The proceedings contain 19 papers. The special focus in this conference is on Difference Equations and Applications. The topics include: Discrete Dynamical systems in Economics: Two Seminal Models and Their ...
ISBN:
(纸本)9783031510489
The proceedings contain 19 papers. The special focus in this conference is on Difference Equations and Applications. The topics include: Discrete Dynamical systems in Economics: Two Seminal Models and Their Developments;on a Class of Applications for Difference Equations in Continuous Time;the Interplay Between Dispersal and Allee Effects in a Two-Patch Discrete-Time Model;krause Mean Processes Generated by Off-Diagonally Uniformly Positive Nonautonomous Stochastic Hyper-Matrices;passivity Techniques and Hamiltonian Structures in Discrete Time;Explicit MPC Solution Using Hasse Diagrams: Construction, Storage and Retrieval;tube Model Predictive control for Flexible Satellite Dynamics;numerical modeling and Some Optimal controlproblems of Dynamic systems Describing Contact problems with Friction in Elasticity;a Particular Solution for Higher Order Non-homogeneous Discrete Cauchy-Euler Equations;about a System of Piecewise Linear Difference Equations with Many Periodic Solutions;boundedness of Solutions of xn+1=an′+bn′ynCn′xn and yn+1=an+bnxn+cnynAn+Bnxn+Cnyn with Non-constant Coefficients;on the Dynamic Geometry of Kasner Polygons with complex Parameter;linear Time-Varying Dynamic-Algebraic Equations of Index One on Time Scales;differentiable Conjugacies for One-Dimensional Maps;topological Entropy of Generalized Bunimovich Stadium Billiards;global Manifolds of Saddle Periodic Orbits Parametrised by Isochrons;on Uniform Dichotomies for the Growth Rates of Linear Discrete-time Dynamical systems in Banach Spaces.
Learning from demonstration (LfD) has emerged as a promising approach enabling robots to acquire complex tasks directly from human demonstrations. However, tasks involving surface interactions on freeform 3D surfaces ...
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ISBN:
(纸本)9798350377712;9798350377705
Learning from demonstration (LfD) has emerged as a promising approach enabling robots to acquire complex tasks directly from human demonstrations. However, tasks involving surface interactions on freeform 3D surfaces present unique challenges in modeling and execution, especially when geometric variations exist between demonstrations and robot execution. This paper proposes a novel framework called probabilistic surface interaction primitives (ProSIP), which systematically incorporates the surface path and the local surface features into the learning procedure. An instrumented tool allows seamless recording and execution of human demonstrations. By design, ProSIPs are independent of time, invariant to rigid-body displacements, and apply to any robotic platform with a Cartesian controller. The framework is employed for an edge-cleaning task of bathroom sinks. The generalization capability to various object geometries and significantly distorted objects is demonstrated. Simulations and an experimental setup with a 9-degrees-of-freedom robotic platform confirm the performance.
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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Modern automation systems are extremely diverse. Traditional multilevel distributed systems of industrial and infrastructural automation, multi-operational manufacturing equipment, instrument automation and other segm...
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Most works on the analysis of risk management problems in complexsystems offer solutions based on the assumption of independence of risk-reducing control actions. At the same time, in practice, control actions on a p...
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Pneumatic artificial muscles (PAMs) have been introduced as actuators due to their low weight, low mass-toforce ratio, compliance, high prevalence in nature and ability to closely mimic the functions of human biologic...
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ISBN:
(纸本)9798350358513;9798350358520
Pneumatic artificial muscles (PAMs) have been introduced as actuators due to their low weight, low mass-toforce ratio, compliance, high prevalence in nature and ability to closely mimic the functions of human biological muscles. When PAMs are applied in robotics and rehabilitation applications, it is essential that the actuator is operated according to user requirements. PAMs, however, present significant challenges in modeling and control due to their time- varying parameters, complex hysteresis, and highly nonlinear properties. This paper proposes an approach for controlling the motion of a PAM. This method applies a hybrid control algorithm to control a fluiddriven origami-inspired artificial muscle (FOAM). By combining a PI controller with feed-forward neural network control, the controller can learn and adapt through the system's behavior. The control algorithm was tested to observe the performance of the controller for displacement control of FOAM via different signals. Additionally, experiments were conducted to evaluate its performance under different load conditions. The results demonstrate exceptional controllability, even when the system faces increased loads, demonstrating the adaptability of the controller to load variations.
Route choice modeling is an important issue in transportation planning. In recent years, the importance of comfortable urban spaces not only for vehicles but also for pedestrians has been increasing toward low-carbon ...
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ISBN:
(纸本)9798350399462
Route choice modeling is an important issue in transportation planning. In recent years, the importance of comfortable urban spaces not only for vehicles but also for pedestrians has been increasing toward low-carbon society. Accordingly, efforts have been made to model the pedestrians' characteristic behaviors. However, most of the existing studies about route choice modeling are limited to the representation within a single traffic mode. In a typical urban street space, each traffic mode is interacting with each other in a complex manner. Describing multimodal interaction is necessary to efficiently allocate the limited road space and to maximize the value of the road space as a result of the network effect. However, there are two challenges to describing the interaction of multiple transportation modes in a route choice model: the computational cost of model estimation and the limited expressive power of the model. In this study, we construct a route choice model based on the AIRL method which can rapidly estimate the user equilibrium under multi-agent Markov game and can take into account the interaction among multiple traffic modes explicitly. The use of highly expressive machine learning models is also expected to lead the high reproducibility of traffic behavior.
Standard method of hazard identification HAZOP has several limitations: impossibility to identify hazards involving interactions between different parts of complex system;experts’ experience can fail while evaluating...
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With the improvement of industrial automation level, higher requirements have been put forward for the control precision, response speed, and stability of complex mechanical systems. The article studied the applicatio...
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modeling the driving behavior of traffic participants in highly interactive traffic situations, such as roundabouts, poses a significant challenge due to the complex interactions and the variety of traffic situations....
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ISBN:
(纸本)9798350399462
modeling the driving behavior of traffic participants in highly interactive traffic situations, such as roundabouts, poses a significant challenge due to the complex interactions and the variety of traffic situations. To address this task, we propose a combination of graph-based representations of the environment with Multi-Agent Reinforcement Learning (MARL). By utilizing a graph-based representation of the local environment of each vehicle, our approach efficiently accounts for road structures and a varying number of surrounding vehicles interacting with each other. Building upon this representation, MARL enables us to learn a driving policy based on a minimal set of principles: drivers want to move along the road while avoiding collisions and maintaining comfortable accelerations. Sharing the learned policy among all agents allows us to leverage Proximal Policy Optimization (PPO), a policy gradient Reinforcement Learning (RL) algorithm. To evaluate our proposed model, we conduct experiments in a roundabout scenario from the INTERACTION dataset and compare it to a model learned via Behavior Cloning (BC). The results demonstrate that our proposed model is capable of maneuvering through dense traffic, indicating that our graph-based representation is well suitable for modeling and understanding complex road layouts and interactions between agents.
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