pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Step Optimal Tracking Control of Unknown Nonzero-Sum Games based on Least Squares and Linear Programming: An Application to a Fully-Automated, Dual-Hormone Artificial Pancreas
Authors: Alexandros Tanzanakis, John Lygeros
Date: 2023-11-06 00:00:00

We consider the problem of optimal tracking control of unknown discrete-time nonlinear nonzero-sum games. The related state-of-art literature is mostly focused on Policy Iteration algorithms and multiple neural network approximation, which may lead to practical implementation challenges and high computational burden. To overcome these problems, we propose a novel Q-function-based multi-step Value Iteration algorithm, which provides the potential to accelerate convergence speed and improve the quality of solutions, with an easy-to-realize initialization condition. A critic-only least squares implementation approach is then employed, which alleviates the computational complexity of commonly used multiple neural network-based methods. Afterwards, by introducing the coupled Bellman operator, a novel linear programming approach is derived, based on which Nash equilibria can be approximately computed by solving a set of tractable finite-dimensional optimization problems. We evaluate the tracking control capabilities of the proposed algorithms to the problem of fully-automated dual-hormone (i.e., insulin and glucagon) glucose control in Type 1 Diabetes Mellitus. The U.S. FDA-accepted DMMS.R simulator from the Epsilon Group is used to conduct extensive in-silico clinical studies on virtual patients under a variety of completely unannounced meal and exercise scenarios. Simulation results demonstrate the high reliability and exceptional performance of the proposed multi-step algorithmic framework to critical complex systems.
Exploiting spatial group error and synchrony for a unicycle tracking controller
Authors: Matthew Hampsey, Pieter van Goor, Robert Mahony
Date: 2023-11-06 00:00:00

Trajectory tracking for the kinematic unicycle has been heavily studied for several decades. The unicycle admits a natural $\SE(2)$ symmetry, a key structure exploited in many of the most successful nonlinear controllers in the literature. To the author's knowledge however, all prior work has used a body-fixed, or left-invariant, group error formulation for the study of the tracking problem. In this paper, we consider the spatial, or right-invariant, group error in the design of a tracking controller for the kinematic unicycle. We provide a physical interpretation of the right-invariant error and go on to show that the associated error dynamics are drift-free, a property that is not true for the body-fixed error. We exploit this property to propose a simple nonlinear control scheme for the kinematic unicycle and prove almost-global asymptotic stability of this control scheme for a class of persistently exciting trajectories. We also verify performance of this control scheme in simulation for an example trajectory.
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems
Authors: Cheng Feng, Kedi Zheng, Yi Wang, Kaibin Huang, Qixin Chen
Date: 2023-11-06 00:00:00
Tasks: Decision Making, Distributed Optimization, Federated Learning

The proliferation of novel industrial applications at the wireless edge, such as smart grids and vehicle networks, demands the advancement of cyber-physical systems. The performance of CPSs is closely linked to the last-mile wireless communication networks, which often become bottlenecks due to their inherent limited resources. Current CPS operations often treat wireless communication networks as unpredictable and uncontrollable variables, ignoring the potential adaptability of wireless networks, which results in inefficient and overly conservative CPS operations. Meanwhile, current wireless communications often focus more on throughput and other transmission-related metrics instead of CPS goals. In this study, we introduce the framework of goal-oriented wireless communication resource allocations, accounting for the semantics and significance of data for CPS operation goals. This guarantees optimal CPS performance from a cybernetic standpoint. We formulate a bandwidth allocation problem aimed at maximizing the information utility gain of transmitted data brought to CPS operation goals. Since the goal-oriented bandwidth allocation problem is a large-scale combinational problem, we propose a divide-and-conquer and greedy solution algorithm. The information utility gain is first approximately decomposed into marginal utility information gains and computed in a parallel manner. Subsequently, the bandwidth allocation problem is reformulated as a knapsack problem, which can be further solved greedily with a guaranteed sub-optimality gap. We further demonstrate how our proposed goal-oriented bandwidth allocation algorithm can be applied in four potential CPS applications, including data-driven decision-making, edge learning, federated learning, and distributed optimization.
Design and Performance Analysis of a Class of Generalized Predictive Controllers
Authors: Feilong Zhang
Date: 2023-11-06 00:00:00

The design and structure of generalized predictive control (GPC) are not simple and intuitive. The performance analysis does not deeply analyze how the controller parameters affect the system characteristics and the relationship between the tracking error caused by the noise and the selected controller parameters. This paper proposes a generalized predictive control, and its design is simple and intuitive for unnecessary solving the Diophantine equation. Then the relationship between desired output, disturbance, and system output is analyzed by the characteristic equation and steady-state analysis. Based on this, the study presents research findings on the steady state of the system and verifies them through simulations. Furthermore, this paper introduces GPC with disturbance compensation and incremental generalized minimum variance control (IGMVC) with disturbance compensation. The conditions for the elimination of disturbance are presented in theory and simulation for the first time.
CESAR: Control Envelope Synthesis via Angelic Refinements
Authors: Aditi Kabra, Jonathan Laurent, Stefan Mitsch, André Platzer
Date: 2023-11-06 00:00:00

This paper presents an approach for synthesizing provably correct control envelopes for hybrid systems. Control envelopes characterize families of safe controllers and are used to monitor untrusted controllers at runtime. Our algorithm fills in the blanks of a hybrid system's sketch specifying the desired shape of the control envelope, the possible control actions, and the system's differential equations. In order to maximize the flexibility of the control envelope, the synthesized conditions saying which control action can be chosen when should be as permissive as possible while establishing a desired safety condition from the available assumptions, which are augmented if needed. An implicit, optimal solution to this synthesis problem is characterized using hybrid systems game theory, from which explicit solutions can be derived via symbolic execution and sound, systematic game refinements. Optimality can be recovered in the face of approximation via a dual game characterization. The resulting algorithm, Control Envelope Synthesis via Angelic Refinements (CESAR), is demonstrated in a range of safe control synthesis examples with different control challenges.
A Unified Dynamic Model for the Decomposition of Skin Conductance and the Inference of Sudomotor Nerve Activities
Authors: Hui Sophie Wang, Stacy Marsella, Misha Pavel
Date: 2023-11-05 00:00:00

In behavioral health informatics, inferring an individual's psychological state from physiological and behavioral data is fundamental. A key physiological signal in this endeavor is electrodermal activity (EDA), often quantified as skin conductance (SC), known for its sensitivity to a variety of psychological stimuli. Traditional methods to analyze skin conductance, such as the trough-to-peak method, often result in imprecise estimations due to overlapping skin conductance responses. While various mathematical models have been proposed to improve the analysis, many of them do not incorporate the tonic level in the dynamic system. This paper introduces a novel fourth order dynamic system to model the temporal dynamics of skin conductance, unifying both the tonic level and phasic response. Applied to a large dataset with over 200 participants, majority of the models achieved an $R^2$ value above 0.99. Furthermore, this work offers a unique three-component decomposition of skin conductance, shedding light on its temporal dynamics. Comparative evaluations highlight the model's capability to differentiate arousal levels and maintain an appropriate sparsity level for the estimated sudomotor nerve activities signal. The code of the proposed model and algorithm are available for open access.
Improving Photovoltaic Hosting Capacity of Distribution Networks with Coordinated Inverter Control -- A Case Study of the EPRI J1 Feeder
Authors: Dhaval Dalal, Madhura Sondharangalla, Raja Ayyanar, Anamitra Pal
Date: 2023-11-05 00:00:00

Adding photovoltaic (PV) systems in distribution networks, while desirable for reducing the carbon footprint, can lead to voltage violations under high solar-low load conditions. The inability of traditional volt-VAr control in eliminating all the violations is also well-known. This paper presents a novel coordinated inverter control methodology that leverages system-wide situational awareness to significantly improve hosting capacity (HC). The methodology employs a real-time voltage-reactive power (VQ) sensitivity matrix in an iterative linear optimizer to calculate the minimum reactive power intervention from PV inverters needed for mitigating over-voltage without resorting to active power curtailing or requiring step voltage regulator setting changes. The algorithm is validated using the EPRI J1 feeder under an extensive set of realistic use cases and is shown to provide 3x improvement in HC under all scenarios.
Mobility as a Resource (MaaR) for resilient human-centric automation: a vision paper
Authors: S. Travis Waller, Amalia Polydoropoulou, Leandros Tassiulas, Athanasios Ziliaskopoulos, Sisi Jian, Susann Wagenknecht, Georg Hirte, Satish Ukkusuri, Gitakrishnan Ramadurai, Tomasz Bednarz
Date: 2023-11-05 00:00:00
Tasks: Cloud Computing, Management

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., Mobility as a Resource, MaaR). Further, due to increasing scarcity of shared mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.
Run-to-Run Adaptive Nonlinear Feedforward Control of Electromechanical Switching Devices
Authors: Eduardo Moya-Lasheras, Edgar Ramirez-Laboreo, Eloy Serrano-Seco
Date: 2023-11-05 00:00:00

Feedforward control can greatly improve the response time and control accuracy of any mechatronic system. However, in order to compensate for the effects of modeling errors or disturbances, it is imperative that this type of control works in conjunction with some form of feedback. In this paper, we present a new adaptive feedforward control scheme for electromechanical systems in which real-time measurements or estimates of the position and its derivatives are not technically or economically feasible. This is the case, for example, of commercial electromechanical switching devices such as solenoid actuators. Our proposal consists of two blocks: on the one hand, a feedforward controller based on differential flatness theory; on the other, an iterative adaptation law that exploits the repetitive operation of these devices to modify the controller parameters cycle by cycle. As shown, this law can be fed with any available measurement of the system, with the only requirement that it can be processed and converted into an indicator of the performance of any given operation. Simulated and experimental results show that our proposal is effective in dealing with a long-standing control problem in electromechanics: the soft-landing control of electromechanical switching devices.
Control of convertible UAV with vectorized thrust
Authors: Tomas Lopes de Oliveira, Andre Anglade, Tarek Hamel, Claude Samson
Date: 2023-11-05 00:00:00

This paper is an addition to an article previously published by three of the authors that addresses the control of convertible fixed-wing aircraft with vectorized thrust. The control solution here developed extends the one presented in the former paper by complementing it with a strategy for the hovering flight and a control policy to handle the transition phase between low and high airspeed. An estimator of the air velocity required in all flight phases is also proposed. Realistic simulation results on a tri-tilt rotor Unmanned Aerial Vehicle (UAV) illustrate and assess the methodology.
Optimal Power Flow Solutions via Noise-Resilient Quantum-Inspired Interior-Point Methods
Authors: Farshad Amani, Amin Kargarian
Date: 2023-11-04 00:00:00

This paper presents three quantum interior-point methods (QIPMs) tailored to tackle the DC optimal power flow (DCOPF) problem using noisy intermediate-scale quantum devices. The optimization model is redefined as a linearly constrained quadratic optimization. By incorporating the Harrow-Hassidim-Lloyd (HHL) quantum algorithm into the IPM framework, Newton's direction is determined through the resolution of linear equation systems. To mitigate the impact of HHL error and quantum noise on Newton's direction calculation, we present a noise-tolerant quantum IPM (NT-QIPM) approach. This approach provides high-quality OPF solutions even in scenarios where inexact solutions to the linear equation systems result in approximated Newton's directions. Moreover, to enhance performance in cases of slow convergence and uphold the feasibility of OPF outcomes upon convergence, we propose a hybrid strategy, classically augmented NT-QIPM. This technique is designed to expedite convergence relative to classical IPM while maintaining the solution accuracy. The efficacy of the proposed quantum IPM variants is studied through comprehensive simulations and error analyses on 3-bus, 5-bus, 118-bus, and 300-bus systems, highlighting their potential and promise in addressing challenging OPF scenarios. By modeling the errors and incorporating quantum computer noise, we simulate the proposed algorithms on both Qiskit and classical computers to gain a deeper understanding of the effectiveness and feasibility of our methods under realistic conditions.
ECO-DKF: Event-Triggered and Certifiable Optimal Distributed Kalman Filter under Unknown Correlations
Authors: Eduardo Sebastián, Eduardo Montijano, Carlos Sagüés
Date: 2023-11-04 00:00:00

This paper presents ECO-DKF, the first Event-Triggered and Certifiable Optimal Distributed Kalman Filter. Our algorithm addresses two major issues inherent to Distributed Kalman Filters: (i) fully distributed and scalable optimal estimation and (ii) reduction of the communication bandwidth usage. The first requires to solve an NP-hard optimisation problem, forcing relaxations that lose optimality guarantees over the original problem. Using only information from one-hop neighbours, we propose a tight Semi-Definite Programming relaxation that allows to certify locally and online if the relaxed solution is the optimum of the original NP-hard problem. In that case, ECO-DKF is optimal in the square error sense under scalability and event-triggered one-hop communications restrictions. Additionally, ECO-DKF is a globally asymptotically stable estimator. To address the second issue, we propose an event-triggered scheme from the relaxed optimisation output. The consequence is a broadcasting-based algorithm that saves communication bandwidth, avoids individual communication links and multiple information exchanges within instants, and preserves the optimality and stability properties of the filter.
Synchronous Observer Design for Inertial Navigation Systems with Almost-Global Convergence
Authors: Pieter van Goor, Tarek Hamel, Robert Mahony
Date: 2023-11-03 00:00:00
Tasks: Position

An Inertial Navigation System (INS) is a system that integrates acceleration and angular velocity readings from an Inertial Measurement Unit (IMU), along with other sensors such as Global Navigation Satellite Systems (GNSS) position, GNSS velocity, and magnetometer, to estimate the attitude, velocity, and position of a vehicle. This paper shows that the INS problem can be analysed using the automorphism group of the extended special Euclidean group: a group we term the extended similarity group . By exploiting this novel geometric framework, we propose a synchronous observer architecture; that is, an observer architecture for which the observer error is stationary if the correction terms are set to zero. In turn, this enables us to derive a modular, or plug-and-play, observer design for INS that allows different sensors to be added or removed depending on what is available in the vehicle sensor suite. We prove both almost-global asymptotic and local exponential stability of the error dynamics for the common scenario of at least IMU and GNSS position. To the authors' knowledge, this is the first non-linear observer design with almost global convergence guarantees or with plug-and-play modular capability. A simulation with extreme initial error demonstrates the almost-global robustness of the system. Real-world capability is demonstrated on data from a fixed-wing UAV, and the solution is compared to the state-of-the-art ArduPilot INS.
A Gronwall Inequality Based Approach to Transient Stability Assessment for Power Grids
Authors: Qian Zhang, Deqiang Gan
Date: 2023-11-03 00:00:00

This paper proposes a novel Gronwall inequality-based method for transient stability assessment for power systems. The challenges of applying such methods to power systems are how to construct the differential inequality and how to treat its nonlinearity. By leveraging partial derivatives, a rotor angle difference inequality model is established, and the difficulty of nonlinearity of this model is solved by piecewise linearization. Based on our method, the upper bound of the rotor angle difference is given analytically, which can be used to estimate the stability boundary, i.e. the critical clearing time (CCT) of power systems. A case study on the IEEE 9-bus system shows the accuracy of the approach in early warning of transient instability for power grids.
Active risk aversion in SIS epidemics on networks
Authors: Anastasia Bizyaeva, Marcela Ordorica Arango, Yunxiu Zhou, Simon Levin, Naomi Ehrich Leonard
Date: 2023-11-03 00:00:00

We present and analyze an actively controlled Susceptible-Infected-Susceptible (actSIS) model of interconnected populations to study how risk aversion strategies, such as social distancing, affect network epidemics. A population using a risk aversion strategy reduces its contact rate with other populations when it perceives an increase in infection risk. The network actSIS model relies on two distinct networks. One is a physical contact network that defines which populations come into contact with which other populations and thus how infection spreads. The other is a communication network, such as an online social network, that defines which populations observe the infection level of which other populations and thus how information spreads. We prove that the model, with these two networks and populations using risk aversion strategies, exhibits a transcritical bifurcation in which an endemic equilibrium emerges. For regular graphs, we prove that the endemic infection level is uniform across populations and reduced by the risk aversion strategy, relative to the network SIS endemic level. We show that when communication is sufficiently sparse, this initially stable equilibrium loses stability in a secondary bifurcation. Simulations show that a new stable solution emerges with nonuniform infection levels.
A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles
Authors: Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay
Date: 2023-10-30 00:00:00
Tasks: Autonomous Vehicles

This work introduces a preference learning method that ensures adherence to given specifications, with an application to autonomous vehicles. Our approach incorporates the priority ordering of Signal Temporal Logic (STL) formulas describing traffic rules into a learning framework. By leveraging Parametric Weighted Signal Temporal Logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons and propose an approach to solve this learning problem. Our approach finds a feasible valuation for the weights of the given PWSTL formula such that, with these weights, preferred signals have weighted quantitative satisfaction measures greater than their non-preferred counterparts. The feasible valuation of weights given by our approach leads to a weighted STL formula that can be used in correct-and-custom-by-construction controller synthesis. We demonstrate the performance of our method with a pilot human subject study in two different simulated driving scenarios involving a stop sign and a pedestrian crossing. Our approach yields competitive results compared to existing preference learning methods in terms of capturing preferences and notably outperforms them when safety is considered.