The proceedings contain 126 papers. The topics discussed include: data-driven prediction of polymer intrinsic viscosity with incomplete time series data;a joint order cost optimization model for multi-item spare parts...
ISBN:
(纸本)9798350335859
The proceedings contain 126 papers. The topics discussed include: data-driven prediction of polymer intrinsic viscosity with incomplete time series data;a joint order cost optimization model for multi-item spare parts manufacturing systems considering the requirement of support;ultimately bounded state estimation for discrete-time singularly perturbed complex networks under bit rate constraints;an expert data generation method for multi-agent cooperative planning method;robotic machining: status, challenges and future trends;an overview of path planning for autonomous robots in smart manufacturing;heuristic data-driven adaptive model predictive control strategy;ontology-based product modeling for disassembly sequence planning in remanufacturing;applying and exploring supervised contrastive regression for vision-based steering prediction;and procurement practices influencing availability of drugs at public healthcare facilities.
The integration of distributed generation (DG) sources - such as thermal power systems, fuel cells, wind turbines, battery energy storage systems, diesel engines, aqua electrolysers, and electric vehicles - has fundam...
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Marine Internet of Things (Marine IoT) has garnered increasing interest in monitoring oceanic environments. But establishing a Marine IoT system for observing and processing marine environmental data presents various ...
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This paper investigates and compares experimental techniques in irrigation systems, focusing on crucial factors such as soil fertility, moisture level, pH level, water availability, and temperature for efficient agric...
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When it comes to large-scale systems with hundreds of components, it is often impossible to create a centralized supervisor due to the state size explosion. The number of states in a modular system expands exponential...
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ISBN:
(数字)9781665495721
ISBN:
(纸本)9781665495721
When it comes to large-scale systems with hundreds of components, it is often impossible to create a centralized supervisor due to the state size explosion. The number of states in a modular system expands exponentially as the number of system components increases. As a result, the centralized supervisory control synthesis approach has a limitation;it may only be suitable for small systems. In this paper, we study the problem of distributed control of networked discrete event systems with communication delays, for both the control channel and the observation channel. communication channel amongst distributed supervisors is built on the finite-state automata, expanding the earlier plant automata translation method into the distributed networked control framework. A more flexible modeling method is developed by incorporating time temporal limitations. By a model transformation, we transform distributed networked supervisor synthesis problem into the (non-networked) distributed supervisor synthesis problem (for non-deterministic plants), then existing tools and algorithms can be used for synthesizing distributed networked supervisors.
Many communication technologies have developed along with the evolution of the internet over the last three decades. Transport layer protocol plays a major role in communication technologies used in both static and dy...
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Model Predictive control (MPC) is a state-of-the-art (SOTA) control technique which requires solving hard constrained optimization problems iteratively. For uncertain dynamics, analytical model based robust MPC impose...
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ISBN:
(纸本)9798350323658
Model Predictive control (MPC) is a state-of-the-art (SOTA) control technique which requires solving hard constrained optimization problems iteratively. For uncertain dynamics, analytical model based robust MPC imposes additional constraints, increasing the hardness of the problem. The problem exacerbates in performance-critical applications, when more compute is required in lesser time. Data-driven regression methods such as Neural Networks have been proposed in the past to approximate system dynamics. However, such models rely on high volumes of labeled data, in the absence of symbolic analytical priors. This incurs non-trivial training overheads. Physics-informed Neural Networks (PINNs) have gained traction for approximating non-linear system of ordinary differential equations (ODEs), with reasonable accuracy. In this work, we propose a Robust Adaptive MPC framework via PINNs (RAMP-Net), which uses a neural network trained partly from simple ODEs and partly from data. A physics loss is used to learn simple ODEs representing ideal dynamics. Having access to analytical functions inside the loss function acts as a regularizer, enforcing robust behavior for parametric uncertainties. On the other hand, a regular data loss is used for adapting to residual disturbances (non-parametric uncertainties), unaccounted during mathematical modelling. Experiments are performed in a simulated environment for trajectory tracking of a quadrotor. We report 7.8% to 43.2% and 8.04% to 61.5% reduction in tracking errors for speeds ranging from 0.5 to 1.75 m/s compared to two SOTA regression based MPC methods.
Memory plays a vital role in growth and development of any device or circuitry. Out of the prominent types of memory cells, MOS memory enables and facilitates various functions in any electronic circuit or device and ...
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In the era of Industry 4.0, the execution of intelligent industrial applications relies on collaborations among multi-type devices, such as sensing, computing, and control devices. Although a universal information mod...
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End-to-end driving behavior decision-making is a research hotspot in the field of driverless driving. This paper studies the end-to-end lane change decision control based on the PPO (Proximal Policy Optimization) deep...
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