pwc-archive/papers-with-abstracts huggingface dataset:
Optimal policy design for decision problems under social influence
Authors: Valentina Breschi, Chiara Ravazzi, Paolo Frasca, Fabrizio Dabbene, Mara Tanelli
Date: 2024-06-11 00:00:00
Tasks: Decision Making

This paper focuses on describing the impact of policy actions on individuals' opinions in the presence of social and external influences toward proposing preliminary nudging strategies to achieve a cost-effectiveness trade-off. To this end, we extend the classical Friedkin and Johnsen model of opinion dynamics to incorporate random factors, such as variability in individual predispositions due to uncontrolled events (e.g., modeling the impact of the weather on daily mobility choices), and describe the impact of personalized policies. Furthermore, we formulate an optimal control problem aimed at fostering the social acceptance of particular actions/choices within the network. Through our analysis and numerical simulations, we illustrate the features of the proposed model in the absence of nudging and the effectiveness of the proposed (optimal) nudging strategies.
Exploiting Heterogeneity in the Decentralised Control of Platoons
Authors: Richard Pates
Date: 2024-06-11 00:00:00

This paper investigates the use of decentralised control architectures with heterogeneous dynamics for improving performance in large-scale systems. Our focus is on two well-known decentralised approaches; the 'predecessor following' and 'bidirectional architectures' for vehicle platooning. The former, utilising homogeneous control dynamics, is known to face exponential growth in disturbance amplification throughout the platoon, resulting in poor scalability properties. We demonstrate that by incorporating heterogeneous control system dynamics, this limitation disappears entirely, even under bandwidth constraints. Furthermore, we reveal that introducing heterogeneity in the bidirectional architecture allows the platoon's behaviour to be rendered independent of its length, allowing for highly scalable performance.
Meta-Backscatter: A New ISAC Paradigm for Battery-Free Internet of Things
Authors: Xu Liu, Hongliang Zhang, Kaigui Bian, Xi Weng, Lingyang Song
Date: 2024-06-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

The meta-material sensor has been regarded as a next-generation sensing technology for the battery-free Internet of Things (IoT) due to its battery-free characteristic and improved sensing performance. The meta-material sensors function as backscatter tags that change their reflection coefficients with the conditions of sensing targets such as temperature and gas concentration, allowing transceivers to perform sensing by analyzing the reflected signals from the sensors. Simultaneously, the sensors also function as environmental scatterers, creating additional signal paths to enhance communication performance. Therefore, the meta-material sensor potentially provides a new paradigm of Integrated Sensing and Communication (ISAC) for the battery-free IoT system. In this article, we first propose a Meta-Backscatter system that utilizes meta-material sensors to achieve diverse sensing functionalities and improved communication performance. We begin with the introduction of the metamaterial sensor and further elaborate on the Meta-Backscatter system. Subsequently, we present optimization strategies for meta-material sensors, transmitters, and receivers to strike a balance between sensing and communication. Furthermore, this article provides a case study of the system and examines the feasibility and trade-off through the simulation results. Finally, potential extensions of the system and their related research challenges are addressed.
Adaptive Control: Algorithms, Analysis and Applications
Authors: Ioan Doré Landau, Rogelio Lozano, Mohammed M Saad, Alireza Karimi
Date: 2024-06-11 00:00:00

Adaptive control provides techniques for adjusting control parameters in real time to maintain system performance despite unknown or changing process parameters. These methods use real data to tune controllers and adjust plant models or controller parameters. The field has progressed significantly since the 1970s, helped by digital computers. Early applications offered essential feedback, and theoretical advances solved many basic problems. This book comprehensively treats adaptive control, guiding readers from basic problems to analytical solutions with practical applications. Presenting a unified view is challenging due to various design steps and applications. However, a coherent presentation of basic techniques is now possible. The book uses a discrete-time approach to reflect the role of digital computers and shares practical experiences and understanding of different control designs. Mathematical aspects of synthesizing and analyzing algorithms are emphasized, though they alone may not solve practical problems. The book includes applications of control techniques but stresses that a solid mathematical understanding is crucial for creatively applying them to new challenges. Mathematical synthesis and analysis are highlighted, but they must be supplemented with practical problem-solving and algorithm modifications for specific applications.
Exploiting Monotonicity to Design an Adaptive PI Passivity-Based Controller for a Fuel-Cell System
Authors: Carlo A. Beltran, Rafael Cisneros, Diego Langarica-Cordoba, Romeo Ortega, Luis H. Diaz-Saldierna
Date: 2024-06-08 00:00:00

We present a controller for a power electronic system composed of a fuel cell (FC) connected to a boost converter which feeds a resistive load. The controller aims to regulate the output voltage of the converter regardless of the uncertainty of the load. Leveraging the monotonicity feature of the fuel cell polarization curve we prove that the nonlinear system can be controlled by means of a passivity-based proportional-integral approach. We afterward extend the result to an adaptive version, allowing the controller to deal with parameter uncertainties, such as inductor parasitic resistance, load, and FC polarization curve parameters. This adaptive design is based on an indirect control approach with online parameter identification performed by a ``hybrid'' estimator which combines two techniques: the gradient-descent and immersion-and-invariance algorithms. The overall system is proved to be stable with the output voltage regulated to its reference. Experimental results validate our proposal under two real-life scenarios: pulsating load and output voltage reference changes.
Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions
Authors: Xiang Zhu, Guangchun Ruan, Hua Geng, Honghai Liu, Mingfei Bai, Chao Peng
Date: 2024-06-11 00:00:00
Tasks: Diversity

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity.
A game-theoretic, market-based approach to extract flexibility from distributed energy resources
Authors: Vineet Jagadeesan Nair, Anuradha Annaswamy
Date: 2024-06-10 00:00:00

We propose a market designed using game theory to optimally utilize the flexibility of distributed energy resources (DERs) like solar, batteries, electric vehicles, and flexible loads. Market agents perform multiperiod optimization to determine their feasible flexibility limits for power injections while satisfying all constraints of their DERs. This is followed by a Stackelberg game between the market operator and agents. The market operator as the leader aims to regulate the aggregate power injection around a desired value by leveraging the flexibility of their agents, and computes optimal prices for both electricity and flexibility services. The agents follow by optimally bidding their desired flexible power injections in response to these prices. We show the existence and uniqueness of a Nash equilibrium among all the agents and a Stackelberg equilibrium between all agents and the operator. In addition to deriving analytical closed-form solutions, we provide simulation results for a small example system to illustrate our approach.
Data-driven Power Flow Linearization: Simulation
Authors: Mengshuo Jia, Gabriela Hug, Ning Zhang, Zhaojian Wang, Yi Wang, Chongqing Kang
Date: 2024-06-10 00:00:00
Tasks: Benchmarking, Computational Efficiency

Building on the theoretical insights of Part I, this paper, as the second part of the tutorial, dives deeper into data-driven power flow linearization (DPFL), focusing on comprehensive numerical testing. The necessity of these simulations stems from the theoretical analysis's inherent limitations, particularly the challenge of identifying the differences in real-world performance among DPFL methods with overlapping theoretical capabilities and/or limitations. The absence of a comprehensive numerical comparison of DPFL approaches in the literature also motivates this paper, especially given the fact that over 95% of existing DPFL studies have not provided any open-source codes. To bridge the gap, this paper first reviews existing DPFL experiments, examining the adopted test scenarios, load fluctuation settings, data sources, considerations for data noise/outliers, and the comparison made so far. Subsequently, this paper evaluates a total of 44 methods, containing over 30 existing DPFL approaches, some innovative DPFL techniques, and several classic physics-driven power flow linearization methods for benchmarking. The evaluation spans various dimensions, including generalizability, applicability, accuracy, and computational efficiency, using various different test cases scaling from 9-bus to 1354-bus systems. The numerical analysis identifies and examines significant trends and consistent findings across all methods under various test cases. It also offers theoretical insights into phenomena like under-performance, failure, excessive computation times, etc. Overall, this paper identifies the differences in the performances of the wide range of DPFL methods, reveals gaps not evident from theoretical discussions, assists in method selection for real-world applications, and provides thorough discussions on open questions within DPFL research, indicating ten potential future directions.
A LoRa-based Energy-efficient Sensing System for Urban Data Collection
Authors: Lukas Schulthess, Tiago Salzmann, Christian Vogt, Michele Magno
Date: 2024-06-10 00:00:00
Tasks: Privacy Preserving

Nowadays, cities provide much more than shopping opportunities or working spaces. Individual locations such as parks and squares are used as meeting points and local recreation areas by many people. To ensure that they remain attractive in the future, the design of such squares must be regularly adapted to the needs of the public. These utilization trends can be derived using public data collection. The more diverse and rich the data sets are, the easier it is to optimize public space design through data analysis. Traditional data collection methods such as questionnaires, observations, or videos are either labor intensive or cannot guarantee to preserve the individual's privacy. This work presents a privacy-preserving, low-power, and low-cost smart sensing system that is capable of anonymously collecting data about public space utilization by analyzing the occupancy distribution of public seating. To support future urban planning the sensor nodes are capable of monitoring environmental noise, chair utilization, and their position, temperature, and humidity and provide them over a city-wide Long Range Wide Area Network (LoRaWAN). The final sensing system's robust operation is proven in a trial run at two public squares in a city with 16 sensor nodes over a duration of two months. By consuming 33.65 mWh per day with all subsystems enabled, including sitting detection based on a continuous acceleration measurement operating on a robust and simple threshold algorithm, the custom-designed sensor node achieves continuous monitoring during the 2-month trial run. The evaluation of the experimental results clearly shows how the two locations are used, which confirms the practicability of the proposed solution. All data collected during the field trial is publicly available as open data.
Thin Film Reconfigurable Intelligent Surface for Harmonic Beam Steering
Authors: Boxuan Xie, Aleksandr D. Kuznetsov, Lauri Mela, Jari Lietzén, Kalle Ruttik, Alp Karakoç, Riku Jäntti
Date: 2024-06-10 00:00:00

This letter explores an implementation of a novel thin film 1-by-4 reconfigurable intelligent surface (RIS) designed for future communication and sensing scenarios. Utilizing cost-effective inkjet printing methods and additive manufacturing, our approach significantly simplifies the RIS construction process and reduces production costs. The RIS, fabricated on a flexible and lightweight polyethylene terephthalate (PET) substrate, integrates antennas, switching circuitry, and a microcontroller unit (MCU), without a ground shield. This setup enables individual and simultaneous control of each RIS element, manipulating the captured carrier signal by reflecting and refracting its dominant harmonics. Beams of the harmonics can be steered to multiple desired directions at both front and back sides of the surface. Measurement results of the beam steering show that the RIS has the potential to enable RIS-aided communication and sensing applications.
Learning-based cognitive architecture for enhancing coordination in human groups
Authors: Antonio Grotta, Marco Coraggio, Antonio Spallone, Francesco De Lellis, Mario di Bernardo
Date: 2024-06-10 00:00:00

As interactions with autonomous agents-ranging from robots in physical settings to avatars in virtual and augmented realities-become more prevalent, developing advanced cognitive architectures is critical for enhancing the dynamics of human-avatar groups. This paper presents a reinforcement-learning-based cognitive architecture, trained via a sim-to-real approach, designed to improve synchronization in periodic motor tasks, crucial for applications in group rehabilitation and sports training. Extensive numerical validation consistently demonstrates improvements in synchronization. Theoretical derivations and numerical investigations are complemented by preliminary experiments with real participants, showing that our avatars can integrate seamlessly into human groups, often being indistinguishable from humans.
A Lora-Based and Maintenance-Free Cattle Monitoring System for Alpine Pastures and Remote Locations
Authors: Lukas Schulthess, Fabrice Longchamp, Christian Vogt, Michele Magno
Date: 2024-06-10 00:00:00

The advent of the Internet of Things (IoT) is boosting the proliferation of sensors and smart devices in industry and daily life. Continuous monitoring IoT systems are also finding applications in agriculture, particularly in the realm of smart farming. The adoption of wearable sensors to record the activity of livestock has garnered increasing interest. Such a device enables farmers to locate, monitor, and constantly assess the health status of their cattle more efficiently and effectively, even in challenging terrain and remote locations. This work presents a maintenance-free and robust smart sensing system that is capable of tracking cattle in remote locations and collecting activity parameters, such as the individual's grazing- and resting time. To support the paradigm of smart farming, the cattle tracker is capable of monitoring the cow's activity by analyzing data from an accelerometer, magnetometer, temperature sensor, and Global Navigation Satellite System (GNSS) module, providing them over Long Range Wide Area Network (LoRaWAN) to a backend server. By consuming 511.9 J per day with all subsystems enabled and a data transmission every 15 minutes, the custom-designed sensor node achieves a battery lifetime of 4 months. When exploiting the integrated solar energy harvesting subsystem, this can be even increased by 40% to up to 6 months. The final sensing system's robust operation is proven in a trial run with two cows on a pasture for over three days. Evaluations of the experimental results clearly show behavior patterns, which confirms the practicability of the proposed solution.
Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation
Authors: Pablo Krupa, Johannes Köhler, Antonio Ferramosca, Ignacio Alvarado, Melanie N. Zeilinger, Teodoro Alamo, Daniel Limon
Date: 2024-06-10 00:00:00
Tasks: Model Predictive Control

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.
Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation
Authors: Abhijit Kulkarni, Remus Teodorescu
Date: 2024-06-10 00:00:00
Tasks: Management

A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been considerable research in developing data-driven methods for improving the accuracy of SoH estimation. The data-driven methods are diverse and use different machine-learning (ML) or artificial intelligence (AI) based techniques. Complex AI/ML techniques are difficult to implement in low-cost microcontrollers used in BMS due to the extensive use of non-linear functions and large matrix operations. This paper proposes a computationally efficient and data-lightweight SoH estimation technique. Online impedance at four discrete frequencies is evaluated to derive the features of a linear regression problem. The proposed solution avoids complex mathematical operations and it is well-suited for online implementation in a commercial BMS. The accuracy of this method is validated on two experimental datasets and is shown to have a mean absolute error (MAE) of less than 2% across diverse training and testing data.
Model Updating for Nonlinear Systems with Stability Guarantees
Authors: Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
Date: 2024-06-10 00:00:00
Tasks: model

To improve the predictive capacity of system models in the input-output sense, this paper presents a framework for model updating via learning of modeling uncertainties in locally (and thus also in globally) Lipschitz nonlinear systems. First, we introduce a method to extend an existing known model with an uncertainty model so that stability of the extended model is guaranteed in the sense of set invariance and input-to-state stability. To achieve this, we provide two tractable semi-definite programs. These programs allow obtaining optimal uncertainty model parameters for both locally and globally Lipschitz nonlinear models, given uncertainty and state trajectories. Subsequently, in order to extract this data from the available input-output trajectories, we introduce a filter that incorporates an approximated internal model of the uncertainty and asymptotically estimates uncertainty and state realizations. This filter is also synthesized using semi-definite programs with guaranteed robustness with respect to uncertainty model mismatches, disturbances, and noise. Numerical simulations for a large data-set of a roll plane model of a vehicle illustrate the effectiveness and practicality of the proposed methodology in improving model accuracy, while guaranteeing stability.
Economic Model Predictive Control of Water Distribution Systems with Accelerated Optimization Algorithm
Authors: Saskia Putri, Faegheh Moazeni, Javad Khazaei
Date: 2024-06-10 00:00:00
Tasks: Blocking, Computational Efficiency, Management, Model Predictive Control

Model predictive control (MPC) has emerged as an effective strategy for water distribution systems (WDSs) management. However, it is hampered by the computational burden for large-scale WDSs due to the combinatorial growth of possible control actions that must be evaluated at each time step. Therefore, a fast computation algorithm to implement MPC in WDSs can be obtained using a move-blocking approach that simplifies control decisions while ensuring solution feasibility. This paper introduces a least-restrictive move-blocking that interpolates the blocked control rate of change, aiming at balancing computational efficiency with operational effectiveness. The proposed control strategy is demonstrated on aggregated WDSs, encompassing multiple hydraulic elements. This implementation is incorporated into a multi-objective optimization framework that concurrently optimizes water level security of the storage tanks, smoothness of the control actions, and cost-effective objectives. A fair comparison between the proposed approach with the non-blocking Economic MPC is provided.
Dynamic Virtual Power Plants with Robust Frequency Regulation Capability
Authors: Xiang Zhu, Hua Geng, Hongyang Qing, Guangchun, Ruan, Xiuqiang He
Date: 2024-06-10 00:00:00

The rapid integration of inverter-based resources (IBRs) into power systems has identified frequency security challenges due to reduced inertia and increased load volatility. This paper proposes a robust power reserve decision-making approach for dynamic virtual power plants (DVPPs) to address these challenges, especially under temporally sequential and uncertain disturbances. An analytical model is developed to characterize the system's frequency response dynamics, enabling the quantification of virtual inertia and virtual damping requirements to meet rate-of-change-of-frequency (RoCoF), frequency nadir, and steady-state deviation constraints. By analytically deriving the regulation power dynamics, the required virtual inertia and damping parameters for the DVPP are determined in a robust way. Then, the total power reserve decision is made by optimally allocating the parameters and calculating the actual power reserves for IBRs, fully considering their economic diversity. Finally, case studies conducted on an IEEE nine-bus system demonstrate the effectiveness of the proposed approach. The results indicate the high reliability of the proposed approach in ensuring frequency security.