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检索条件"任意字段=Conference on Complexity and Nonlinear Dynamics"

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A Modelling Method for All-Axle Steered Multi-Articulated Vehicles

A Modelling Method for All-Axle Steered Multi-Articulated Ve...

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UKACC International conference on Control (CONTROL)

作者： Xingguo Li Chao Liang Timothy Gordon Chengbin Hou Maozhen Luo Zhanyi Shu Jun Wang CRRC Qingdao Sifang Co. Ltd. Qingdao P. R. China Department of Control Science and Engineering Tongji University Shanghai P. R. China School of Engineering University of Lincoln Lincoln UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

For a complex system like a multi-articulated all-axle steering vehicle, an accurate and physically meaningful dynamic model is crucial for analyzing vehicle characteristics and designing controllers. This paper proposes a dynamic modelling method for multi-articulated vehicles based on the principles of Lagrangian dynamics. This method effectively balances the complexity and precision of the model. Firstly, a set of clear symbol representations is constructed based on the actual physical meanings, standardising the modelling process. Secondly, nonlinear dynamic equations are constructed using Lagrange's equations, the principle of virtual work, and corresponding actuator models. Finally, through a series of transformations and simplifications, the vehicle dynamic model is obtained in matrix form. The model is validated by comparison with the model built under TruckMaker and collaborative simulation with controller.

关键词： Damping Transmission line matrix methods Symbols Process control Aerodynamics Mathematical models nonlinear dynamical systems

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Cardiac Anomaly Detection using Embedded Attractors Reconstructed from Multichannel ECG 22

Cardiac Anomaly Detection using Embedded Attractors Reconstr...

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Proceedings of the 2022 9th International conference on Biomedical and Bioinformatics Engineering

作者： Basab Bijoy Purkayastha Anakhi Hazarika Amal Thomas Soumyajit Poddar Electronics and Communication Engineering Indian Institute of Information Technology Guwahati India and Physics Indian Institute of Technology Guwahati India Electronics and Communication Engineering Indian Institute of Information Technology Guwahati India

ISBN: (纸本)9781450397223

The degree of complexity associated with the dynamics of the human heart is different for different categories of cardiac abnormalities. A quantitative measure of the complexities associated with cardiac dynamics can be obtained with a nonlinear study like a Multifractal analysis of the electrocardiogram signals. Here in this work, we have modeled the heart as a nonlinear dynamical system and obtained its phase space structure or embedded attractor reconstructed out of the ECG time series corresponding to four channels. Then, studied the multifractal behavior of the embedded attractor in order to quantify the degree of complexities associated with the dynamics of the heart. In particular, we have derived a few parameters out of the multifractal singularity spectrum and used them as a discriminative measure between various kinds of cardiac conditions. We have demonstrated a gradient boosting-based parametric classification model to discriminate between different kinds of arrhythmias and normal sinus rhythms. This work achieves considerably high accuracy in classifying different variants of cardiac disorders when trained with the parameters obtained from the multifractal singularity spectrum associated with the embedded attractor reconstructed out of the ECG time series.

关键词： nonlinear dynamical system Cardiac dynamics Multifractal singularity spectrum Gradient boosting algorithm Embedded attractor Phase space structure

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Optimal Model Order Reduction Based on Hybridization of Adaptive Safe Experimentation dynamics-nonlinear Sine Cosine Algorithm

Optimal Model Order Reduction Based on Hybridization of Adap...

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International conference on System Science and Engineering (ICSSE)

作者： Mohd Helmi Suid Mohd Ashraf Ahmad Salmiah Ahmad Mohd Riduwan Ghazali Zaidi Mohd Tumari Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia Kuliyyah of Engineering International Islamic University Malaysia Gombak Malaysia Faculty of Electrical & Electronics Engineering Technology Universiti Teknikal Malaysia Melaka Ayer Keroh Malaysia

Convoluted high-order structures as modeled through mathematical principle including telecommunication systems, power plants for urbanized energy supply and aerospace systems are often accompanied by the apparent setbacks in analyzing, experimentation and operational control. The complexity of such structures is proposedly decreased within the current study through introduction of a hybridized meta-heuristics fine-tuning approach between Adaptive Safe Experimentation dynamics (ASED) and nonlinear Sine Cosine Algorithm (NSCA). Entrapment within the local optima is hereby overcome through ASED by adaptive random perturbation, with improved exploration and exploitation of the introduced approach being further enabled by NSCA. The method’s potency was evaluated through an empirically adopted 6th order numerical function. Experimentation outcomes uncovered profound robustness and consistency from ASED-NSCA against alternative modern optimization-based techniques towards comparatively outstanding model order reduction (MOR).

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RLIDT: A Novel Reinforcement Learning-Infused Deep Transformer Model for Multivariate Electricity Load Forecasting

RLIDT: A Novel Reinforcement Learning-Infused Deep Transform...

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International conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Seyed Mohammad Jafar Jalali Fateme Fahiman Mahdi Khodayar School of Computer Science Edith Cowan University Joondalup Australia Fateme Fahiman School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Computer Science University of Tulsa Tulsa Oklahoma United States

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Electricity load forecasting plays a crucial role in the management of electricity power grids, enhancing operational efficiency, ensuring network reliability, facilitating infrastructure planning, and promoting energy sustainability As the complexity of energy consumption patterns increases traditional forecasting techniques struggle to accommodate the intricate and nonlinear temporal dynamics characteristics present within the data. This paper introduces a novel hybrid model called RLIDT that merges reinforcement learning (RL) with the deep transformer architecture to address the complicated challenges associated with load forecasting effectively. The integration of RL for hyperparameter optimization within the transformer framework not only utilizes their respective advantages but also provides a dynamic, adaptive model that exhibits versatility, robustness, and enhanced predictive accuracy through continuous learning. The experimental investigations conducted on real-world datasets have clearly demonstrated the remarkable advantage of the proposed RLIDT model when compared to traditional methods.

关键词： Adaptation models Load forecasting Electricity Computational modeling Power system dynamics Predictive models Transformers

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A Sensitivity Analysis of Symbolic dynamics indices for Fetal Heart Rate Monitoring 10

A Sensitivity Analysis of Symbolic Dynamics indices for Feta...

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10th E-Health and Bioengineering conference, EHB 2022

作者： Ponsiglione, Alfonso Maria Donisi, Leandro Adamo, Sarah Tedesco, Annarita Ricciardi, Carlo Romano, Maria Amato, Francesco Department of Electrical Engineering and Information Technology University of "Naples Federico II" Naples Italy Istituti Clinici Scientifici ICS Maugeri Pavia Italy University of Salento Department of Engineering for Innovation Lecce Italy

ISBN: (纸本)9781665485579

The assessment of fetal heart rate variability (FHRV) is fundamental for evaluating fetal well-being during pregnancy. Through cardiotocography (CTG), it is possible to monitor both the fetal heart rate (FHR) and the uterine contractions (UC) and, through the use og computerized analysis, it is also possible to extract synthetic parameters of the FHRV. Among the available FHRV indices, the use of symbolic dynamics (SD) arises as a promising nonlinear indicator to investigate the complexity in FHRV signals. However, there is not enough evidence of the reliability of such indices and no consensus on the proposed formula to compute them. Based on previous works on this topic, here we aim at carrying out a sensitivity analysis of one of the proposed SD parameters for FHRV analysis, namely the Variability Index (VI). A database of 583 CTG signals has been used as basis for this study. The signals were preprocessed and analyzed to extract the FHRV series and calculate the VI according to a predefined set of weights. Then, the weights have been systematically varied and the sensitivity of the obtained VI in discriminating different fetal conditions and signal characteristics was assessed by means of a statistical approach. The results obtained show that the choice of weights can be decisive for specific purposes, e.g. in evaluating fetal development and distinguishing cesarean from spontaneous sections. On the other hand, weights do not alter the VI sensitivity in discriminating between active and rest states, presence and absence of accelerations. © 2022 IEEE.

关键词： Sensitivity analysis

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GPU-Accelerated Full-Field Modelling of Highly Dispersive Ultrafast Optical Parametric Oscillators

GPU-Accelerated Full-Field Modelling of Highly Dispersive Ul...

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conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Sebastian C. Robarts Derryck T. Reid Richard A. McCracken Scottish Universities Physics Alliance (SUPA) Institute of Photonics and Quantum Sciences School of Engineering and Physical Sciences Heriot-Watt University Riccarton Edinburgh UK

ISBN: (纸本)9798350345995

Ultrafast synchronously-pumped optical parametric oscillators (OPOs) are broadband systems displaying complex dynamics and spectral structures which cannot be approximated using the classical, coupled wave equations. These systems are more readily described by a $\chi^complexity$ nonlinear envelope equation (NEE) [1], an analytical technique for studying ultra-broadband $\chi^complexity$ interactions. The NEE has shown good agreement with OPOs containing short $(\leq 1$ mm) nonlinear crystals operating in a low-dispersion regime [2], [3]; however, OPO cavities containing long crystals [4], significant material dispersion [5], or those pumped with highly chirped pulses, result in fields ex-hibiting much larger time-bandwidth products. Modelling such fields is computationally expensive due to the data required to maintain oversampling in large temporal/spectral windows (space complexity), compounded by the many round-trips needed for complex cavities to achieve steady-state (time complexity). Although evolution of the full field is inherently sequential, independent calculation of all sample points at each time step allows paral-lel processing to be leveraged for large data sets. Here we demonstrate GPU acceleration of full-field split-step Fourier methods (SSFM) through extreme parallelism, with an adaptive step-size algorithm, leading to more than three orders of magnitude improvement in computation time for long-crystal OPOs.

关键词： Dispersion Display technology Fourier transforms nonlinear optical crystals Optical parametric oscillators Periodically poled lithium niobate

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A Hybrid FNS Generator for Human Trunk Posture Control with Incomplete Knowledge of Neuromusculoskeletal dynamics

A Hybrid FNS Generator for Human Trunk Posture Control with ...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Xuefeng Bao Aidan R. Friederich Ronald J. Triolo Musa L. Audu College of Engineering & Applied Science University of Wisconsin-Milwaukee Milwaukee WI USA Department of Biomedical Engineering Case Western Reserve University Cleveland OH USA US Department of Veterans Affairs Advanced Platform Technology Center Cleveland OH USA

The trunk movements of an individual paralyzed by spinal cord injury (SCI) can be restored by Functional Neuromuscular Stimulation (FNS), a technique that applies low-level current to motor nerves to activate the muscles generating torques, and thus, produce trunk motions. FNS can be modulated to control trunk movements. However, a stabilizing modulation policy (i.e., control law) is difficult to derive due to the complexity of neuromusculoskeletal dynamics, which consist of skeletal dynamics (i.e., multi-joint rigid body dynamics) and neuromuscular dynamics (i.e., a highly nonlinear, non-autonomous, and input redundant dynamics). Therefore, an FNS-based control method that can stabilize the trunk without knowing the accurate skeletal and neuromuscular dynamics is desired. This work proposed an FNS generator, which consists of a robust nonlinear controller (RNC) that provides stabilizing torque command and an artificial neural network (ANN)-based torque-to-activation (T-A) map to ensure that the muscle generates the stabilizing torque to the skeleton. Due to the robustness and learning capability of this control framework, full knowledge of the trunk neuromusculoskeletal dynamics is not required. The proposed control framework has been tested in a simulation environment where an anatomically realistic 3D musculoskeletal model of the human trunk was manipulated to follow a time-varying reference that moves in the anterior-posterior and medial-lateral directions. From the results, it can be seen that the trunk motion converges to a satisfactory trajectory while the ANN is being updated. The results suggest the potential of this control framework for trunk tracking tasks in a clinical application.

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Analysis of Oscillations in nonlinear Human-Operated Robotic System

Analysis of Oscillations in Nonlinear Human-Operated Robotic...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Boris Andrievsky Nikolay V. Kuznetsov Iuliia Zaitceva dep. Control of Complex Systems Institute of Problems in Mechanical Engineering of RAS dept. Applied Cybernetics St. Petersburg State University St. Petersburg Russia dep. Information and Control Systems Institute of Problems in Mechanical Engineering of RAS

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The development of automation is characterized by the increasing complexity of control tasks associated with various technical objects. However, this does not exclude the human element from the control process. Manual control has certain drawbacks that negatively impact the quality of management, primarily arising from the interaction between the operator and the nonlinear dynamics of the robot. This study examines a manual control system for a robotic manipulator, taking into account nonlinear constraints that may manifest as limitations on the power of the actuating mechanism or as part of a local regulator. The parameters of the operator's control actions exhibit a certain range due to individual characteristics of the human operator or the control technique employed. Additionally, signal transmission delays may be present in the control loop. Considering these system characteristics provides a more comprehensive understanding of control quality and allows for the establishment of acceptable control techniques for the human operator. Depending on the frequency of input disturbances, this work analyzes the closed-loop system comprising the operator and manipulator using methods based on harmonic linearization of nonlinearity and harmonic balance equations: locus of a perturbed relay system and harmonic stabilization. The results of calculations and simulations using these methods indicate that there exists a parameter space within which oscillations may arise. Thus, when designing manual control systems for robotic manipulators, it is essential to pay attention to the control techniques employed by the human operator and to overall delays in order to avoid undesirable operational modes of the system.

关键词： Training Service robots System performance Human factors Harmonic analysis Mathematical models Delays Relays Oscillators Remote control

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Foot Placement for Footpad Robots Based on Perceptive MPC

Foot Placement for Footpad Robots Based on Perceptive MPC

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IEEE International conference on Robotics and Biomimetics

作者： Junjie Qiang Dezhi Qin Yuyi Jia Xin Luo State Key Laboratory of Intelligent Manufacturing Equipment and Technology (iMET) School of Mechanical Science and Engineering (SMSE) Huazhong University of Science and Technology (HUST) Wuhan China iMET SMSE HUST Guangdong Intelligent Robotics Institute Dongguan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

For legged robots, achieving dynamic motion in challenging terrains requires precise foot positioning and careful planning of underactuated system dynamics. Compared to robots with point feet, those with surface feet face a more complex task in selecting landing areas, and the surface contact with the ground further complicates the dynamics due to its varying interaction forms. We propose an online perception-control framework that optimizes the robot's full-degree-of-freedom motion in real time based on perceived terrain information. To manage the complexity of surface contact, we employ a contact point model that simplifies the interaction between the foot and the ground into multiple discrete point contacts, which are treated as landing points. An elevation map is created, and plane segmentation is precomputed to select the most suitable landing plane. Inequality constraints are extracted as approximations of the selected plane region and integrated into a nonlinear model predictive controller. Simulation experiments on bipedal robots were conducted in various complex terrains, including slopes, stairs, and uneven surfaces resembling plum blossom piles. The results demonstrate that our framework effectively enhances the robot's adaptability to diverse terrains.

关键词： System dynamics Motion segmentation dynamics Stairs Predictive models Real-time systems Planning Robots Surface treatment Foot

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DeepReach: A Deep Learning Approach to High-Dimensional Reachability

DeepReach: A Deep Learning Approach to High-Dimensional Reac...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Bansal, Somil Tomlin, Claire J. Univ Southern Calif ECE Los Angeles CA 90007 USA Univ Calif Berkeley EECS Berkeley CA USA

ISBN: (纸本)9781728190778

Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical control systems. Its advantages include compatibility with general nonlinear system dynamics, formal treatment of bounded disturbances, and the ability to deal with state and input constraints. However, it involves solving a PDE, whose computational and memory complexity scales exponentially with respect to the number of state variables, limiting its direct use to small-scale systems. We propose DeepReach, a method that leverages new developments in sinusoidal networks to develop a neural PDE solver for high-dimensional reachability problems. The computational requirements of DeepReach do not scale directly with the state dimension, but rather with the complexity of the underlying reachable tube. DeepReach achieves comparable results to the state-of-the-art reachability methods, does not require any explicit supervision for the PDE solution, can easily handle external disturbances, adversarial inputs, and system constraints, and also provides a safety controller for the system. We demonstrate DeepReach on a 9D multi-vehicle collision problem, and a 10D narrow passage problem, motivated by autonomous driving applications.

关键词： Deep learning Limiting Handheld computers Control systems Safety complexity theory nonlinear dynamical systems

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