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检索条件"机构=Dept. of Automatic Control and Systems Analysis"

共 503 条记录，以下是1-10 订阅

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Comparative analysis of Optimal Path Planning Algorithms Using Deep Learning Technique

Comparative Analysis of Optimal Path Planning Algorithms Usi...

引用

2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Diyachuk, Anna Systems Analysis and Control Dept. Moscow Russia

ISBN: (纸本)9798350376739

In this paper effective algorithms of optimal route planning in the presence of threat zones and uncertainties with the use of deep learning technology are considered. At the present time, in theory, there are a huge number of algorithms to determine the shortest route, which are widely used to create optimal aircrafts pathways in order to achieve their required destination. A comparative analysis of two investigated methods is carried out: A* (A-star) and Dijkstra's algorithm. The discussed algorithms are compared according to two criteria: the length of the shortest trajectory found and searching steps count. The analytical and graphical results of the comparison are presented. Because of the artificial intellect development, there are many possibilities for applying deep learning methods to build optimal path planning algorithms and methodology in the future. Conclusions about considered methods advantages and their application field have been made according to the performed estimation. © 2024 IEEE.

关键词： Contrastive Learning

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Synthesis of Neural Network Program control Based on Pontryagin's Maximum Principle 8

Synthesis of Neural Network Program Control Based on Pontrya...

引用

8th International Conference on Information, control, and Communication Technologies, ICCT 2024

作者： Kozyr, Andrey Feofilov, Sergey Khapkin, Dmitry Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798331517564

This paper solves the problem of synthesizing optimal control for complex nonlinear dynamic objects. Such problems often arise in various engineering applications such as robotics, mechatronics, and aerospace. Examples of such problems include the computation of the optimal flight path of an aircraft, manipulator control, and control of industrial applications. In most real-world applications, the control object is a high-order nonlinear dynamical system, and the parameters may be uncertain or vary over *** and iterative methods of optimal control synthesis are known;however, such methods are not applicable in the case of high dimensionality of the object "curse of dimensionality"and have poor convergence. In this paper, a new approach to solving the problem of program control synthesis based on the use of neural network controller is proposed. The loss function is formulated based on an adapted set of necessary optimality conditions derived from the maximum principle and approximation of the Hamilton-Jacobi-Bellman equation using neural networks. Compared to direct iterative methods, the method presented in this paper can efficiently obtain control from the state-space domain. The demonstration of the proposed method is carried out on a model example of constructing the optimal control of the output to the unstable position of the inverse pendulum, and the results of numerical simulation are given. The research was carried out at the expense of the grant of the Russian Science Foundation No. 23-29-00609, https://***/project/23-29-00609. © 2024 IEEE.

关键词： Flight paths

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Speech Emotion Recognition Using Deep Learning 27

Speech Emotion Recognition Using Deep Learning

引用

27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Gismelbari, Mohamed A. Vixnin, Ilya I. Kovalev, Gregory M. Gogolev, Eugene E. Saint Petersburg Electrotechnical University 'LETI' Dept. of Automatic Control Systems Saint Petersburg Russia

ISBN: (纸本)9798350363708

This study explores the application of deep learning techniques in recognizing emotional states from spoken language. Specifically, we employ Convolutional Neural Networks (CNNs) and the HuBERT model to analyze the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Our findings suggest that deep learning models, particularly the HuBERT model, exhibit significant potential in accurately identifying speech emotions. The models were trained and tested on a dataset containing various emotional expressions, including happiness, sadness, anger, and fear, among others. The experimentation involved preprocessing the audio data, feature extraction using Mel Frequency Cepstral Coefficients (MFCCs), and implementing deep learning architectures for emotion classification. The HuBERT model, with its advanced self-supervised learning mechanism, outperformed traditional CNNs in terms of accuracy and efficiency. This research highlights the importance of selecting appropriate deep learning models and feature sets for the task of speech emotion recognition. Our analysis demonstrates that the HuBERT model, by leveraging contextual information and temporal dynamics in speech, offers a promising approach for developing more sensitive and accurate SER systems. These systems have potential applications in various fields, including mental health assessment, interactive voice response systems, and educational software, by enabling machines to understand and respond to human emotions more effectively. The findings of this study contribute to the ongoing discussion in the field of artificial intelligence about the best practices for implementing deep learning techniques in speech processing tasks. © 2024 IEEE.

关键词： Convolution

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Features of Synthesis of Neural Network Simulators with Limiters 5

Features of Synthesis of Neural Network Simulators with Limi...

引用

5th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Feofilov, Dmitry Khapkin, Dmitry Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350309126

One of the problems of the modern theory of automatic control is the construction of verified mathematical models of objects of regulation. In some cases, obtaining such models is not possible. This paper discusses approaches to the use of neural networks as regulators. Due to the presence of the adaptivity property, neuroregulators in some cases show a better quality of control in comparison with classical ones. The most common architectures of neural networks and methods of their training are considered. A comparative analysis was carried out. The first stage in the design of neural network regulators is the creation of a neuroimitator. An example of the synthesis of a neural network simulator is given. The proposed scheme has been worked out on systems with saturation type limiters and a mechanical stop. © 2023 IEEE.

关键词： Recurrent neural networks

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Synthesis of Neural Network Compensation of an Autonomous Electro-Hydraulic Actuator Based on a Sliding Mode control 7

Synthesis of Neural Network Compensation of an Autonomous El...

引用

7th International Conference on Information, control, and Communication Technologies, ICCT 2023

作者： Kozyr, Andrey Feofilov, Sergey Khapkin, Dmitry Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350340945

Autonomous electro-hydraulic actuators (AEHA) are widely used in industrial applications such as mobile manipulators, robotic exoskeleton, aerospace applications. AEHA has a number of significant advantages in relation to the electric drive: a high ratio of the developed force to the weight of the drive, compact dimensions of the autonomous drive, which are provided by the integrated layout of all elements of the electro-hydraulic system. However, the control of such systems becomes much more complicated due to the presence of significant nonlinearities in the control object. These include the non-linear characteristic of the switchgear, dry friction, and hard mechanical stops. All this makes it difficult to implement a control system based on classical controllers. The paper proposes a technique for synthesizing a controller that provides a sliding mode with compensatory neural network control for objects with an indefinite static characteristic. Using the direct Lyapunov method, we prove the stability of a feedback system. Numerical simulation results are presented to demonstrate the effectiveness of the proposed approaches. The study was supported by the Russian Science Foundation grant No. 23-29-00609, https://***/project/23-29-00609 © 2023 IEEE.

关键词： Lyapunov functions

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The Root-Mean-Square Velocity Stabilization of Swarm Multicopters Interacted by the Thermal Motion Equivalent Method 7

The Root-Mean-Square Velocity Stabilization of Swarm Multico...

引用

7th International Conference on Information, control, and Communication Technologies, ICCT 2023

作者： Heiss, Ed Kozyr, Andrey Morozov, Oleg Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350340945

The paper considers the problem of designing a regulator for a multicopter agent. Swarm control is organized by the thermal motion equivalent method. The idea of the method is the organization of the swarm agents' behavioral similarity to the atoms' thermal motion. This method makes it quite easy to organize the swarm agents' interaction and effectively solve problems: exploration issue in unknown environment, formation, tracking an object, mutual positioning of swarm agents. The key problem with the thermal motion equivalent method is the stabilization of the multicopter RMS velocity over time. Spontaneous growth or decrease in the agents' speed can lead to loss of swarm system controllability and mission failure. The paper proposes to synthesize a control system based on a linearized quadcopter model, which provides positional test oscillation stability of the aircraft to solve the above problem. The static state controller synthesized by the LMI method is used. Nonlinear model motion numerical simulation of a single UAV in a closed space is carried out in the paper. The results show the undamped multicopter oscillations. Multiple quadcopters are simulated in a confined space. The results show that the RMS agents' velocity does not change over time, which was required. © 2023 IEEE.

关键词： Stabilization

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Thermal Motion Equivalent Method Application for Multicopter Swarm in Exploration Task 7

Thermal Motion Equivalent Method Application for Multicopter...

引用

7th International Conference on Information, control, and Communication Technologies, ICCT 2023

作者： Heiss, Ed Morozov, Oleg Kozyr, Andrey Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350340945

The paper investigates the exploration issue by a swarm of quadrocopters. The thermal motion equivalent method is used for agents moving. The solution evaluation criterion is the swarm exploration time of more than 95% of an area square in case of limited inter-agent communication and partial agents' failure (lost). For comparison, a well-known rational-formed solution (called far as trivial) to the exploration issue is given. It consists of each agent disjoint trajectories planning at the swarm launch time. The trajectories' structure takes into account the agent field of view and covers the entire territory. The paper presents a mathematical model of the quadrocopter dynamics and thermal motion equivalent method. The exploration efficiency by a swarm using the thermal motion method depends, among other things, on the initial velocity vector direction. The paper presents the results of swarm simulation. The thermal motion equivalent method generally increases the exploration efficiency in conditions of limited communication and a decrease in the swarm number. However, in some cases, the exploration time is higher than in a trivial solution. © 2023 IEEE.

关键词： Efficiency

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Synthesis of Neural Network Stabilizing control for Objects with Mechanical Limiter 5

Synthesis of Neural Network Stabilizing Control for Objects ...

引用

5th International Conference on control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Kozyr, Andrey Feofilov, Sergey Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350309126

The article presents a new approach to creating a neural network simulator of a control object with a mechanical limiter, which allows you to identify the parameters of the limiter in real time. It is shown that the most effective way to identify such dynamic systems is to use neural networks based on ReLU activation functions. The effectiveness of the developed method is demonstrated by the example of the synthesis of stabilizing control for an aerodynamic pendulum using neural network predictive control. © 2023 IEEE.

关键词： Chemical activation

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Method for the Synthesis of Stable Autonomous Neural Network control systems for Objects with Limiters 7

Method for the Synthesis of Stable Autonomous Neural Network...

引用

7th International Conference on Information, control, and Communication Technologies, ICCT 2023

作者： Khapkin, Dmitry Feofilov, Sergey Kozyr, Andrey Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350340945

Breakthroughs in artificial intelligence and deep learning have had an impact not only on image and text processing, but also on robotics. Artificial neural networks (ANNs) are now used in automatic control systems. However, systems with a neural network controller lack theoretical guarantees of stability. Classical methods usually allow to obtain a controller with a proof of closed-loop stability for linear and linearized control objects. The proposed method allows to synthesize a neurocontroller that provides guaranteed asymptotic stability in the local region of phase space for objects that have constraints of various kinds. Also for the synthesized control system it is important the transition time, which meets the specified requirements. For this purpose, its optimization in terms of control time is provided by introducing an additional criterion. To guarantee the stability, an ANN-based Lyapunov function is synthesized together with the neurocontroller. Mixed Integer Programming (MIP) is used to confirm that the Lyapunov function satisfies the stability conditions. The result of solving the MIP problem either confirms that the stability conditions are met or is used to train the controller and Lyapunov function. As an example, the developed method was used to synthesize a tracking control system for a hydraulic drive. The obtained neural network system is compared by the tracking error with the linear-quadratic regulator (LQR). © 2023 IEEE.

关键词： Lyapunov functions

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Evaluation of Convergence of Neural Network Regulators Training Methods 7

Evaluation of Convergence of Neural Network Regulators Train...

引用

7th International Conference on Information, control, and Communication Technologies, ICCT 2023

作者： Feofilov, Dmitry Tula State University Dept. of Automatic Control Systems Tula Russia

ISBN: (纸本)9798350340945

Neural network controllers are a promising replacement for classical controls in closed tracking systems, but for the correct operation of an artificial neural network (ANN) in this capacity, its correct configuration is necessary. One of the most frequently used algorithms for training neural networks is the method of error back propagation, which is used when updating the weights of a multilayer neural network. This paper presents recommendations for analyzing the convergence of the error back propagation method in the training of artificial neural networks. The algorithm is considered as a variation of gradient descent, as a result of which the approaches used to analyze the convergence of numerical iterative methods are used. The final expression for evaluating the possibility of successful training of neural networks by the method under consideration is obtained, recommendations are given to increase the probability of convergence of the learning process, the most frequent problems that arise when configuring the ANN are indicated. © 2023 IEEE.

关键词： Errors

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