pwc-archive/papers-with-abstracts huggingface dataset:
ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems
Authors: Mehdi Zafari, Divyanshu Pandey, Rahman Doost-Mohammady, César A. Uribe
Date: 2024-09-09 00:00:00
Tasks: Distributed Optimization

In cell-free massive MIMO systems with multiple distributed access points (APs) serving multiple users over the same time-frequency resources, downlink beamforming is done through spatial precoding. Precoding vectors can be optimally designed to use the minimum downlink transmit power while satisfying a quality-of-service requirement for each user. However, existing centralized solutions to beamforming optimization pose challenges such as high communication overhead and processing delay. On the other hand, distributed approaches either require data exchange over the network that scales with the number of antennas or solve the problem for cellular systems where every user is served by only one AP. In this paper, we formulate a multi-user beamforming optimization problem to minimize the total transmit power subject to per-user SINR requirements and propose a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve it. In our method, every AP solves an iterative optimization problem using its local channel state information. APs only need to share a real-valued vector of interference terms with the size of the number of users. Through simulation results, we demonstrate that our proposed algorithm solves the optimization problem within tens of ADMM iterations and can effectively satisfy per-user SINR constraints.
The Dilemma of Electricity Grid Expansion Planning in Areas at the Risk of Wildfire
Authors: Muhammad Waseem, Reza Bayani, Saeed D. Manshadi
Date: 2024-08-31 00:00:00

The utilities consider public safety power shut-offs imperative for the mitigation of wildfire risk. This paper presents expansion planning of power system under fire hazard weather conditions. The power lines are quantified based on the risk of fire ignition. A 10-year expansion planning scenario is discussed to supply power to customers by considering three decision variables: distributed solar generation; modification of existing power lines; addition of new lines. Two-stage robust optimization problem is formulated and solved using Column-and-Constraint Generation Algorithm to find improved balance among de-energization of customers, distributed solar generation, modification of power lines, and addition of new lines. It involves lines de-energization of high wildfire risk regions and serving the customers by integrating distributed solar generation. The impact of de-energization of lines on distributed solar generation is assessed. The number of hours each line is energized and total load shedding during a 10-year period is evaluated. Different uncertainty levels for system demand and solar energy integration are considered to find the impact on the total operation cost of the system. The effectiveness of the presented algorithm is evaluated on 6- and 118-bus systems.
Dynamics modelling and path optimization for the on-orbit assembly of large flexible structures using a multi-arm robot
Authors: Ricardo Rodrigues, Valentin Preda, Francesco Sanfedino, Daniel Alazard
Date: 2024-09-09 00:00:00

This paper presents a comprehensive methodology for modeling an on-orbit assembly mission scenario of a large flexible structure using a multi-arm robot. This methodology accounts for significant changes in inertia and flexibility throughout the mission, addressing the problem of coupling dynamics between the robot and the evolving flexible structure during the assembly phase. A three-legged walking robot is responsible for building the structure, with its primary goal being to walk stably on the flexible structure while picking up, carrying and assembling substructure components. To accurately capture the dynamics and interactions of all subsystems in the assembly scenario, various linear fractional representations (LFR) are developed, considering the changing geometrical configuration of the multi-arm robot, the varying flexible dynamics and uncertainties. A path optimization algorithm is proposed for the multi-arm robot, capable of selecting trajectories based on various cost functions related to different performance and stability metrics. The obtained results demonstrate the effectiveness of the proposed modeling methodology and path optimization algorithm.
Supervised Learning for Stochastic Optimal Control
Authors: Vince Kurtz, Joel W. Burdick
Date: 2024-09-09 00:00:00

Supervised machine learning is powerful. In recent years, it has enabled massive breakthroughs in computer vision and natural language processing. But leveraging these advances for optimal control has proved difficult. Data is a key limiting factor. Without access to the optimal policy, value function, or demonstrations, how can we fit a policy? In this paper, we show how to automatically generate supervised learning data for a class of continuous-time nonlinear stochastic optimal control problems. In particular, applying the Feynman-Kac theorem to a linear reparameterization of the Hamilton-Jacobi-Bellman PDE allows us to sample the value function by simulating a stochastic process. Hardware accelerators like GPUs could rapidly generate a large amount of this training data. With this data in hand, stochastic optimal control becomes supervised learning.
Almost Global Trajectory Tracking for Quadrotors Using Thrust Direction Control on $\mathcal{S}^2$
Authors: Mirko Leomanni, Alberto Dionigi, Francesco Ferrante, Paolo Valigi, Gabriele Costante
Date: 2024-09-09 00:00:00

Many of the existing works on quadrotor control address the trajectory tracking problem by employing a cascade design in which the translational and rotational dynamics are stabilized by two separate controllers. The stability of the cascade is often proved by employing trajectory-based arguments, most notably, integral input-to-state stability. In this paper, we follow a different route and present a control law ensuring that a composite function constructed from the translational and rotational tracking errors is a Lyapunov function for the closed-loop cascade. In particular, starting from a generic control law for the double integrator, we develop a suitable attitude control extension, by leveraging a backstepping-like procedure. Using this construction, we provide an almost global stability certificate. The proposed design employs the unit sphere $\mathcal{S}^2$ to describe the rotational degrees of freedom required for position control. This enables a simpler controller tuning and an improved tracking performance with respect to previous global solutions. The new design is demonstrated via numerical simulations and on real-world experiments.
On the role of the signature transform in nonlinear systems and data-driven control
Authors: Anna Scampicchio, Melanie N. Zeilinger
Date: 2024-09-09 00:00:00

Classic control techniques typically rely on a model of the system's response to external inputs, which is difficult to obtain from first principles especially if the unknown dynamics are nonlinear. In this paper, we address this issue by presenting an approach based on the so-called signature transform, a tool that is still largely unexplored in data-driven control. We first show that the signature provides rigorous and practically effective features to represent and predict system trajectories. Furthermore, we propose a novel use of this tool on an output-matching problem, paving the way for signature-based, data-driven predictive control.
Power Control of Converters Connected via an L Filter to a Weak Grid. A Flatness-Based Approach
Authors: Sebastian Gomez Jorge, Jorge A. Solsona, Claudio A. Busada, Gerardo Tapia-Otaegui, Ana Susperregui, M. Itsaso Martínez
Date: 2024-09-09 00:00:00

In this article, a nonlinear strategy based on a flatness approach is used for controlling the instantaneous complex power supplied from the Point of Common Coupling (PCC) to a weak grid. To this end, the strategy introduced by the authors in [1] considering a strong grid is robustified for avoiding system instability when the converter is connected to an unknown grid. The robustification method consists of including a notch filter that estimates the PCC voltage and using it to build the controller (i.e. the measured PCC voltage used to design the control strategy for a strong grid is replaced by the PCC voltage estimated with the notch filter). In addition, before designing the controller, the steady-state stability and safe operation limits when injecting complex instantaneous power to a grid of unknown impedance are analyzed. This analysis is independent of the control strategy, and applies to all power injection schemes. Simulations are presented for showing the performance of the proposed controller in presence of a weak grid.
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement
Authors: Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
Date: 2024-09-09 00:00:00
Tasks: Distributed Optimization, Quantization

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through $3$-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only $3$-bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics.
Energy Internet: A Standardization-Based Blueprint Design
Authors: Ye Guo, Hanyang Lin, Hongbin Sun
Date: 2024-09-08 00:00:00

The decarbonization of power and energy systems faces a bottleneck: The enormous number of user-side resources cannot be properly managed and operated by centralized system operators, who used to send dispatch instructions only to a few large power plants. To break through, we need not only new devices and algorithms, but structural reforms of our energy systems. Taking the Internet as a paradigm, a practicable design of the Energy Internet is presented based on the principle of standardization. A combination of stylized data and energy delivery, referred to as a Block of Energy Exchange (BEE), is designed as the media to be communicated, which is parsed by the Energy Internet Card. Each Energy Internet Card is assigned a unique MAC address, defining a participant of the Energy Internet, whose standardized profile will be automatically updated according to BEE transfers without the intervention of any centralized operator. The structure of Energy Internet and protocols thereof to support the transfer of BEE are presented. System operators will become Energy Internet Service Providers, who operate the energy system by flow control and dispatching centralized resources, which is decoupled from users' behaviors in the Energy Internet. Example shows that the Energy Internet can not only reduce carbon emissions via interactions between peers, but also promotes energy democracy and dwindles the gap in energy equity.
Large-scale road network partitioning: a deep learning method based on convolutional autoencoder model
Authors: Pengfei Xu, Weifeng Li, Chenjie Xu, Jian Li
Date: 2024-09-08 00:00:00
Tasks: Clustering, Management

With the development of urbanization, the scale of urban road network continues to expand, especially in some Asian countries. Short-term traffic state prediction is one of the bases of traffic management and control. Constrained by the space-time cost of computation, the short-term traffic state prediction of large-scale urban road network is difficult. One way to solve this problem is to partition the whole network into multiple sub-networks to predict traffic state separately. In this study, a deep learning method is proposed for road network partitioning. The method mainly includes three steps. First, the daily speed series for roads are encoded into the matrix. Second, a convolutional autoencoder (AE) is built to extract series features and compress data. Third, the spatial hierarchical clustering method with adjacency relationship is applied in the road network. The proposed method was verified by the road network of Shenzhen which contains more than 5000 links. The results show that AE-hierarchical clustering distinguishes the tidal traffic characteristics and reflects the process of congestion propagation. Furthermore, two indicators are designed to make a quantitative comparison with spectral clustering: intra homogeneity increases by about 9% on average while inter heterogeneity about 9.5%. Compared with past methods, the time cost also decreases. The results may suggest ways to improve the management and control of urban road network in other metropolitan cities. The proposed method is expected to be extended to other problems related to large-scale networks.
Nonlinear Cooperative Output Regulation with Input Delay Compensation
Authors: Shiqi Zheng, Choon Ki Ahn, Xiaowei Jiang, Huaicheng Yan, Peng Shi
Date: 2024-09-08 00:00:00

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results.
On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community
Authors: Lingfei Wang, Yu Xing, Karl H. Johansson
Date: 2024-09-08 00:00:00

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness.
An Analysis of Logit Learning with the r-Lambert Function
Authors: Rory Gavin, Ming Cao, Keith Paarporn
Date: 2024-09-08 00:00:00
Tasks: Imitation Learning

The well-known replicator equation in evolutionary game theory describes how population-level behaviors change over time when individuals make decisions using simple imitation learning rules. In this paper, we study evolutionary dynamics based on a fundamentally different class of learning rules known as logit learning. Numerous previous studies on logit dynamics provide numerical evidence of bifurcations of multiple fixed points for several types of games. Our results here provide a more explicit analysis of the logit fixed points and their stability properties for the entire class of two-strategy population games -- by way of the $r$-Lambert function. We find that for Prisoner's Dilemma and anti-coordination games, there is only a single fixed point for all rationality levels. However, coordination games exhibit a pitchfork bifurcation: there is a single fixed point in a low-rationality regime, and three fixed points in a high-rationality regime. We provide an implicit characterization for the level of rationality where this bifurcation occurs. In all cases, the set of logit fixed points converges to the full set of Nash equilibria in the high rationality limit.
Protecting residential electrical panels and service through model predictive control: A field study
Authors: Elias N. Pergantis, Levi D. Reyes Premer, Alex H. Lee, Priyadarshan, Haotian Liu, Eckhard A. Groll, Davide Ziviani, Kevin J. Kircher
Date: 2024-09-07 00:00:00
Tasks: Model Predictive Control

Residential electrification - replacing fossil-fueled appliances and vehicles with electric machines - can significantly reduce greenhouse gas emissions and air pollution. However, installing electric appliances or vehicle charging in a residential building can sharply increase its current draws. In older housing, high current draws can jeopardize electrical infrastructure, such as circuit breaker panels or electrical service (the wires that connect a building to the distribution grid). Upgrading electrical infrastructure can entail long delays and high costs, so poses a significant barrier to electrification. This paper develops and field-tests a control system that avoids the need for electrical upgrades by keeping an electrified home's total current draw within the safe limits of its panel and service. In the proposed control architecture, a high-level controller plans device set-points over a rolling prediction horizon. A low-level controller monitors real-time conditions and ramps down devices if necessary. The control system was tested in an occupied, electrified single-family house with code-minimum insulation, an air-to-air heat pump and backup resistance heat, a resistance water heater, and a plug-in hybrid electric vehicle with Level I charging. The field tests spanned 31 winter days with outdoor temperatures as low as -20 C. The control system maintained the whole-home current within the safe limits of electrical panels and service rated at 100 A, a common rating for older houses in North America, by adjusting only the temperature set-points of the heat pump and water heater. Simulations suggest that the same 100 A limit could accommodate a second electric vehicle with Level II charging. The proposed control system could allow older homes to safely electrify without upgrading electrical panels or service, saving a typical household on the order of $2,000 to $10,000.
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks
Authors: Ahrar N. Hamad, Ahmad Adnan Qidan, Taisir E. H. ElGorashi, Jaafar M. H. Elmirghani
Date: 2024-09-07 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optical wireless communication (OWC) is envisioned as one of the main enabling technologies of 6G networks, complementing radio frequency (RF) systems to provide high data rates. One of the crucial issues in indoor OWC is service interruptions due to blockages that obstruct the line of sight (LoS) between users and their access points (APs). Recently, reflecting surfaces referred to as intelligent reflecting surfaces (IRSs) have been considered to provide improved connectivity in OWC systems by reflecting AP signals toward users. In this study, we investigate the integration of IRSs into an indoor OWC system to improve the sum rate of the users and to ensure service continuity. We formulate an optimization problem for sum rate maximization, where the allocation of both APs and mirror elements of IRSs to users is determined to enhance the aggregate data rate. Moreover, reinforcement learning (RL) algorithms, specifically Q-learning and SARSA algorithms, are proposed to provide real-time solutions with low complexity and without prior system knowledge. The results show that the RL algorithms achieve near-optimal solutions that are close to the solutions of mixed integer linear programming (MILP). The results also show that the proposed scheme achieves up to a 45% increase in data rate compared to a traditional scheme that optimizes only the allocation of APs while the mirror elements are assigned to users based on the distance.
Barrier Integral Control for Global Asymptotic Stabilization of Uncertain Nonlinear Systems under Smooth Feedback and Transient Constraints
Authors: Christos K. Verginis
Date: 2024-09-07 00:00:00

This paper addresses the problem of asymptotic stabilization for high-order control-affine MIMO nonlinear systems with unknown dynamic terms. We introduce Barrier Integral Control, a novel algorithm designed to confine the system's state within a predefined funnel, ensuring adherence to prescribed transient constraints, and asymptotically drive it to zero from any initial condition. The algorithm leverages the innovative integration of a reciprocal barrier function and an error-integral term, featuring smooth feedback control. Notably, it operates without relying on any information or approximation schemes for the (unknown) dynamic terms, which, unlike a large class of previous works, are not assumed to be bounded or to comply with globally Lipschitz/growth conditions. Additionally, the system's trajectory and asymptotic performance are decoupled from the uncertain model, control-gain selection, and initial conditions. Finally, comparative simulation studies validate the effectiveness of the proposed algorithm.
Physical Design: Methodologies and Developments
Authors: Atharva M. Kulkarni, Abhay Chopde
Date: 2024-09-07 00:00:00

The design and production of VLSI chips is a multilevel heirarchical process. As the demand for reduced die-area and technology nodes becomes prevalent, it gets increasingly challenging to optimize Power, Performance and Area (PPA) parameters to accommodate for the ever-increasing core logic on a chip. A well defined heirarchical flow is thus quintessential when it comes to VLSI design process. A robust heirarchical flow should encompass all stages, right from Gate-level RTL Synthesis (Front End Design) to Logic Placement and Verification (Back End Physical Design) and finally culminating with tapeout / production. Physical Design in this aforementioned flow is the process of translating logical circuit description into physically realizable GDSII form. This involves defining the best possible placement and routing for standard cells, macros and I/Os in the design to optimize PPA for any given netlist. This paper helps capture the nitty-gritty of methodologies and algorithms that are pertinent to the building and optimization of an efficient and robust physical design flow in VLSI chip-designing process.