This paper discusses the optimal synchronisation problem of multi-agent systems with unknown system dynamics, where each agent is subject to both input saturation and external disturbances. A novel data-driven control...
详细信息
This paper discusses the optimal synchronisation problem of multi-agent systems with unknown system dynamics, where each agent is subject to both input saturation and external disturbances. A novel data-driven control approach is developed in this paper based on low-gain technique, output regulation, differential game theory, and adaptive dynamic programming (ADP). Unlike existing approaches to the data-driven optimal synchronisation problem, our method eliminates the need for an initially admissible stabilising control policy, and the proposed distributed control law ensures asymptotic tracking even in the presence of both modelling disturbances and unmodeled disturbances.
Under the background of dual-carbon, electric vehicle (EV) show a trend of large-scale popularization and promotion, and the disorderly charging behavior of EV cluster has a significant impact on the safe operation an...
详细信息
The development of network technology has laid a solid foundation for the network transmission of integrated media. The amount of information in integrated media is large, and it has the characteristics of real-time, ...
详细信息
This paper proposes an integrated quantum-classical approach that merges quantum computational dynamics with classical computing methodologies tailored to address control problems based on Pontryagin's minimum pri...
详细信息
ISBN:
(纸本)9798331541378
This paper proposes an integrated quantum-classical approach that merges quantum computational dynamics with classical computing methodologies tailored to address control problems based on Pontryagin's minimum principle within a Physics-Informed Neural Network (PINN) framework. By leveraging a dynamic quantum circuit that combines Gaussian and non-Gaussian gates, the study showcases an innovative approach to optimizing quantum state manipulations. The proposed hybrid model effectively applies machine learning techniques to solve optimal control problems. This is illustrated through the design and implementation of a hybrid PINN structure to solve a quantum state transition problem in a two and three-level system, highlighting its potential across various quantum computing applications.
As Multi-Robot systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space expl...
详细信息
ISBN:
(纸本)9798350377712;9798350377705
As Multi-Robot systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles. Note-Videos are provided in the supplementary material and also at https://***/lb-iros24.
As the number of people living in cities increases and more people drive, one of the most important problems is traffic congestion. An intelligent system that could effectively manage traffic congestion based on traff...
详细信息
In the automobile sector, Hybrid Electric Vehicles (HEVs) serve as a game-changer due to they provide a sustainable way to improve fuel efficiency and address environmental issues. Regenerative braking is a crucial co...
详细信息
A typical sense-plan-act robotics pipeline is brittle due to the inherent inaccuracies in the output of the sensing module and the lack of awareness of the planning module to those inaccuracies. This paper develops a ...
详细信息
ISBN:
(纸本)9798350377712;9798350377705
A typical sense-plan-act robotics pipeline is brittle due to the inherent inaccuracies in the output of the sensing module and the lack of awareness of the planning module to those inaccuracies. This paper develops a framework to predict uncertainty estimates for neural network-based vision models used for state estimation in robotics pipelines. Our uncertainty estimates are based directly on the image observation data and are explicitly trained to model the error distribution on a held-out calibration set. We also demonstrate how predicted uncertainties can be used to select robust control strategies. We conduct experiments on the mobile manipulation problem of articulating everyday objects (e.g. opening a cupboard) and demonstrate the quality of estimated uncertainty and its downstream impact on robustness of inferred control strategies.
Traffic signals, also known as traffic lights, are an essential tool for managing intersections. Improving traffic signal control can enhance the overall performance of transportation systems. Recently, reinforcement ...
详细信息
ISBN:
(纸本)9798350322811
Traffic signals, also known as traffic lights, are an essential tool for managing intersections. Improving traffic signal control can enhance the overall performance of transportation systems. Recently, reinforcement learning has shown promise in optimizing traffic signal control by utilizing high-resolution, real-time data from advanced traffic monitoring systems. However, creating a reinforcement learning-based control that is robust to all scenarios is challenging because the real world is a diverse, non-stationarity and open-ended environment. Therefore, a system that can continually monitor and evaluate the traffic signal control's operation is necessary for deploying such these controls. In this paper, we proposed a novel reinforcement learning-based traffic signal control system that supports near real-time performance assessment.
This paper investigates the over-the-air computation (AirComp) problem in a hybrid intelligent reflecting surface (IRS) and cell-free massive multiple-input multiple-output (CF-mMIMO) assisted digital twin system, whe...
详细信息
ISBN:
(纸本)9798350354720;9798350354713
This paper investigates the over-the-air computation (AirComp) problem in a hybrid intelligent reflecting surface (IRS) and cell-free massive multiple-input multiple-output (CF-mMIMO) assisted digital twin system, where multiple users offload their data to access points (APs) and central processing unit (CPU) via the IRS for data aggregation. We formulate a joint beamforming design, IRS phase shift optimization, and power allocation problem to minimize the mean squared error (MSE) of data aggregation. We solve the resultant non-convex optimization problem in three steps. First, we transform the original problem into two sub-problems. Then, we exploit a convex optimization framework to respectively determine the beamforming design, IRS phase shift optimization, and power allocation. Last, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.
暂无评论