pwc-archive/papers-with-abstracts huggingface dataset:
State and parameter estimation for retinal laser treatment
Authors: Viktoria Kleyman, Manuel Schaller, Mario Mordmuller, Mitsuru Wilson, Ralf Brinkmann, Karl Worthmann, Matthias A. Muller
Date: 2022-03-23 00:00:00
Tasks: Model Predictive Control, parameter estimation

Adequate therapeutic retinal laser irradiation needs to be adapted to the local absorption. This leads to time-consuming treatments as the laser power needs to be successively adjusted to avoid under- and overtreatment caused by too low or too high temperatures. Closed-loop control can overcome this burden by means of temperature measurements. To allow for model predictive control schemes, the current state and the spot-dependent absorption need to be estimated. In this paper, we thoroughly compare moving horizon estimator (MHE) and extended Kalman filter (EKF) designs for joint state and parameter estimation. We consider two different scenarios, the estimation of one or two unknown absorption coefficients. For one unknown parameter, both estimators perform very similar. For two unknown parameters, we found that the MHE benefits from active parameter constraints at the beginning of the estimation, whereas after a settling time both estimators perform again very similar as long as the parameters are inside the considered parameter bounds.
A Levenberg-Marquardt algorithm for sparse identification of dynamical systems
Authors: Mark Haring, Esten Ingar Grøtli, Signe Riemer-Sørensen, Katrine Seel, Kristian Gaustad Hanssen
Date: 2022-03-23 00:00:00

Low complexity of a system model is essential for its use in real-time applications. However, sparse identification methods commonly have stringent requirements that exclude them from being applied in an industrial setting. In this paper, we introduce a flexible method for the sparse identification of dynamical systems described by ordinary differential equations. Our method relieves many of the requirements imposed by other methods that relate to the structure of the model and the data set, such as fixed sampling rates, full state measurements, and linearity of the model. The Levenberg-Marquardt algorithm is used to solve the identification problem. We show that the Levenberg-Marquardt algorithm can be written in a form that enables parallel computing, which greatly diminishes the time required to solve the identification problem. An efficient backward elimination strategy is presented to construct a lean system model.
Long hauling eco-driving: heavy-duty trucks operational modes control with integrated road slope preview
Authors: Gustavo R. Gonçalves da Silva, Mircea Lazar
Date: 2022-03-23 00:00:00

In this paper, a complete eco-driving strategy for heavy-duty trucks (HDT) based on a finite number of driving modes with corresponding gear shifting is developed to cope with different route events and with road slope data. The problem is formulated as an optimal control problem with respect to fuel consumption and trip duration, and solved using a Pontryagin minimum principle (PMP) algorithm for a path search problem, such that computations can be carried out online, in real-time. The developed eco-driving assistance system (EDAS) provides a velocity profile and a sequence of driving modes (and gears) recommendation to the driver, without actively controlling the HDT (human in the loop) and, in practice, allows contextual feedback incorporation from the driver for safety. Simulation results show that the developed methodology is able to provide a velocity profile for a complete route based on known road events and slope information while satisfying all truck operational constraints.
A Fast Diagnostic to Inform Screening of Discarded or Retired Batteries
Authors: Joseph A. Drallmeier, Clement Wong, Charles E. Solbrig, Jason B. Siegel, Anna G. Stefanopoulou
Date: 2022-03-23 00:00:00
Tasks: Diagnostic

With the increased pervasiveness of Lithium-ion batteries, there is growing concern for the amount of retired batteries that will be entering the waste stream. Although these batteries no longer meet the demands of their first application, many still have a significant portion of their initial capacity remaining for use in secondary applications. Yet, direct repurposing is generally not possible and each cell in a battery must be evaluated, increasing the cost of the repurposed packs due to the time intensive screening process. In this paper, a rapid assessment of the internal resistance of a cell is proposed. First, this method of measuring the resistance is completed on cells from twelve retired battery packs and one fresh pack using a hybrid pulse power characterization (HPPC) test as a benchmark for the analysis. Results from these tests show relatively constant resistance measurements across mid to high terminal voltages, allowing this metric to be independent of state of charge (SOC). Then, the relation between internal resistance and capacity across the various packs is discussed. Initial experimental results from this study show a correlation between internal resistance and capacity which can be approximated with a linear fit, suggesting internal resistance measurements taken above a threshold cell terminal voltage may be a suitable initial screening metric for the capacity of retired cells without knowledge of the SOC.
Values of Coordinated Residential Space Heating in Demand Response Provision
Authors: Zihang Dong, Xi Zhang, Goran Strbac
Date: 2022-03-23 00:00:00

Demand-side response from space heating in residential buildings can potentially provide a huge amount of flexibility for the power system, particularly with deep electrification of the heat sector. In this context, this paper presents a novel distributed control strategy to coordinate space heating across numerous residential households with diversified thermal parameters. By employing an iterative algorithm under the game-theoretical framework, each household adjusts its own heating schedule through demand shift and thermal comfort compensation with the purpose of achieving individual cost savings, whereas the aggregate peak demand is effectively shaved on the system level. Additionally, an innovative thermal comfort model which considers both the temporal and spatial differences in customised thermal comfort requirements is proposed. Through a series of case studies, it is demonstrated that the proposed space heating coordination strategy can facilitate effective energy arbitrage for individual buildings, driving a 13.96% reduction in system operational cost and 28.22% peak shaving. Moreover, the superiority of the proposed approach in thermal comfort maintenance is numerically analysed based on the proposed thermal comfort quantification model.
Energy Efficient Placement of ML-Based Services in IoT Networks
Authors: Mohammed M. Alenazi, Barzan A. Yosuf, Sanaa H. Mohamed, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2022-03-23 00:00:00

The Internet of Things (IoT) is gaining momentum in its quest to bridge the gap between the physical and the digital world. The main goal of the IoT is the creation of smart environments and self-aware things that help to facilitate a variety of services such as smart transport, climate monitoring, e-health, etc. Huge volumes of data are expected to be collected by the connected sensors/things, which in traditional cases are processed centrally by large data centers in the core network that will inevitably lead to excessive transportation power consumption as well as added latency overheads. Instead, fog computing has been proposed by researchers from industry and academia to extend the capability of the cloud right to the point where the data is collected at the sensing layer. This way, primitive tasks that can be hosted in IoT sensors do not need to be sent all the way to the cloud for processing. In this paper we propose energy efficient embedding of machine learning (ML) models over a cloud-fog network using a Mixed Integer Linear Programming (MILP) optimization model. We exploit virtualization in our framework to provide service abstraction of Deep Neural Networks (DNN) layers that can be composed into a set of VMs interconnected by virtual links. We constrain the number of VMs that can be processed at the IoT layer and study the impact on the performance of the cloud fog approach.
Koopman-Based Neural Lyapunov Functions for General Attractors
Authors: Shankar A. Deka, Alonso M. Valle, Claire J. Tomlin
Date: 2022-03-23 00:00:00

Koopman spectral theory has grown in the past decade as a powerful tool for dynamical systems analysis and control. In this paper, we show how recent data-driven techniques for estimating Koopman-Invariant subspaces with neural networks can be leveraged to extract Lyapunov certificates for the underlying system. In our work, we specifically focus on systems with a limit-cycle, beyond just an isolated equilibrium point, and use Koopman eigenfunctions to efficiently parameterize candidate Lyapunov functions to construct forward-invariant sets under some (unknown) attractor dynamics. Additionally, when the dynamics are polynomial and when neural networks are replaced by polynomials as a choice of function approximators in our approach, one can further leverage Sum-of-Squares programs and/or nonlinear programs to yield provably correct Lyapunov certificates. In such a polynomial case, our Koopman-based approach for constructing Lyapunov functions uses significantly fewer decision variables compared to directly formulating and solving a Sum-of-Squares optimization problem.
Enhanced Contour Tracking: a Time-Varying Internal Model Principle-Based Approach
Authors: Yue Cao, Zhen Zhang
Date: 2022-03-23 00:00:00
Tasks: Position

Contour tracking plays a crucial role in multi-axis motion control systems, and it requires both multi-axial contouring as well as standard servo performance in each axis. Among the existing contouring control methods, the cross coupled control (CCC) lacks of an asymptotical tracking performance for general contours, and the task coordinate frame (TCF) control usually leads to system nonlinearity, and by design is not well-suited for multi-axis contour tracking. Here we propose a novel time-varying internal model principle-based contouring control (TV-IMCC) methodology to enhance contour tracking performance with both axial and contour error reduction. The proposed TV-IMCC is twofold, including an extended position domain framework with master-slave structures for contour regulation, and a time-varying internal model principle-based controller for each axial tracking precision improvement. Specifically, a novel signal conversion algorithm is proposed with the extended position domain framework, hence the original n-axis contouring problem can be decoupled into (n-1) two-axis master-slave tracking problems in the position domain, and the class of contour candidates can be extended as well. With this, the time-varying internal model principle-based control method is proposed to deal with the time-varying dynamics in the axial systems resulted from the transformation between the time and position domains. Furthermore, the stability analysis is provided for the closed-loop system of the TV-IMCC. Various simulation and experimental results validate the TV-IMCC with enhanced contour tracking performance compared with the existing methods. Moreover, there is no strict requirement on the precision of the master axis, therefore a potential application of the TV-IMCC is multi-axis macro-micro motion systems.
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach
Authors: Daniel Tabas, Baosen Zhang
Date: 2022-03-23 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time. Offline (explicit) solutions for MPC attempt to alleviate real time computational challenges using either multiparametric programming or machine learning. The multiparametric approaches are typically applied to linear or quadratic MPC problems, while learning-based approaches can be more flexible and are less memory-intensive. Existing learning-based approaches offer significant speedups, but the challenge becomes ensuring constraint satisfaction while maintaining good performance. In this paper, we provide a neural network parameterization of MPC policies that explicitly encodes the constraints of the problem. By exploring the interior of the MPC feasible set in an unsupervised learning paradigm, the neural network finds better policies faster than projection-based methods and exhibits substantially shorter solve times. We use the proposed policy to solve a robust MPC problem, and demonstrate the performance and computational gains on a standard test system.
Joint Covering Congestion Rents in Multi-area Power Systems Considering Loop Flow Effects
Authors: Hao liu, Ye Guo, Haitian Liu, Hongbin Sun
Date: 2022-03-23 00:00:00
Tasks: Scheduling

We consider the problem of how multiple areas should jointly cover congestion rents of internal and tie-lines in an interconnected power system. A key issue of our concern is the loop flow problem, which represents discrepancies between scheduled and actual power flow distributions because electric power does not always flow along the most direct paths of transactions. We employ generalized coordinated transaction scheduling (GCTS) for interchange scheduling, which can eliminate dispatch errors caused by loop flow effects and asymptotically converge to the joint economic dispatch (JED) under ideal assumptions. Subsequently, distributed algorithms are proposed for each area to recover multipliers of the global GCTS model, as well as quantifying and pricing its contribution to line congestions. Thereby, all areas and interface bids can jointly cover congestion rents of internal and tie-lines with their merchandise surpluses and profits. Simulations demonstrate the effectiveness of the proposed approach of LMP recovery and joint covering congestion rents.
Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions
Authors: Ehsan Sabouni, Christos G. Cassandras, Wei Xiao, Nader Meskin
Date: 2022-03-22 00:00:00

We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be solved through a combination of tractable optimal control problem formulations and the use of Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved on line over discrete time steps. However, the feasibility of each such QP cannot be guaranteed over every time step. To overcome this limitation, we develop an event-driven approach such that the next QP is triggered by properly defined events and show that this approach can eliminate infeasible cases due to time-driven inter-sampling effects. Simulation examples show how overall infeasibilities can be significantly reduced with the proposed event-triggering scheme, while also reducing the need for communication among CAVs without compromising performance.
Time-domain Generalization of Kron Reduction
Authors: Manish K. Singh, Sairaj Dhople, Florian Dorfler, Georgios B. Giannakis
Date: 2022-03-22 00:00:00
Tasks: Domain Generalization

Kron reduction is a network-reduction method that eliminates nodes with zero current injections from electrical networks operating in sinusoidal steady state. In the time domain, the state-of-the-art application of Kron reduction has been in networks with transmission lines that have constant R/L ratios. This paper considers RL networks without such restriction and puts forth a provably exact time-domain generalization of Kron reduction. Exemplifying empirical tests on a wye-delta network are provided to validate the analytical results.
Energy-optimal Three-dimensional Path-following Control of Autonomous Underwater Vehicles under Ocean Currents
Authors: Niankai Yang, Chao Shen, Matthew Johnson-Roberson, Jing Sun
Date: 2022-03-22 00:00:00

This paper presents a three-dimensional (3D) energy-optimal path-following control design for autonomous underwater vehicles subject to ocean currents. The proposed approach has a two-stage control architecture consisting of the setpoint computation and the setpoint tracking. In the first stage, the surge velocity, heave velocity, and pitch angle setpoints are optimized by minimizing the required vehicle propulsion energy under currents, and the line-of-sight (LOS) guidance law is used to generate the yaw angle setpoint that ensures path following. In the second stage, two model predictive controllers are designed to control the vehicle motion in the horizontal and vertical planes by tracking the optimal setpoints. The proposed controller is compared with a conventional LOS-based control that maintains zero heave velocity relative to the current (i.e., relative heave velocity) and derives pitch angle setpoint using LOS guidance to reach the desired depth. Through simulations, we show that the proposed approach can achieve more than 13% energy saving on a lawnmower-type and an inspection mission under different ocean current conditions. The simulation results demonstrate that allowing motions with non-zero relative heave velocity improves energy efficiency in 3D path-following applications.
Data-Driven Optimal Control of Affine Systems: A Linear Programming Perspective
Authors: Andrea Martinelli, Matilde Gargiani, Marina Draskovic, John Lygeros
Date: 2022-03-22 00:00:00
Tasks: Data Driven Optimal Control, LEMMA

In this letter, we discuss the problem of optimal control for affine systems in the context of data-driven linear programming. First, we introduce a unified framework for the fixed point characterization of the value function, Q-function and relaxed Bellman operators. Then, in a model-free setting, we show how to synthesize and estimate Bellman inequalities from a small but sufficiently rich dataset. To guarantee exploration richness, we complete the extension of Willem's fundamental lemma to affine systems.
Bearing-Based Formation Control with Optimal Motion Trajectory
Authors: Zili Wang, Sean B. Andersson, Roberto Tron
Date: 2022-03-22 00:00:00

Bearing-based distributed formation control is attractive because it can be implemented using vision-based measurements to achieve a desired formation. Gradient-descent-based controllers using bearing measurements have been shown to have many beneficial characteristics, such as global convergence, applicability to different graph topologies and workspaces of arbitrary dimension, and some flexibility in the choice of the cost. In practice, however, such controllers typically yield convoluted paths from their initial location to the final position in the formation. In this paper we propose a novel procedure to optimize gradient-descent-based bearing-based formation controllers to obtain shorter paths. Our approach is based on the parameterization of the cost function and, by extension, of the controller. We form and solve a nonlinear optimization problem with the sum of path lengths of the agent trajectories as the objective and subject to the original equilibria and global convergence conditions for formation control. Our simulation shows that the parameters can be optimized from a very small number of training samples (1 to 7) to straighten the trajectory by around 16% for a large number of random initial conditions for bearing-only formation. However, in the absence of any range information, the scale of the formation is not fixed and this optimization may lead to an undesired compression of the formation size. Including range measurements avoids this issue and leads to further trajectories straightening by 66%.
Adaptive movement strategy may promote biodiversity in the rock-paper-scissors model
Authors: J. Menezes, M. Tenorio, E. Rangel
Date: 2022-03-22 00:00:00

We study the role of the adaptive movement strategy in promoting biodiversity in cyclic models described by the rock-paper-scissors game rules. We assume that individuals of one out of the species may adjust their movement to escape hostile regions and stay longer in their comfort zones. Running a series of stochastic simulations, we calculate the alterations in the spatial patterns and population densities in scenarios where not all organisms are physically or cognitively conditioned to perform the behavioural strategy. Although the adaptive movement strategy is not profitable in terms of territorial dominance for the species, it may promote biodiversity. Our findings show that if all individuals are apt to move adaptively, coexistence probability increases for intermediary mobility. The outcomes also show that even if not all individuals can react to the signals received from the neighbourhood, biodiversity is still benefited, but for a shorter mobility range. We find that the improvement in the coexistence conditions is more accentuated if organisms adjust their movement intensely and can receive sensory information from longer distances. We also discover that biodiversity is slightly promoted for high mobility if the proportion of individuals participating in the strategy is low. Our results may be helpful for biologists and data scientists to understand adaptive process learning in system biology.
Precise sinusoidal signal extraction from noisy waveform in vibration calibration
Authors: Tomofumi Shimoda, Wataru Kokuyama, Hideaki Nozato
Date: 2022-03-23 00:00:00

Precise extraction of sinusoidal vibration parameters is essential for the dynamic calibration of vibration sensors, such as accelerometers. However, several standard methods have not yet been optimized for large background noise. In this work, signal processing methods to extract small vibration signals from noisy data in the case of accelerometer calibration is discussed. The results show that spectral leakage degrades calibration accuracy. Three methods based on the use of a filter, window function, and numerical differentiation are investigated with theoretical calculations, simulations, and experiments. These methods can effectively reduce the contribution of the calibration system noise. The uncertainty of micro vibration calibration in the National Metrology Institute of Japan is reduced by two orders of magnitudes using the proposed methods. The theoretical analyses in this work can lay the foundation for the optimization of signal processing in vibration calibration, and can be applied to other dynamic calibration fields.