking performance. Finally, both the Lyapunov-based technique and numerical simulation results were provided to validate the effectiveness of the proposed control method for TDBT systems.
modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging modelingproblems. An off-road vehicle...
modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging modelingproblems. An off-road vehicle encounters highly complex and difficult-to-model terrain/vehicle interactions, as well as having complex vehicle dynamics of its own. These complexities can create challenges for effective high-speed control and planning. In this paper, we introduce a framework for multistep dynamics prediction that explicitly handles the accumulation of modeling error and remains scalable for sampling-based controllers. Our method uses a specially-initialized Long Short-Term Memory (LSTM) over a limited time horizon as the learned component in a hybrid model to predict the dynamics of a 4-person seating all-terrain vehicle (Polaris S4 1000 RZR) in two distinct environments. By only having the LSTM predict over a fixed time horizon, we negate the need for long term stability that is often a challenge when training recurrent neural networks. Our framework is flexible as it only requires odometry information for labels. Through extensive experimentation, we show that our method is able to predict millions of possible trajectories in real-time, with a time horizon of five seconds in challenging off road driving scenarios.
This paper presents a novel Q-learning algorithm to address the optimal load frequency control (LFC) problem in a single-area power system with unknown parameters. LFC is a critical issue for ensuring the stability an...
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ISBN:
(数字)9798331504755
ISBN:
(纸本)9798331504762
This paper presents a novel Q-learning algorithm to address the optimal load frequency control (LFC) problem in a single-area power system with unknown parameters. LFC is a critical issue for ensuring the stability and reliability of power systems. Traditional control methods often rely on precise mathematical models of the system, which are challenging to obtain or continuously changing in practical applications. To overcome this challenge, we design an adaptive Q-learning con-trol algorithm. This algorithm continuously learns and optimizes control strategies through interactions between the agent and the power system environment, achieving efficient and stable frequency control of the system. Simulation results demonstrate that the proposed Q-learning algorithm effectively maintains system frequency stability under conditions of parameter un-certainty, proving its applicability and effectiveness in solving LFC problems in power systems.
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