pwc-archive/papers-with-abstracts huggingface dataset:
Efficient MPC for Emergency Evasive Maneuvers, Part II: Comparative Assessment for Hybrid Control
Authors: Leila Gharavi, Bart De Schutter, Simone Baldi
Date: 2023-10-01 00:00:00
Tasks: Computational Efficiency, Friction, Model Predictive Control

Optimization-based approaches such as Model Predictive Control (MPC) are promising approaches in proactive control for safety-critical applications with changing environments such as automated driving systems. However, the computational complexity of the MPC optimization problem coupled with the need for real-time control in hazardous scenarios is the main bottleneck in realization of automation levels four and five for driving systems. In this paper, we construct hybrid formulations of the nonlinear MPC problem for tracking control during emergency evasive maneuvers and assess their computational efficiency in terms of accuracy and solution time. To hybridize the MPC problem, we combine three hybrid approximations of the prediction model and four approximations of the nonlinear stability and tire saturation constraints and simulate the closed-loop behavior of the resulting controllers during five emergency maneuvers for different prediction horizons. Further, we compare the robustness of the controllers in the presence of friction uncertainty as well to assess the accuracy-time trade-off in cases where the friction of the road is either unknown or has an offset error with respect to the prediction model. This robustness is studied for different levels of friction uncertainty, as well as investigated with respect to the proximity to the vehicle handling limits. We show that the hybridization of the MPC problem is an efficient approach for real-time implementation of MPC during emergency evasive maneuvers, paving the way for implementation of high levels of automation.
Efficient MPC for Emergency Evasive Maneuvers, Part I: Hybridization of the Nonlinear Problem
Authors: Leila Gharavi, Bart De Schutter, Simone Baldi
Date: 2023-10-01 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Despite the extensive application of nonlinear Model Predictive Control (MPC) in automated driving, balancing its computational efficiency with respect to the control performance and constraint satisfaction remains a challenge in emergency scenarios: in such situations, sub-optimal but computationally fast responses are more valuable than optimal responses obtained after long computations. In this paper, we introduce a hybridization approach for efficient approximation of nonlinear vehicle dynamics and non-convex constraints using a hybrid systems modeling framework. Hybridization allows to reformulate the nonlinear MPC problem during emergency evasive maneuvers as a hybrid MPC problem. In this regard, Max-Min-Plus-Scaling (MMPS) hybrid modeling is used to approximate the nonlinear vehicle dynamics. Meanwhile, different formulations for constraint approximation are presented, and various grid-generation methods are compared to solve these approximation problems. Among these, two novel grid types are introduced to structurally include the influence of the system dynamics on the grid point distributions in the state domain. Overall, the work presents and compares three hybrid models and four hybrid constraints for efficient MPC synthesis and offers guidelines for implementation of the presented hybridization framework in other applications.
Mechatronic Generation of Datasets for Acoustics Research
Authors: Austin Lu, Ethaniel Moore, Arya Nallanthighall, Kanad Sarkar, Manan Mittal, Ryan M. Corey, Paris Smaragdis, Andrew Singer
Date: 2023-10-01 00:00:00

We address the challenge of making spatial audio datasets by proposing a shared mechanized recording space that can run custom acoustic experiments: a Mechatronic Acoustic Research System (MARS). To accommodate a wide variety of experiments, we implement an extensible architecture for wireless multi-robot coordination which enables synchronized robot motion for dynamic scenes with moving speakers and microphones. Using a virtual control interface, we can remotely design automated experiments to collect large-scale audio data. This data is shown to be similar across repeated runs, demonstrating the reliability of MARS. We discuss the potential for MARS to make audio data collection accessible for researchers without dedicated acoustic research spaces.
Deriving Loss Function for Value-oriented Renewable Energy Forecasting
Authors: Yufan Zhang, Honglin Wen, Yuexin Bian, Yuanyuan Shi
Date: 2023-10-01 00:00:00

Renewable energy forecasting is the workhorse for efficient energy dispatch. However, forecasts with small mean squared errors (MSE) may not necessarily lead to low operation costs. Here, we propose a forecasting approach specifically tailored for operational purposes, by incorporating operational problems into the estimation of forecast models via designing a loss function. We formulate a bilevel program, where the operation problem is at the lower level, and the forecast model estimation is at the upper level. We establish the relationship between the lower-level optimal solutions and forecasts through multiparametric programming. By integrating it into the upper-level objective for minimizing expected operation cost, we convert the bilevel problem to a single-level one and derive the loss function for training the model. It is proved to be piecewise linear, for linear operation problem. Compared to the commonly used loss functions, e.g. MSE, our approach achieves lower operation costs.
Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions
Authors: Anni Li, Christos G. Cassandras, Wei Xiao
Date: 2023-10-01 00:00:00

This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict. In order to address this challenge, we employ event-triggered Control Barrier Functions (CBFs) to estimate the HDV model online, construct data-driven and state-feedback safety controllers, and transform constrained optimal control problems for CAVs into a sequence of event-triggered quadratic programs. We show that we can ensure collision-free between HDVs and CAVs and demonstrate the robustness and flexibility of our framework on different types of human drivers in lane-changing scenarios while guaranteeing safety with human-in-the-loop interactions.
Analytical Modeling of Parameter Imbalance in Permanent Magnet Synchronous Machines
Authors: Prerit Pramod
Date: 2023-09-30 00:00:00

This paper presents a systematic and comprehensive analysis of the impact of parameter imbalance in permanent magnet synchronous machines. Analytical models that reveal the effects of imbalance are obtained for each parameter. Thereafter, the models are verified for accuracy by comparison with complex simulations that closely represent true machine behavior. Such models may be utilized for developing (general) algorithms for detection, learning and mitigation of the negative effects of parameter imbalance including current (and thus torque) pulsations during real-time operation.
Forced oscillation source localization from generator measurements
Authors: Melvyn Tyloo, Marc Vuffray, Andrey Y. Lokhov
Date: 2023-09-30 00:00:00

Malfunctioning equipment, erroneous operating conditions or periodic load variations can cause periodic disturbances that would persist over time, creating an undesirable transfer of energy across the system -- an effect referred to as forced oscillations. Wide-area oscillations may damage assets, trigger inadvertent tripping or control actions, and be the cause of equipment failure. Unfortunately, for wide-area oscillations, the location, frequency, and amplitude of these forced oscillations may be hard to determine. Recently, a data-driven maximum-likelihood-based method was proposed to perform source localization in transmission grids under wide-area response scenarios. However, this method relies on full PMU coverage and all buses having inertia and damping. Here, we extend this method to realistic scenarios which includes buses without inertia or dumping, such as passive loads and inverter-based generators. Incorporating Kron reduction directly into the maximum likelihood estimator, we are able to identify the location and frequency of forcing applied at both traditional generators and loads.
Optimizing Parameters of the DC Power Flow
Authors: Babak Taheri, Daniel K. Molzahn
Date: 2023-09-30 00:00:00

Many power system operation and planning problems use the DC power flow approximation to address computational challenges from the nonlinearity of the AC power flow equations. The DC power flow simplifies the AC power flow equations to a linear form that relates active power flows to phase angle differences across branches, parameterized by coefficients based on the branches' susceptances. Inspired by techniques for training machine learning models, this paper proposes an algorithm that seeks optimal coefficient and bias parameters to improve the DC power flow approximation's accuracy. Specifically, the proposed algorithm selects the coefficient and bias parameter values that minimize the discrepancy, across a specified set of operational scenarios, between the power flows given by the DC approximation and the power flows from the AC equations. Gradient-based optimization methods like Broyden-Fletcher-Goldfarb-Shanno (BFGS), Limited-Memory BFGS (L-BFGS), and Truncated Newton Conjugate-Gradient (TNC) enable solution of the proposed algorithm for large systems. After an off-line training phase, the optimized parameters are used to improve the accuracy of the DC power flow during on-line computations. Numerical results show several orders of magnitude improvements in accuracy relative to a hot-start DC power flow approximation across a range of test cases.
Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks
Authors: Yanheng Liu, Hongyang Pan, Geng Sun, Aimin Wang, Jiahui Li, Shuang Liang
Date: 2023-09-30 00:00:00
Tasks: Scheduling

Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have the broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this paper, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a K-means operator and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks
Authors: Matthias Franke, Ognjen Stanojev, Lesia Mitridati, Gabriela Hug
Date: 2023-09-30 00:00:00
Tasks: Distributed Optimization, Privacy Preserving

Amidst the worldwide efforts to decarbonize power networks, Local Electricity Markets (LEMs) in distribution networks are gaining importance due to the increased adoption of renewable energy sources and prosumers. Considering that LEMs involve data exchange among independent entities, privacy and cybersecurity are some of the main practical challenges in LEM design. This paper proposes a secure market protocol using innovations from distributed optimization and Secure MultiParty Computation (SMPC). The considered LEM is formulated as an uncertainty-aware joint market for energy and reserves with affine balancing policies. To achieve scalability and enable the use of SMPC, market clearing is solved using the Consensus ADMM algorithm. Subsequently, the data exchange among participants via ADMM iterations is protected using the Shamir secret-sharing scheme to ensure privacy. The market protocol is further reinforced by a secure and verifiable settlement process that uses SMPC and ElGamal commitments to verify market quantities and by a secure recovery scheme for missing network measurements. Finally, the feasibility and performance of the proposed LEM are evaluated on a 15-bus test network.
Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles
Authors: Hongyang Pan, Yanheng Liu, Geng Sun, Junsong Fan, Shuang Liang, Chau Yuen
Date: 2023-09-30 00:00:00
Tasks: Scheduling

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-II with K-means initialization operator and Variable dimension mechanism (NSGA-II-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks.
A geographic information system-based modelling, analysing and visualising of low voltage networks: The potential of demand time-shifting in the power quality improvement
Authors: Tomislav Antic, Tomislav Capuder
Date: 2023-09-30 00:00:00

The challenges of power quality are an emerging topic for the past couple of years due to massive changes occurring in low voltage distribution networks, being even more emphasized in the years marked by the novel COVID-19 disease affecting people's behaviour and energy crisis increasing the awareness and need of end-users energy independence. Both of these phenomena additionally stress the need for changes in distribution networks as the traditional consumption patterns of the end-users are significantly different. To overcome these challenges it is necessary to develop tools and methods that will help Distribution System Operators (DSOs). In this paper, we present a geographic information system (GIS)-based tool that, by using open source technologies, identifies and removes errors both in the GIS data, representing a distribution network, and in the consumption data collected from the smart meters. After processing the initial data, a mathematical model of the network is created, and the impact of different electricity consumption scenarios on power quality (PQ) indicators voltage magnitude, voltage unbalance factor, and total voltage harmonic distortion are calculated. The results of simulations are visualised using a GIS tool, and based on the results, time periods that are most affected by the change of end-users behaviour are detected. The potential of the end-users in the PQ improvement is investigated and an algorithm that shifts consumption to more adequate time periods is implemented. The results show that the pandemic negatively affect all analysed PQ indicators since the change in the average value of PQ disturbances increased both during the hard and post-lockdown period. The time-shifting of consumption shows significant potential in how the end-users can not only reduce their own energy costs but create power quality benefits by reducing all relevant indicators.
Composition of Control Barrier Functions With Differing Relative Degrees for Safety Under Input Constraints
Authors: Pedram Rabiee, Jesse B. Hoagg
Date: 2023-09-30 00:00:00

This paper presents a new approach for guaranteed safety subject to input constraints (e.g., actuator limits) using a composition of multiple control barrier functions (CBFs). First, we present a method for constructing a single CBF from multiple CBFs, which can have different relative degrees. This construction relies on a soft minimum function and yields a CBF whose $0$-superlevel set is a subset of the union of the $0$-superlevel sets of all the CBFs used in the construction. Next, we extend the approach to systems with input constraints. Specifically, we introduce control dynamics that allow us to express the input constraints as CBFs in the closed-loop state (i.e., the state of the system and the controller). The CBFs constructed from input constraints do not have the same relative degree as the safety constraints. Thus, the composite soft-minimum CBF construction is used to combine the input-constraint CBFs with the safety-constraint CBFs. Finally, we present a feasible real-time-optimization control that guarantees that the state remains in the $0$-superlevel set of the composite soft-minimum CBF. We demonstrate these approaches on a nonholonomic ground robot example.
An Adaptation of the AAA-Interpolation Algorithm for Model Reduction of MIMO Systems
Authors: Jared Jonas, Bassam Bamieh
Date: 2023-09-30 00:00:00

We consider the Adaptive Antoulas-Anderson (AAA) rational interpolation algorithm recently developed by Trefethen and co-authors, which can be viewed as a type of moment-matching technique for system realization and approximation. We consider variations on this algorithm that are suitable for model reduction of linear time invariant systems while addressing some of the shortcomings of the block-AAA variant of the algorithm for MIMO systems. In particular, we develop state-space formulas and keep track of the state-space dimension at every step of the adaptive block-AAA algorithm, showing an unfavorable increase of the state dimension. We propose a new low-rank adaptive interpolation algorithm that addresses this shortcoming. Comparative computational results are included for the algorithms above, together with comparisons to balanced reduction.
LQ-OCP: Energy-Optimal Control for LQ Problems
Authors: Logan E. Beaver
Date: 2023-09-29 00:00:00

This article presents a method to automatically generate energy-optimal trajectories for systems with linear dynamics, linear constraints, and a quadratic cost functional (LQ systems). First, using recent advancements in optimal control, we derive the optimal motion primitive generator for LQ systems--this yields linear differential equations that describe all dynamical motion primitives that the optimal system follows. We also derive the optimality conditions where the system switches between motion primitives--a system of equations that are bilinear in the unknown junction time. Finally, we demonstrate the performance of our approach on an energy-minimizing submersible robot with state and control constraints. We compare our approach to an energy-optimizing Linear Quadratic Regulator (LQR), where we learn the optimal weights of the LQR cost function to minimize energy consumption while ensuring convergence and constraint satisfaction. Our approach converges to the optimal solution 6,400% faster than the LQR weight optimization, and that our solution is 350% more energy efficient. Finally, we disturb the initial state of the submersible to show that our approach still finds energy-efficient solutions faster than LQR when the unconstrained solution is infeasible.
Sensor Placement for Flapping Wing Model Using Stochastic Observability Gramians
Authors: Burak Boyacıoğlu, Mahnoush Babaei, Amanuel H. Mamo, Sarah Bergbreiter, Thomas L. Daniel, Kristi A. Morgansen
Date: 2023-09-29 00:00:00

Systems in nature are stochastic as well as nonlinear. In traditional applications, engineered filters aim to minimize the stochastic effects caused by process and measurement noise. Conversely, a previous study showed that the process noise can reveal the observability of a system that was initially categorized as unobservable when deterministic tools were used. In this paper, we develop a stochastic framework to explore observability analysis and sensor placement. This framework allows for direct studies of the effects of stochasticity on optimal sensor placement and selection to improve filter error covariance. Numerical results are presented for sensor selection that optimizes stochastic empirical observability in a bioinspired setting.
PID Optimization Using Lagrangian Mechanics
Authors: Ethan Kou, Majid Moghadam
Date: 2023-09-25 00:00:00

Creating a simulation of a system enables the tuning of control systems without the need for a physical system. In this paper, we employ Lagrangian Mechanics to derive a set of equations to simulate an inverted pendulum on a cart. The system consists of a freely-rotating rod attached to a cart, with the rod's balance achieved through applying the correct forces to the cart. We manually tune the proportional, integral, and derivative gain coefficients of a Proportional Integral Derivative controller (PID) to balance a rod. To further improve PID performance, we can optimize an objective function to find better gain coefficients.
Solvent constraints for biopolymer folding and evolution in extraterrestrial environments
Authors: Ignacio E. Sánchez, Ezequiel A. Galpern, Diego U. Ferreiro
Date: 2023-09-29 00:00:00

We propose that spontaneous folding and molecular evolution of biopolymers are two universal aspects that must concur for life to happen. These aspects are fundamentally related to the chemical composition of biopolymers and crucially depend on the solvent in which they are embedded. We show that molecular information theory and energy landscape theory allow us to explore the limits that solvents impose on biopolymer existence. We consider 54 solvents, including water, alcohols, hydrocarbons, halogenated solvents, aromatic solvents, and low molecular weight substances made up of elements abundant in the universe, which may potentially take part in alternative biochemistries. We find that along with water, there are many solvents for which the liquid regime is compatible with biopolymer folding and evolution. We present a ranking of the solvents in terms of biopolymer compatibility. Many of these solvents have been found in molecular clouds or may be expected to occur in extrasolar planets.