pwc-archive/papers-with-abstracts huggingface dataset:
Provable Reinforcement Learning for Networked Control Systems with Stochastic Packet Disordering
Authors: Wenqian Xue, Yi Jiang, Frank L. Lewis, Bosen Lian
Date: 2023-12-05 00:00:00
Tasks: Q-Learning, reinforcement-learning

This paper formulates a stochastic optimal control problem for linear networked control systems featuring stochastic packet disordering with a unique stabilizing solution certified. The problem is solved by proposing reinforcement learning algorithms. A measurement method is first presented to deal with PD and calculate the newest control input. The NCSs with stochastic PD are modeled as stochastic NCSs. Then, given a cost function, a modified algebraic Riccati equation is derived within the formulation. We propose offline policy iteration and value iteration algorithms to solve the MARE associated with provable convergence. These two algorithms require knowledge of NCS dynamics and PD probabilities. To release that, we further design online model-free off-policy and Q-learning algorithms with an online estimation method for PD probability. Both model-free algorithms solve the optimal control problem using real-time system states, control inputs, and PD probability estimates. Simulation results verify the proposed formulation and algorithms at last.
Almost Exact Recovery in Gossip Opinion Dynamics over Stochastic Block Models
Authors: Yu Xing, Karl H. Johansson
Date: 2023-12-05 00:00:00
Tasks: Clustering, Community Detection

We study community detection based on state observations from gossip opinion dynamics over stochastic block models (SBM). It is assumed that a network is generated from a two-community SBM where each agent has a community label and each edge exists with probability depending on its endpoints' labels. A gossip process then evolves over the sampled network. We propose two algorithms to detect the communities out of a single trajectory of the process. It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities. That is, the algorithm can recover all but a vanishing part of community labels with high probability. In contrast, when the influence of stubborn agents is large, another algorithm based on clustering time average of agent states can achieve almost exact recovery. Numerical experiments are given for illustration of the two algorithms and the theoretical results of the paper.
Joint State and Sparse Input Estimation in Linear Dynamical Systems
Authors: Rupam Kalyan Chakraborty, Geethu Joseph, Chandra R. Murthy
Date: 2023-12-04 00:00:00

Sparsity constraints on the control inputs of a linear dynamical system naturally arise in several practical applications such as networked control, computer vision, seismic signal processing, and cyber-physical systems. In this work, we consider the problem of jointly estimating the states and sparse inputs of such systems from low-dimensional (compressive) measurements. Due to the low-dimensional measurements, conventional Kalman filtering and smoothing algorithms fail to accurately estimate the states and inputs. We present a Bayesian approach that exploits the input sparsity to significantly improve estimation accuracy. Sparsity in the input estimates is promoted by using different prior distributions on the input. We investigate two main approaches: regularizer-based MAP, and {Bayesian learning-based estimation}. We also extend the approaches to handle control inputs with common support and analyze the time and memory complexities of the presented algorithms. Finally, using numerical simulations, we show that our algorithms outperform the state-of-the-art methods in terms of accuracy and time/memory complexities, especially in the low-dimensional measurement regime.
Tuning of Online Feedback Optimization for setpoint tracking in centrifugal compressors
Authors: Marta Zagorowska, Lukas Ortmann, Alisa Rupenyan, Mehmet Mercangoez, Lars Imsland
Date: 2023-12-04 00:00:00

Online Feedback Optimization (OFO) controllers steer a system to its optimal operating point by treating optimization algorithms as auxiliary dynamic systems. Implementation of OFO controllers requires setting the parameters of the optimization algorithm that allows reaching convergence, posing a challenge because the convergence of the optimization algorithm is often decoupled from the performance of the controlled system. OFO controllers are also typically designed to ensure steady-state tracking by fixing the sampling time to be longer than the time constants of the system. In this paper, we first quantify the impact of OFO parameters and the sampling time on the tracking error and number of oscillations of the controlled system, showing that adjusting them without waiting for steady state allows good tracking. We then propose a tuning method for the sampling time of the OFO controller together with the parameters to allow tracking fast trajectories while reducing oscillations. We validate the proposed tuning approach in a pressure controller in a centrifugal compressor, tracking trajectories faster than the time needed to reach the steady state by the compressor. The results of the validation confirm that simultaneous tuning of the sampling time and the parameters of OFO yields up to 87% times better tracking performance than manual tuning based on steady state.
Consensus-Based Distributed Nonlinear Filtering with Kernel Mean Embedding
Authors: Liping Guo, Jimin Wang, Yanlong Zhao, Ji-Feng Zhang
Date: 2023-12-04 00:00:00

This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the system state is embedded into a higher-dimensional reproducing kernel Hilbert space (RKHS), and then the nonlinear measurement function is linearly converted. As a result, an update rule of KME of posterior distribution is established in the RKHS. To show the proposed distributed filter being capable of achieving the centralized estimation accuracy, a centralized filter, serving as an extension of the standard Kalman filter in the state space to the RKHS, is developed first. Benefited from the KME, the proposed distributed filter converges to the centralized one while maintaining the distributed pattern. Two examples are introduced to demonstrate the effectiveness of the developed filters in target tracking scenarios including nearly constantly moving target and turning target, respectively, with bearing-only, range and bearing measurements.
New method for SISO strong stabilization with advantages over existing methods
Authors: Abdul Hannan Faruqi, Anindya Chatterjee
Date: 2023-12-04 00:00:00

We address stabilization of linear time-invariant (LTI), single-input single-output (SISO) systems in the Laplace domain, with a stable controller in a single feedback loop. Such stabilization is called strong. Plants that satisfy a parity interlacing property are known to be strongly stabilizable. Finding such controllers is a well known difficult problem. Existing general methods are based on either manual search or a clever use of Nevanlinna-Pick interpolation with polynomials of possibly high integer order. Here we present a new, simple, and general method for strongly stabilizing systems of relative degree less than 3. We call our method Real to Integers (RTI). Our theoretical contributions constitute proposing the functional form used, which involves a product of several terms of the form $\displaystyle \left ( \frac{s+a}{s+b} \right )^m$, showing that real $m$'s will arise whenever the plant is strongly stabilizable, and proving that integer $m$'s can be obtained by continuously varying free parameters (i.e., the $a$'s and $b$'s). Our practical contributions include demonstrating a simple way, based on a trigonometric trick, to adjust the fractional powers until they take reasonable integer values. We include brief but necessary associated discussion to make the paper accessible to a broad audience. We also present ten numerical examples of successful control design with varying levels of difficulty, including plants whose transfer functions have relative degrees of 0, 1 or 2; and with right half plane zeros of multiplicity possibly exceeding one.
Closed-Form Solutions for Grid-Forming Converters: A Design-Oriented Study
Authors: Fangzhou Zhao, Tianhua Zhu, Lennart Harnefors, Bo Fan, Heng Wu, Zichao Zhou, Yin Sun, Xiongfei Wang
Date: 2023-12-04 00:00:00
Tasks: Form

This paper derives closed-form solutions for grid-forming converters with power synchronization control (PSC) by subtly simplifying and factorizing the complex closed-loop models. The solutions can offer clear analytical insights into control-loop interactions, enabling guidelines for robust controller design. It is proved that 1) the proportional gains of PSC and alternating voltage control (AVC) can introduce negative resistance, which aggravates synchronous resonance (SR) of power control, 2) the integral gain of AVC is the cause of sub-synchronous resonance (SSR) in stiff-grid interconnections, albeit the proportional gain of AVC can help dampen the SSR, and 3) surprisingly, the current controller that dampens SR actually exacerbates SSR. Controller design guidelines are given based on analytical insights. The findings are verified by simulations and experimental results.
Interference-Constrained Scheduling of a Cognitive Multi-hop Underwater Acoustic Network
Authors: Chen Peng, Urbashi Mitra
Date: 2023-12-04 00:00:00
Tasks: Scheduling

This paper investigates optimal scheduling for a cognitive multi-hop underwater acoustic network with a primary user interference constraint. The network consists of primary and secondary users, with multi-hop transmission adopted for both user types to provide reliable communications. Critical characteristics of underwater acoustic channels, including significant propagation delay, distance-and-frequency dependent attenuation, half-duplex modem, and inter-hop interference, are taken into account in the design and analysis. In particular, time-slot allocation is found to be more effective than frequency-slot allocation due to the underwater channel model. The goal of the network scheduling problem is to maximize the end-to-end throughput of the overall system while limiting the throughput loss of primary users. Both centralized and decentralized approaches are considered. Partially Observable Markov Decision Processes (POMDP) framework is applied to formulate the optimization problem, and an optimal dynamic programming algorithm is derived. However, the optimal dynamic programming solution is computationally intractable. Key properties are shown for the objective function, enabling the design of approximate schemes with significant complexity reduction. Numerical results show that the proposed schemes significantly increase system throughput while maintaining the primary throughput loss constraint. Under certain traffic conditions, the throughput gain over frequency-slot allocation schemes can be as high as 50%.
Koopman-based feedback design with stability guarantees
Authors: Robin Strässer, Manuel Schaller, Karl Worthmann, Julian Berberich, Frank Allgöwer
Date: 2023-12-03 00:00:00
Tasks: Scheduling

We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman-operator theory and uses robust control to explicitly account for approximation errors due to finitely many data samples. To simplify practical usage across various applications, we provide a tutorial-style exposition of the feedback design and its stability guarantees for single-input systems. Moreover, we extend this controller design to multi-input systems and more flexible nonlinear state-feedback controllers using gain-scheduling techniques to increase the guaranteed region of attraction. As the proposed controller design is framed as a semidefinite program, it allows for an efficient solution. Further, we enhance the geometry of the region of attraction through a heuristic algorithm that establishes a connection between the employed Koopman lifting and the dynamics of the system. Finally, we validate the proposed feedback design procedure by means of numerical examples.
OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation
Authors: Ruidi Qiu, Amro Eldebiky, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, Bing Li
Date: 2023-12-03 00:00:00
Tasks: Knowledge Distillation

Having the potential for high speed, high throughput, and low energy cost, optical neural networks (ONNs) have emerged as a promising candidate for accelerating deep learning tasks. In conventional ONNs, light amplitudes are modulated at the input and detected at the output. However, the light phases are still ignored in conventional structures, although they can also carry information for computing. To address this issue, in this paper, we propose a framework called OplixNet to compress the areas of ONNs by modulating input image data into the amplitudes and phase parts of light signals. The input and output parts of the ONNs are redesigned to make full use of both amplitude and phase information. Moreover, mutual learning across different ONN structures is introduced to maintain the accuracy. Experimental results demonstrate that the proposed framework significantly reduces the areas of ONNs with the accuracy within an acceptable range. For instance, 75.03% area is reduced with a 0.33% accuracy decrease on fully connected neural network (FCNN) and 74.88% area is reduced with a 2.38% accuracy decrease on ResNet-32.
Introducing Modelling, Analysis and Control of Three-Phase Electrical Systems Using Geometric Algebra
Authors: Manel Velasco, Isiah Zaplana, Arnau Dòria-Cerezo, Josué Duarte, Pau Martí
Date: 2023-12-03 00:00:00

State-of-the-art techniques for modeling, analysis and control of three-phase electrical systems belong to the real-valued multi-input/multi-output (MIMO) domain, or to the complex-valued nonlinear single-input/single-output (SISO) domain. In order to complement both domains while simplifying complexity and offering new analysis and design perspectives, this paper introduces the application of geometric algebra (GA) principles to the modeling, analysis and control of three-phase electrical systems. The key contribution for the modeling part is the identification of the transformation that allows transferring real-valued linear MIMO systems into GA-valued linear SISO representations (with independence of having a balanced or unbalanced system). Closed-loop stability analysis in the new space is addressed by using intrinsic properties of GA. In addition, a recipe for designing stabilizing and decoupling GA-valued controllers is provided. Numerical examples illustrate key developments and experiments corroborate the main findings.
A Literature Review on the Smart Wheelchair Systems
Authors: Yane Kim, Bharath Velamala, Youngseo Choi, Yujin Kim, Hyunkin Kim, Nishad Kulkarni, Eung-Joo Lee
Date: 2023-12-03 00:00:00
Tasks: Autonomous Navigation, Brain Computer Interface, Rhythm

This study offers an in-depth analysis of smart wheelchair (SW) systems, charting their progression from early developments to future innovations. It delves into various Brain-Computer Interface (BCI) systems, including mu rhythm, event-related potential, and steady-state visual evoked potential. The paper addresses challenges in signal categorization, proposing the sparse Bayesian extreme learning machine as an innovative solution. Additionally, it explores the integration of emotional states in BCI systems, the application of alternative control methods such as EMG-based systems, and the deployment of intelligent adaptive interfaces utilizing recurrent quantum neural networks. The study also covers advancements in autonomous navigation, assistance, and mapping, emphasizing their importance in SW systems. The human aspect of SW interaction receives considerable attention, specifically in terms of privacy, physiological factors, and the refinement of control mechanisms. The paper acknowledges the commercial challenges faced, like the limitations of indoor usage and the necessity for user training. For future applications, the research explores the potential of autonomous systems adept at adapting to changing environments and user needs. This exploration includes reinforcement learning and various control methods, such as eye and voice control, to improve adaptability and interaction. The potential integration with smart home technologies, including advanced features such as robotic arms, is also considered, aiming to further enhance user accessibility and independence. Ultimately, this study seeks to provide a thorough overview of SW systems, presenting extensive research to detail their historical evolution, current state, and future prospects.
Vehicle path and traffic flow optimization via lane changing of automated or semi-automated vehicles on motorways
Authors: Antonios Georgantas
Date: 2023-12-02 00:00:00

Emerging vehicle automation and communication systems (VACS) may contribute to the improvement of vehicle travel time and the mitigation of motorway traffic congestion on the basis of appropriate control strategies. This work considers the possibility that automated, or semi-automated, vehicles are equipped with devices that perform (or recommend) lane-changing tasks. The lane-changing strategy MOBIL (minimizing overall braking induced by lane changing) has been chosen for its simplicity and ductility, as well as for the reduced number of parameters that need to be specified (namely, politeness factor and threshold). A wide set of simulations, where MOBIL has been implemented within the microscopic traffic simulator Aimsun for a calibrated motorway network (representing a stretch of motorway A12 in the Netherlands), has been performed. Simulations revealed the impact that the choice of different parameters have on the travel time of different vehicles, allowing also to analyse their behaviour with respect to different traffic conditions (without or with traffic congestion).
Learning-based Ecological Adaptive Cruise Control of Autonomous Electric Vehicles: A Comparison of ADP, DQN and DDPG Approaches
Authors: Sunwoo Kim, Kwang-Ki K. Kim
Date: 2023-12-02 00:00:00

This paper presents model-based and model-free learning methods for economic and ecological adaptive cruise control (Eco-ACC) of connected and autonomous electric vehicles. For model-based optimal control of Eco-ACC, we considered longitudinal vehicle dynamics and a quasi-steady-state powertrain model including the physical limits of a commercial electric vehicle. We used adaptive dynamic programming (ADP), in which the value function was trained using data obtained from IPG CarMaker simulations. For real-time implementation, forward multi-step look-ahead prediction and optimization were executed in a receding horizon scheme to maximize the energy efficiency of the electric machine while avoiding rear-end collisions and satisfying the powertrain, speed, and distance-gap constraints. For model-free optimal control of Eco-ACC, we applied two reinforcement learning methods, Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), in which deep neural networks were trained in IPG CarMaker simulations. For performance demonstrations, the HWFET, US06, and WLTP Class 3b driving cycles were used to simulate the front vehicle, and the energy consumptions of the host vehicle and front vehicle were compared. In high-fidelity IPG CarMaker simulations, the proposed learning-based Eco-ACC methods demonstrated approximately 3-5% and 10-14% efficiency improvements in highway and city-highway driving scenarios, respectively, compared with the front vehicle. A video of the CarMaker simulation is available at https://youtu.be/DIXzJxMVig8.
A Cyclic Small Phase Theorem
Authors: Chao Chen, Wei Chen, Di Zhao, Jianqi Chen, Li Qiu
Date: 2023-12-01 00:00:00

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a single-loop cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between $-\pi$ and $\pi$. The proposed theorem complements a cyclic version of the celebrated small gain theorem. In addition, a generalization of the proposed theorem is made via the use of angular scaling techniques for reducing conservatism.
Improved Stability and Controller Design Criteria for Two-dimensional Differential-Algebraic-Equation Systems via LMI Approach
Authors: Abdolah RoshanaeeDeh, Hajar Atrianfar, Masoud Shafiee
Date: 2023-12-01 00:00:00

This paper addresses issues concerning asymptotic stability testing and controller design for the two-dimensional Rosser model in Differential-Algebraic-Equations systems (DAEs). We present sufficient stability criteria based on the Lyapunov approach, utilizing a set of Linear-Matrix-Inequalities (LMIs) tailored for two-dimensional DAEs. Furthermore, we establish a set of sufficient conditions for determining the feasibility of both state- and output-feedback controllers. Our methods eliminate the need for decomposing the two-dimensional DAEs into separate algebraic and differential components.
Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Authors: YiXuan Wang, Ruochen Jiao, Sinong Simon Zhan, Chengtian Lang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu
Date: 2023-11-28 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Common Sense Reasoning, Model Predictive Control

Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data. To this end, this paper explores the integration of Large Language Models (LLMs) into AD systems, leveraging their robust common-sense knowledge and reasoning abilities. The proposed methodologies employ LLMs as intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning, for enhancing driving performance and safety. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine. Demonstrating superior performance and safety metrics compared to state-of-the-art approaches, our approach shows the promising potential for using LLMs for autonomous vehicles.