pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Attitude Estimation for Multi-agent Systems on $SO(3)$
Authors: Mouaad Boughellaba, Abdelhamid Tayebi
Date: 2023-04-04 00:00:00
Tasks: Pose Estimation

We consider the problem of distributed attitude estimation of multi-agent systems, evolving on $SO(3)$, relying on individual angular velocity and relative attitude measurements. The interaction graph topology is assumed to be an undirected tree. First, we propose a continuous nonlinear distributed attitude estimation scheme with almost global asymptotic stability guarantees. Thereafter, we proceed with the \textit{hybridization} of the proposed estimation scheme to derive a new hybrid nonlinear distributed attitude estimation scheme enjoying global asymptotic stabilization of the attitude estimation errors to a common constant orientation. In addition, the proposed hybrid attitude estimation scheme is used to solve the problem of pose estimation of $N$-vehicles navigating in a three-dimensional space, with global asymptotic stability guarantees, where the only available measurements are the local relative bearings and the individual linear velocities. Simulation results are provided to illustrate the effectiveness of the proposed estimation schemes.
Uncertainty Quantification for Recursive Estimation in Adaptive Safety-Critical Control
Authors: Max H. Cohen, Makai Mann, Kevin Leahy, Calin Belta
Date: 2023-04-04 00:00:00
Tasks: parameter estimation, Uncertainty Quantification

In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control. The key insight enabling our approach is that the parameter estimate generated by the continuous-time recursive least squares (RLS) algorithm at any point in time is an affine transformation of the initial parameter estimate. This property allows for parameterizing such estimates using objects that are closed under affine transformation, such as zonotopes, and enables the efficient propagation of such set-based estimates as time progresses. We illustrate how such an approach facilitates the synthesis of safety-critical controllers for systems with parametric uncertainty and additive disturbances using control barrier functions, and demonstrate the utility of our approach through illustrative examples.
Bearing-based distributed pose estimation for multi-agent networks
Authors: Mouaad Boughellaba, Abdelhamid Tayebi
Date: 2023-04-04 00:00:00
Tasks: Pose Estimation, Position

In this paper, we address the distributed pose estimation problem for multi-agent systems, where the agents have unknown static positions and time-varying orientations. The interaction graph is assumed to be directed and acyclic with two leaders that have access to their position and orientation. We propose a nonlinear distributed pose estimation scheme relying on individual angular velocity measurements and local relative (time-varying) bearing measurements. The proposed estimation scheme consists of two cascaded distributed observers, an almost globally asymptotically stable (AGAS) attitude observer and an input-to-state stable (ISS) position observer, leading to an overall AGAS distributed localization scheme. Numerical simulation results are presented to illustrate the performance of our proposed distributed pose estimation scheme.
Sampled-Data Controller Synthesis using Dissipative Linear Periodic Jump-Flow Systems with Design Applications
Authors: L. M. Spin, M. C. F. Donkers
Date: 2023-04-04 00:00:00

In this paper, we will propose linear-matrix-inequality-based techniques for the design of sampled-data controllers that render the closed-loop system dissipative with respect to \textcolor{blue}{quadratic supply functions}, which includes passivity and an upper-bound on the system's $\mathcal{H}_\infty$-norm as a special case. To arrive at these results, we model the sampled-data control system as a linear periodic jump-flow system, study dissipativity in terms of differential linear matrix inequalities (DLMIs) and then convert these DLMIs into a single linear matrix inequality. We will present three applications of these synthesis techniques: 1) passivity-based controller synthesis, as found in teleoperations, 2) input-output-response matching of a continuous-time filter with a discrete-time filter (by minimizing the $\mathcal{H}_{\infty}$-norm of a generalized plant) and 3) a sampled-data controller redesign problem, where the objective is to find the best sampled-data controller, in the $\mathcal{H}_{\infty}$-norm sense, for a given continuous-time controller. We will show that synthesising sampled-data controllers leads to better closed-loop system behaviour than using a Tustin discretization of a continuous-time controller.
Unified Behavioral Data-Driven Performance Analysis: A Generalized Plant Approach
Authors: L. M. Spin, C. Verhoek, W. P. M. H. Heemels, N. van de Wouw, R. Tóth
Date: 2023-04-04 00:00:00
Tasks: Form, Sensitivity

In this paper, we present a novel approach to combine data-driven non-parametric representations with model-based representations of dynamical systems. Based on a data-driven form of linear fractional transformations, we introduce a data-driven form of generalized plants. This form can be leveraged to accomplish performance characterizations, e.g., in the form of a mixed-sensitivity approach, and LMI-based conditions to verify finite-horizon dissipativity. In particular, we show how finite-horizon $\ell_2$-gain under weighting filter-based general performance specifications are verified for implemented controllers on systems for which only input-output data is available. The overall effectiveness of the proposed method is demonstrated by simulation examples.
Co-Design for Spectral Coexistence between RIS-aided MIMO Radar and MIMO Communication Systems
Authors: Da Li, Bo Tang, Xuyang Wang, Wenjun Wu, Lei Xue
Date: 2023-04-04 00:00:00

Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements. RIS can improve the performance of radar and communication systems by dynamically modulating the wireless channels. In this paper, we consider the co-design for improving the co-existence between multiple-input-multiple-output (MIMO) radar and MIMO communication system with the aid of RIS.The design purpose is to improve the radar detection performance and guarantee the communication capability. Due to the unimodular constraint on the RIS coefficients and the constant-envelope constraint on the radar transmit waveforms, the associated optimization problem is non-convex.To tackle this problem, we develop a cyclic method based on minorization-maximization, semi-definite programming, and alternating direction method of multipliers. Numerical examples verify the effectiveness of the proposed algorithm.
Proving the Convergence to Limit Cycles using Periodically Decreasing Jacobian Matrix Measures
Authors: Jawher Jerray, Laurent Fribourg
Date: 2023-04-04 00:00:00

Methods based on "(Jacobian) matrix measure" to show the convergence of a dynamical system to a limit cycle (LC), generally assume that the measure is negative everywhere on the LC. We relax this assumption by assuming that the matrix measure is negative "on average" over one period of LC. Using an approximate Euler trajectory, we thus present a method that guarantees the LC existence, and allows us to construct a basin of attraction. This is illustrated on the example of the Van der Pol system.
Barrier Function-based Distributed Symbolic Controller for Multi-Agent Systems
Authors: David Smith Sundarsingh, Ratnangshu Das, Adnane Saoud, Pushpak Jagtap
Date: 2023-04-04 00:00:00

Because of the scalability issues associated with the symbolic controller synthesis approach, employing it in a multi-agent system (MAS) framework becomes difficult. In this paper, we present a novel approach for synthesizing distributed symbolic controllers for MAS, that enforces a local Linear Temporal Logic (LTL) specification on each agent and global safety specifications on the MAS, in a computationally efficient manner by leveraging the concept of control barrier functions (CBF). In addition, we also provide an analysis on the effect of the CBF parameters on the conservatism introduced by our proposed approach in the size and domain of the synthesized controller. The effectiveness of this approach is demonstrated through a comparison with the conventional monolithic symbolic control, using simulation as well as hardware demonstrations.
Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology
Authors: Danilo Saccani, Lorenzo Fagiano, Melanie N. Zeilinger, Andrea Carron
Date: 2023-04-04 00:00:00
Tasks: Model Predictive Control

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from mobile robot swarms, in this paper, we address this problem by optimizing individual agent performance while guaranteeing persistent constraint satisfaction in presence of bounded communication range and time-varying network topology. The approach we propose is a model predictive control (MPC) formulation, building on multi-trajectory MPC (mt-MPC) concepts. To enable plug and play operations when the system is in closed-loop without the need of a request, the proposed MPC scheme predicts two different state trajectories in the same finite horizon optimal control problem. One trajectory drives the system to the desired target, assuming that the network topology will not change in the prediction horizon, while the second one ensures constraint satisfaction assuming a worst-case scenario in terms of new agents joining the network in the planning horizon. Recursive feasibility and stability of the closed-loop system during plug and play operations are shown. The approach effectiveness is illustrated with a numerical simulation.
Moving Obstacle Collision Avoidance via Chance-Constrained MPC with CBF
Authors: Ming Li, Zhiyong Sun, Zirui Liao, Siep Weiland
Date: 2023-04-04 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

Model predictive control (MPC) with control barrier functions (CBF) is a promising solution to address the moving obstacle collision avoidance (MOCA) problem. Unlike MPC with distance constraints (MPC-DC), this approach facilitates early obstacle avoidance without the need to increase prediction horizons. However, the existing MPC-CBF method is deterministic and fails to account for perception uncertainties. This paper proposes a generalized MPC-CBF approach for stochastic scenarios, which maintains the advantages of the deterministic method for addressing the MOCA problem. Specifically, the chance-constrained MPC-CBF (CC-MPC-CBF) technique is introduced to ensure that a user-defined collision avoidance probability is met by utilizing probabilistic CBFs. However, due to the potential empty intersection between the reachable set and the safe region confined by CBF constraints, the CC-MPC-CBF problem can pose challenges in achieving feasibility. To address this issue, we propose a sequential implementation approach that involves solving a standard MPC optimization problem followed by a predictive safety filter optimization, which leads to improved feasibility. Furthermore, we introduce an iterative convex optimization scheme to further expedite the resolution of the predictive safety filter, which results in an efficient approach to tackling the non-convex CC-MPC-CBF problem. We apply our proposed algorithm to a double integrator system for MOCA, and we showcase its resilience to obstacle measurement uncertainties and favorable feasibility properties.
Tractable Identification of Electric Distribution Networks
Authors: Ognjen Stanojev, Lucien Werner, Steven Low, Gabriela Hug
Date: 2023-04-04 00:00:00
Tasks: Computational Efficiency, Network Identification

The identification of distribution network topology and parameters is a critical problem that lays the foundation for improving network efficiency, enhancing reliability, and increasing its capacity to host distributed energy resources. Network identification problems often involve estimating a large number of parameters based on highly correlated measurements, resulting in an ill-conditioned and computationally demanding estimation process. We address these challenges by proposing two admittance matrix estimation methods. In the first method, we use the eigendecomposition of the admittance matrix to generalize the notion of stationarity to electrical signals and demonstrate how the stationarity property can be used to facilitate a maximum a posteriori estimation procedure. We relax the stationarity assumption in the second proposed method by employing Linear Minimum Mean Square Error (LMMSE) estimation. Since LMMSE estimation is often ill-conditioned, we introduce an approximate well-conditioned solution based on eigenvalue truncation. Our quantitative results demonstrate the improvement in computational efficiency compared to the state-of-the-art methods while preserving the estimation accuracy.
State and Parameter Estimation for Affine Nonlinear Systems
Authors: Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2023-04-04 00:00:00
Tasks: Model-based Reinforcement Learning, parameter estimation, reinforcement-learning

Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, model-based reinforcement learning architecture that simultaneously learns and implements an optimal controller while maintaining stability during the learning phase. Using multiplier matrices, a convenient way to search for observer gains is designed along with a controller that learns from simulated experience to ensure stability and convergence of trajectories of the closed-loop system to a neighborhood of the origin. Local uniform ultimate boundedness of the trajectories is established using a Lyapunov-based analysis and demonstrated through simulation results, under mild excitation conditions.
Design and Implementation of a Controller for Hexaglide CNC Machine
Authors: Kaveh Safavigerdini, Aria Alasty, Mohammad Reza Movahhedy
Date: 2023-04-03 00:00:00

The objective of this research is to develop an advanced controller for CNC machines equipped with Hexaglide parallel mechanisms. While traditional CNC machines employ mechanisms with perpendicular axes, accuracy may be compromised when processing high force loads. Parallel mechanisms, such as Hexaglide, have gained attention in the CNC industry due to their ability to maintain precision at high speeds and power. However, the absence of a sophisticated controller for converting G-Code to motion paths has been a significant drawback for CNC machines with parallel mechanisms. This research proposes a solution by introducing a controller that can translate G-Code generated by code software into appropriate motion commands for the Hexaglide mechanism.
Can listening to more neighbours help CAVs be faster and safer?
Authors: Mohit Garg, Mélanie Bouroche
Date: 2023-04-03 00:00:00
Tasks: Autonomous Vehicles

Connected Autonomous Vehicles (CAVs) are widely expected to improve traffic safety and efficiency by exploiting information from surrounding vehicles via V2V communication. A CAV typically adapts its speed based on information from the vehicle it follows. CAVs can also use information from vehicles further ahead within their communication range, and this results in improved traffic safety and efficiency. In mixed traffic scenarios, however, this may not always be possible due to the presence of human-driven vehicles that do not have communication capabilities. Furthermore, as wireless vehicular networks are unreliable, information from other vehicles can be delayed or lost, which brings more challenges for CAVs in utilizing information from multiple leading vehicles. A few studies have investigated the impact of CAVs where they use information from multiple leading vehicles on traffic safety and efficiency, but only in very limited scenarios (i.e., with a very small number of vehicles). In contrast, this paper investigates the impact of CAV car-following control based on multiple leading vehicles information on both mixed traffic safety and efficiency in realistic scenarios in terms of imperfect communication, vehicle modelling, and traffic scenario. Results show that exploiting information from multiple, rather than a single, leading vehicles in CAV controller design further improves both traffic safety and efficiency especially at high penetration rates. In addition to proper tuning of CAV controller parameters (control gains and time headways), the scale of the improvement depends on both market penetration rate (MPR) and communication reliability. A packet error rate (PER) of 70% leads to an increase in traffic efficiency by 4.18% (at 40% MPR) and 12.19% (at 70% MPR), compared to the simple single leading vehicle information based controller.
Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent
Authors: Chongyang Shi, Abhishek N. Kulkarni, Hazhar Rahmani, Jie Fu
Date: 2023-04-03 00:00:00
Tasks: Motion Planning

This paper studies a language-based opacity enforcement in a two-player, zero-sum game on a graph. In this game, player 1 (P1) wins if it can achieve a secret temporal goal described by the language of a finite automaton, no matter what strategy the opponent player 2 (P2) selects. In addition, P1 aims to win while making its goal opaque to a passive observer with imperfect information. However, P2 colludes with the observer to reveal P1's secret whenever P2 cannot prevent P1 from achieving its goal, and therefore, opacity must be enforced against P2. We show that a winning and opacity-enforcing strategy for P1 can be computed by reducing the problem to solving a reachability game augmented with observer's belief states. Furthermore, if such a strategy does not exist, winning for P1 must entail the price of revealing his secret to the observer. We demonstrate our game-theoretic solution of opacity-enforcement control through a small illustrative example and in a robot motion planning problem.
Dark Energy is the Cosmological Quantum Vacuum Energy of Light Particles-The Axion and the Lightest Neutrino
Authors: Hector J. de Vega, Norma G. Sanchez
Date: 2007-01-08 00:00:00

We uncover the general mechanism producing the dark energy(DE). This is only based on well known quantum physics and cosmology. We show that the observed DE originates from the cosmological quantum vacuum of light particles which provides a continuous energy distribution able to reproduce the data. Bosons give positive contributions to the DE while fermions yield negative contributions. As usual in field theory, ultraviolet divergences are subtracted from the physical quantities. The subtractions respect the symmetries of the theory and we normalize the physical quantities to be zero for the Minkowski vacuum. The resulting finite contributions to the energy density and the pressure from the quantum vacuum grow as log a(t) where a(t) is the scale factor, while the particle contributions dilute as 1/a^3(t), as it must be for massive particles. The DE equation of state P = w(z)H turns to be w(z)<-1 with w(z) asymptotically reaching the value -1 from below.A scalar particle can produce the observed DE through its quantum cosmological vacuum provided:(i)its mass is of the order of 10^{-3} eV = 1 meV,(ii) it is very weakly coupled and (iii) it is stable on the time scale of the age of the universe. The axion vacuum thus appears as a natural candidate. The neutrino vacuum (especially the lightest mass eigenstate) can give negative contributions to the DE. We find that w(z=0) is slightly below -1 by an amount ranging from [-1.5 10^{-3}] to [-8 10^{-3}] and we predict the axion mass to be in the range between 4 and 5 meV. We find that the universe will expand in the future faster than the de Sitter universe, as an exponential in the square of the cosmic time. DE arises from the quantum vacua of light particles in FRW cosmological space time in an analogous way to the Casimir effect in Minkowski spacetime with non trivial boundaries.
A photo-click thiol-ene collagen-based hydrogel platform for skeletal muscle tissue engineering
Authors: Roisin Holmes, Xuebin B. Yang, David J. Wood, Giuseppe Tronci
Date: 2023-03-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

UV-cured collagen-based hydrogels hold promise in skeletal muscle regeneration due to their soft elastic properties and porous architecture. However, the complex triple helix conformation of collagen and environmental conditions, i.e. molecular oxygen, pose risks to reaction controllability, wet-state integrity and reproducibility. To address this challenge, a photo-click hydrogel platform is presented through an oxygen-insensitive thiol-ene reaction between 2-iminothiolane (2IT)-functionalized type I collagen and multi-arm, non-homopolymerizable norbornene-terminated polyethylene glycol (PEG). UV-induced network formation is demonstrated by oscillatory time sweeps on the reacting thiol-ene mixture, so that significantly increased storage moduli are measured and adjusted depending on the photo-initiator concentration. Variations in PEG functionality (4-arm and 8-arm) and PEG content generate hydrogels with skeletal muscle native stiffness (Ec= 1.3-11.5 kPa), diffusion-controlled swelling behavior and erosion-driven degradability. In vitro, no cytotoxic effect is detected on C2C12 murine myoblasts, while myogenic differentiation is successfully accomplished on hydrogel-seeded cells in low serum culture medium. In vivo, 7-day subcutaneous implantation of selected thiol-ene hydrogel in rats reveal higher cell infiltration, blood vessel formation, and denser tissue interface compared to a clinical gold standard collagen matrix (Mucograft, Geistlich). These results therefore support the applicability and further development of this hydrogel platform for skeletal muscle regeneration.
Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds
Authors: Junlong Feng, Sokbae Lee
Date: 2023-04-04 00:00:00

We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a novel confidence set under an independence restriction. This confidence set is computationally simple and robust to weak instruments, nonlinearity, and partial identification. The validity of the confidence set is guaranteed by our new results on the joint limiting distribution of the independence test by Chatterjee (2021). These results together with the confidence set may have applications beyond welfare analysis. Monte Carlo simulations and two empirical applications on gasoline and food demand demonstrate the effectiveness of our method.
Heterogeneity-robust granular instruments
Authors: Eric Qian
Date: 2023-04-03 00:00:00

Granular instrumental variables (GIV) has experienced sharp growth in empirical macro-finance. The methodology's rise showcases granularity's potential for identification across many economic environments, like the estimation of spillovers and demand systems. I propose a new estimator--called robust granular instrumental variables (RGIV)--that enables studying unit-level heterogeneity in spillovers. Unlike existing methods that assume heterogeneity is a function of observables, RGIV leaves heterogeneity unrestricted. In contrast to the baseline GIV estimator, RGIV allows for unknown shock variances and equal-sized units. Applied to the Euro area, I find strong evidence of country-level heterogeneity in sovereign yield spillovers.