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Data Driven Control and Learning Systems (DDCLS)

作者： Yihong Lin Peng He Haiying Wan Zhuangyu Liu Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper presents a model-free $H_{2}/H_{\infty}$ Q-learning predictive control strategy for linear discrete-time systems. To design predictive controller with the system measured states, a policy iteration solution algorithm is employed to approximate the control inputs. Specifically, the developed algorithm is formulated in the form of linear matrix inequalities, designed to stabilize the system with mixed $H_{2}/H_{\infty}$ control performance. Additionally, to improve robust performance under system disturbance variations, we introduce the receding horizon optimization into the Q-learning based predictive control. Finally, simulation results demonstrate the effectiveness of the proposed approach.

关键词： Discrete-time systems Q-learning Simulation Heuristic algorithms Predictive models Prediction algorithms approximation algorithms

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MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA

MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical ...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Bao Bach Jose Falla Ilya Safro Computer and Information Sciences Quantum Science and Engineering University of Delaware Newark DE USA Physics and Astronomy University of Delaware Newark DE USA Computer and Information Sciences Physics and Astronomy University of Delaware Newark DE USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Learning the problem structure at multiple levels of coarseness to inform the decomposition-based hybrid quantum-classical combinatorial optimization solvers is a promising approach to scaling up variational approaches. We introduce a multilevel algorithm reinforced with the spectral graph representation learning-based accelerator to tackle large-scale graph maximum cut instances and fused with several versions of the quantum approximate optimization algorithm (QAOA) and QAOA-inspired algorithms. The graph representation learning model utilizes the idea of QAOA variational parameters concentration and substantially improves the performance of QAOA. We demonstrate the potential of using multilevel QAOA and representation learning-based approaches on extensive graphs by achieving high-quality solutions much faster. Reproducibility: Our source code and results are available at https://***/bachbao/MLQAOA.

关键词： Representation learning Quantum computing Source coding approximation algorithms Reproducibility of results Optimization

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Active RIS-aided Uplink for Robust and Secure Multi-User Private Industrial Network

Active RIS-aided Uplink for Robust and Secure Multi-User Pri...

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IEEE Conference on Vehicular Technology (VTC)

作者： Sandeep Singh Allu Raviteja Keshav Singh Sandeep Kumar Singh Aryan Kaushik Meng-Lin Ku Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of ECE Motilal Nehru National Institute of Technology Allahabad India School of Engineering and Informatics University of Sussex Brighton BN1 UK Department of Communication Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this work, we investigate the performance of an active reconfigurable intelligent surface (RIS)-aided multi-user uplink secure private industrial network. With an aim to provide a more sophisticated and consolidated framework towards the robust transmission design, we formulate a sum secrecy rate maximization while ensuring a minimum performance at each user within available resource constraints considering the norm-bounded imperfect channel state information (CSI) at Eavesdropper (Eve). To tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the equalizer, beamforming at the RIS, and power allocation at each user. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of active RIS, compared to passive RIS, towards a robust uplink secure private network is demonstrated. Finally, we discuss the impact of key parameters such as maximum power budget at each user and RIS, and CSI error on the secrecy performance of the considered network.

关键词： Vehicular and wireless technologies Upper bound Array signal processing Equalizers Reconfigurable intelligent surfaces approximation algorithms Resource management Uplink Optimization Convergence

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Embedding Learning-based Optimal Controllers with Assured Safety

Embedding Learning-based Optimal Controllers with Assured Sa...

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IEEE Conference on Decision and Control

作者： Filippos Fotiadis George A. Rovithakis Kyriakos G. Vamvoudakis School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider an off-policy reinforcement learning algorithm that gathers input and state data from a nonlinear system, and uses them to approximate the infinite-horizon optimal control for that system. However, as this algorithm relies on neural networks, its convergence depends on restrictive assumptions regarding the underlying neural network structure. Moreover, the derived approximate optimal controller that it yields is stabilizing only within a compact set $\Omega$ of the state space, leading to significant issues of robustness and safety for real-world implementations. Motivated by this, to increase the robustness and safety guarantees of controllers obtained by off-policy reinforcement learning procedures, we combine them with a novel safety net. The safety net is minimally interfering, leaving the approximate optimal controller unaltered within the compact set $\Omega$ in which it is valid and stabilizing. On the other hand, the safety net interferes with the approximate optimal controller whenever the set $\Omega$ is violated, so as to guarantee the boundedness and integrity of the closed loop. Since the proposed net is model-agnostic yet learning-free, it provides, for the first time, hard guarantees of safety, established by rigorous theoretical analysis and subsequently verified in simulations.

关键词： Hands Analytical models Neural networks Optimal control Reinforcement learning approximation algorithms Robustness Safety Nonlinear systems Multi-agent systems

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Dynamic Consistent k-Center Clustering with Optimal Recourse

arXiv

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arXiv 2024年

作者： Forster, Sebastian Skarlatos, Antonis Department of Computer Science University of Salzburg Austria

Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a k-clustering problem? This question has received attention in recent years under the name consistent clustering. Previous works by Lattanzi and Vassilvitskii [ICLM’17] and Fichtenberger, Lattanzi, Norouzi-Fard, and Svensson [SODA’21] studied k-clustering objectives, including the k-center and the k-median objectives, under only point insertions. In this paper we study the k-center objective in the fully dynamic setting, where the update is either a point insertion or a point deletion. Before our work, Lącki Haeupler Grunau Rozhoň Jayaram [SODA’24] gave a deterministic fully dynamic constant-factor approximation algorithm for the k-center objective with worst-case recourse of 2 per point update (i.e., point insertion/point deletion). In this work, we prove that the k-center clustering problem admits optimal recourse bounds by developing a deterministic fully dynamic constant-factor approximation algorithm with worst-case recourse of 1 per point update. Moreover our algorithm performs simple choices based on light data structures, and thus is arguably more direct and faster than the previous one which uses a sophisticated combinatorial structure. Additionally to complete the picture, we develop a new deterministic decremental algorithm and a new deterministic incremental algorithm, both of which maintain a 6-approximate k-center solution with worst-case recourse of 1 per point update. Our incremental algorithm improves over the 8-approximation algorithm by Charikar, Chekuri, Feder, and Motwani [STOC’97]. Finally, we remark that since all three of our algorithms are deterministic, they work against an adaptive adversary. © 2024, CC BY.

关键词： approximation algorithms

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Underactuated USV Path Following Control Approach for Inland River Search and Rescue

Underactuated USV Path Following Control Approach for Inland...

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Chinese Control and Decision Conference, CCDC

作者： Yi-Ming Bai Yi-Qi Wang Zheng Wang Long-Wei Li School of Marine Eelectrical Engineering Dalian Maritime University Dalian China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

With the increasing importance and complexity of searching mission in inland river, it is a current research hot topic to require underactuated Unmanned Surface Vehicle (USV) to offset the large drift angle caused by the harsh environment, so as to accurately and efficiently arrive at the accident site to search and rescue people who fell into the water. In response to the problem, this article designs a Line-of-sight (LOS) guidance method based on the Extended State Observer (ESO) to accurately estimate the time-varying large sideslip angle, consequently, achieving the desired heading angle. Taking into account the presence of unknown time-varying disturbances and dynamic uncertainties, this paper introduces an estimator to improve the Radial Basis Function (RBF) neural network and designs an adaptive path-following controller. By employing a Radial basis function neural network that utilizes estimator-based error estimation, the approximation of unknown terms in the model is achieved, resulting in enhanced transient performance and robustness of the controller. Ultimately, the effectiveness and robustness of the algorithm are validated through stability analysis and comparative simulation experiments.

关键词： Adaptation models Adaptive systems Uncertainty approximation algorithms Robustness Stability analysis Rivers

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Quantile Slice Sampling

arXiv

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arXiv 2024年

作者： Heiner, Matthew J. Johnson, Samuel B. Christensen, Joshua R. Dahl, David B. Department of Statistics Brigham Young University ProvoUT United States

We propose and demonstrate an alternate, effective approach to simple slice sampling. Using the probability integral transform, we first generalize Neal’s shrinkage algorithm, standardizing the procedure to an automatic and universal starting point: the unit interval. This enables the introduction of approximate (pseudo-) targets through importance reweighting, a technique that has popularized elliptical slice sampling. Reasonably accurate pseudo-targets can boost sampler efficiency by requiring fewer rejections and by reducing target skewness. This strategy is effective when a natural, possibly crude, approximation to the target exists. Alternatively, obtaining a marginal pseudo-target from initial samples provides an intuitive and automatic tuning procedure. We consider two metrics for evaluating the quality of approximation;each can be used as a criterion to find an optimal pseudo-target or as an interpretable diagnostic. We examine performance of the proposed sampler relative to other popular, easily implemented MCMC samplers on standard targets in isolation, and as steps within a Gibbs sampler in a Bayesian modeling context. We extend the transformation method to multivariate slice samplers and demonstrate with a constrained state-space model for which a readily available forward-backward algorithm provides the target approximation. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Simultaneous Optimization of Placement Planning and Motion Planning for a Single Robotic Arm Using Genetic Algorithm

Simultaneous Optimization of Placement Planning and Motion P...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： T. Taniguchi T. Nishi Z. Liu T. Fujiwara Faculty of Environmental Life Natural Science and Technology Okayama University Okayama Japan

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

Industrial robots are commonly used in factories for efficient high-mix low-volume production. It is required to determine the optimal placement and posture of a robot arm to pick up and place a workpiece to minimize the total operation time. We propose an efficient optimization approach for solving the placement planning and motion planning problems of a six-axis robot arm using a genetic algorithm (GA). To obtain a feasible solution to the problem of minimizing the total operation time, the proposed method conducts placement planning using GA and motion planning using Robot Operation System (ROS) simulation with a Rapidly-exploring Random Tree Star (RRT*) for a six-axis robot arm. The performance of the proposed algorithm is compared with that of a conventional method that uses particle swarm optimization. The computational results show that the proposed algorithm can reduce approximately 26% of the motion planning time compared to the conventional method. The proposed method can provide a near-optimal layout and motion planning solution within a practical computing time.

关键词： Service robots Stars Manipulators approximation algorithms Production facilities Planning Particle swarm optimization Robots Optimization Genetic algorithms

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Overmind: Fast and Scalable Decentralized Learning Simulation for Mobile Environments

Overmind: Fast and Scalable Decentralized Learning Simulatio...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Sangsu Lee Haoxiang Yu Xi Zheng Christine Julien Department of Electrical and Computer Engineering University of Texas at Austin School of Computing Macquarie University

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Recent progress in decentralized machine learning provides a foundation for a future in which mobile devices collaboratively learn from the vast amount of crowdsourced data they produce without necessarily relying on a centralized server for coordination. However, testing novel decentralized learning algorithms requires many iterations that are very costly in terms of time and budget. We present Overmind, a fast simulation framework for testing the performance of decentralized learning algorithms in dynamic networks. The design of Overmind allows users to easily scale their scenarios in terms of both simulated nodes and the number of physical machines on which the simulation runs. Overmind supports an operational mode that expedites the simulation by approximating the interaction time between two nodes, which allows skipping events that are unlikely to happen (e.g, two nodes exchanging big model parameters on a connection that is maintained for a very short time period). Users can also configure Overmind with a dataset of connection traces of simulated nodes, which is especially helpful to test algorithms given realistic dynamic networks in which devices join and leave unpredictably. To leverage Overmind, users just need to implement the generic behavior that is invoked on participating nodes when they connect to or disconnect from other nodes. The implemented behaviors are invoked in Overmind according to a provided encounter trace. This node-centric programming model that Overmind provides, where users define node behavior based on an individual node's perspective, is an intuitive design for decentralized settings. In this paper, we discuss the design of Overmind and demonstrate that it enables fast, scalable, and easily configurable decentralized learning simulations.

关键词： Performance evaluation Machine learning algorithms Heuristic algorithms Machine learning Programming Smart systems approximation algorithms Mobile handsets Servers Testing

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Scalable Reinforcement Learning for Linear-Quadratic Control of Networks

Scalable Reinforcement Learning for Linear-Quadratic Control...

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American Control Conference (ACC)

作者： Johan Olsson Runyu Cathy Zhang Emma Tegling Na Li The Department of Automatic Control and the ELLIIT Strategic Research Area Lund University Sweden The John A. Paulson School of Engineering and Applied Sciences Harvard University MA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algorithm for learning a distributed controller. Recent work has shown that the optimal controller can be well approximated only using information from a kq -neighborhood of each agent. Motivated by these results, we show that similar results hold for the agents' individual value and Q-functions. We continue by designing an algorithm, based on the actor-critic framework, to learn distributed controllers only using local information. Specifically, the Q-function is estimated by modifying the Least Squares Temporal Difference for Q-functions method to only use local information. The algorithm then updates the policy using gradient descent. Finally, we evaluate the algorithm through simulations that indeed suggest near-optimal performance.

关键词： State feedback Regulators Costs Heuristic algorithms Optimal control Reinforcement learning approximation algorithms

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