pwc-archive/papers-with-abstracts huggingface dataset:
Learning Stable Koopman Embeddings for Identification and Control
Authors: Fletcher Fan, Bowen Yi, David Rye, Guodong Shi, Ian R. Manchester
Date: 2024-01-16 00:00:00
Tasks: Imitation Learning

This paper introduces new model parameterizations for learning discrete-time dynamical systems from data via the Koopman operator and studies their properties. Whereas most existing works on Koopman learning do not take into account the stability or stabilizability of the model -- two fundamental pieces of prior knowledge about a given system to be identified -- in this paper, we propose new classes of Koopman models that have built-in guarantees of these properties. These guarantees are achieved through a novel {\em direct parameterization approach} that leads to {\em unconstrained} optimization problems over their parameter sets. {These results rely on the invertibility of the vector fields for autonomous systems and the generalized feedback linearizability (under smooth feedback), respectively.} To explore the representational flexibility of these model sets, we establish the theoretical connections between the stability of discrete-time Koopman embedding and contraction-based forms of nonlinear stability and stabilizability. The proposed approach is illustrated in applications to stable nonlinear system identification and imitation learning via stabilizable models. Simulation results empirically show that the proposed learning approaches outperform prior methods lacking stability guarantees.
Distributed Stackelberg Equilibrium Seeking for Networked Multi-Leader Multi-Follower Games with A Clustered Information Structure
Authors: Yue Chen, Peng Yi
Date: 2024-01-16 00:00:00
Tasks: Decision Making

The Stackelberg game depicts a leader-follower relationship wherein decisions are made sequentially, and the Stackelberg equilibrium represents an expected optimal solution when the leader can anticipate the rational response of the follower. Motivated by control of network systems with two levels of decision-making hierarchy, such as the management of energy networks and power coordination at cellular networks, a networked multi-leaders and multi-followers Stackelberg game is proposed. Due to the constraint of limited information interaction among players, a clustered information structure is assumed that each leader can only communicate with a portion of overall followers, namely its subordinated followers, and also only with its local neighboring leaders. In this case, the leaders cannot fully anticipate the collective rational response of all followers with its local information. To address Stackelberg equilibrium seeking under this partial information structure, we propose a distributed seeking algorithm based on implicit gradient estimation and network consensus mechanisms. We rigorously prove the convergence of the algorithm for both diminishing and constant step sizes under strict and strong monotonicity conditions, respectively. Furthermore, the model and the algorithm can also incorporate linear equality and inequality constraints into the followers' optimization problems, with the approach of the interior point barrier function. Finally, we present numerical simulations in applications to corroborate our claims on the proposed framework.
Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries
Authors: Baozhao Yi, Xinhao Du, Jiawei Zhang, Xiaogang Wu, Qiuhao Hu, Weiran Jiang, Xiaosong Hu, Ziyou Song
Date: 2024-01-16 00:00:00
Tasks: State Estimation

Accurate estimation of the state of charge (SOC) and state of health (SOH) is crucial for the safe and reliable operation of batteries. Voltage measurement bias highly affects state estimation accuracy, especially in Lithium Iron Phosphate (LFP) batteries, which are susceptible due to their flat open-circuit voltage (OCV) curves. This work introduces a bias-compensated algorithm to reliably estimate the SOC and SOH of LFP batteries under the influence of voltage measurement bias. Specifically, SOC and SOH are estimated using the Dual Extended Kalman Filter (DEKF) in the high-slope SOC range, where voltage measurement bias effects are weak. Besides, the voltage measurement biases estimated in the low-slope SOC regions are compensated in the following joint estimation of SOC and SOH to enhance the state estimation accuracy further. Experimental results indicate that the proposed algorithm significantly outperforms the traditional method, which does not consider biases under different temperatures and aging conditions. Additionally, the bias-compensated algorithm can achieve low estimation errors of below 1.5% for SOC and 2% for SOH, even with a 30mV voltage measurement bias. Finally, even if the voltage measurement biases change in operation, the proposed algorithm can remain robust and keep the estimated errors of states around 2%.
Operation Scheme Optimizations to Achieve Ultra-high Endurance (1010) in Flash Memory with Robust Reliabilities
Authors: Yang Feng, Zhaohui Sun, Chengcheng Wang, Xinyi Guo, Junyao Mei, Yueran Qi, Jing Liu, Junyu Zhang, Jixuan Wu, Xuepeng Zhan, Jiezhi Chen
Date: 2024-01-16 00:00:00

Flash memory has been widely adopted as stand-alone memory and embedded memory due to its robust reliability. However, the limited endurance obstacles its further applications in storage class memory (SCM) and to proceed endurance-required computing-in-memory (CIM) tasks. In this work, the optimization strategies have been studied to tackle this concern. It is shown that by adopting the channel hot electrons injection (CHEI) and hot hole injection (HHI) to implement program/erase (PE) cycling together with a balanced memory window (MW) at the high-Vth (HV) mode, impressively, the endurance can be greatly extended to 1010 PE cycles, which is a record-high value in flash memory. Moreover, by using the proposed electric-field-assisted relaxation (EAR) scheme, the degradation of flash cells can be well suppressed with better subthreshold swings (SS) and lower leakage currents (sub-10pA after 1010 PE cycles). Our results shed light on the optimization strategy of flash memory to serve as SCM and implementendurance-required CIM tasks.
Online Simulation at Machine Level: A Systematic Review
Authors: Darius Deubert, Lars Klingel, Andreas Selig
Date: 2024-01-15 00:00:00

The importance of simulation at machine level in industrial environments is steadily increasing especially in the design and commissioning phase. Using models during the operation phase together with the real machine or plant is referred to as online simulation. Online simulation is used for system monitoring, predictive analyses, decision support or online optimization and therefore has various advantages and a wide field of applications. This paper has the aim to characterize online simulation at machine level in industrial automation focusing on key technologies and common applications. Therefore, a set of 65 relevant publications, which are focusing on this subject, is found by database search, expert consultation, and snowballing. As key technological aspects, the used model types, interfaces and platforms, and the aspects of initialization and synchronization are further investigated. The results are interpreted and limitations, knowledge gaps and future prospects are discussed. The potential of online simulation at machine level especially arises due to the increasing availability of component and machine models from the design and commissioning phase, which can be reused for online simulation. Remaining challenges are identified concerning implementation, simulation platforms, model maintenance and especially in the field of synchronization.
Enforcing contraction via data
Authors: Zhongjie Hu, Claudio De Persis, Pietro Tesi
Date: 2024-01-15 00:00:00

We present data-based conditions for enforcing contractivity via feedback control and obtain desired asymptotic properties of the closed-loop system. We focus on unknown nonlinear control systems whose vector fields are expressible via a dictionary of functions and derive data-dependent semidefinite programs whose solution returns the controller that guarantees contractivity. When data are perturbed by disturbances that are linear combinations of sinusoids of known frequencies (but unknown amplitude and phase) and constants, we remarkably obtain conditions for contractivity that do not depend on the magnitude of the disturbances, with imaginable positive consequences for the synthesis of the controller. Finally, we show how to design from data an integral controller for nonlinear systems that achieves constant reference tracking and constant disturbance rejection.
Learning Soft Constrained MPC Value Functions: Efficient MPC Design and Implementation providing Stability and Safety Guarantees
Authors: Nicolas Chatzikiriakos, Kim P. Wabersich, Felix Berkel, Patricia Pauli, Andrea Iannelli
Date: 2024-01-15 00:00:00
Tasks: Model Predictive Control

Model Predictive Control (MPC) can be applied to safety-critical control problems, providing closed-loop safety and performance guarantees. Implementation of MPC controllers requires solving an optimization problem at every sampling instant, which is challenging to execute on embedded hardware. To address this challenge, we propose a framework that combines a tightened soft constrained MPC formulation with supervised learning to approximate the MPC value function. This combination enables us to obtain a corresponding optimal control law, which can be implemented efficiently on embedded platforms. The framework ensures stability and constraint satisfaction for various nonlinear systems. While the design effort is similar to that of nominal MPC, the proposed formulation provides input-to-state stability (ISS) with respect to the approximation error of the value function. Furthermore, we prove that the value function corresponding to the soft constrained MPC problem is Lipschitz continuous for Lipschitz continuous systems, even if the optimal control law may be discontinuous. This serves two purposes: First, it allows to relate approximation errors to a sufficiently large constraint tightening to obtain constraint satisfaction guarantees. Second, it paves the way for an efficient supervised learning procedure to obtain a continuous value function approximation. We demonstrate the effectiveness of the method using a nonlinear numerical example.
Effect of target signals and delays on spatially selective active noise control for open-fitting hearables
Authors: Tong Xiao, Simon Doclo
Date: 2024-01-15 00:00:00

Spatially selective active noise control (ANC) hearables are designed to reduce unwanted noise from certain directions while preserving desired sounds from other directions. In previous studies, the target signal has been defined either as the delayed desired component in one of the reference microphone signals or as the desired component in the error microphone signal without any delay. In this paper, we systematically investigate the influence of delays in different target signals on the ANC performance and provide an intuitive explanation for how the system obtains the desired signal. Simulations were conducted on a pair of open-fitting hearables for localized speech and noise sources in an anechoic environment. The performance was assessed in terms of noise reduction, signal quality and control effort. Results indicate that optimal performance is achieved without delays when the target signal is defined at the error microphone, whereas causality necessitates delays when the target signal is defined at the reference microphone. The optimal delay is found to be the acoustic delay between this reference microphone and the error microphone from the desired source.
Eco-driving Intelligent Systems and Algorithms: A Patent Review
Authors: Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Grace Ma
Date: 2024-01-15 00:00:00
Tasks: energy management, Management

The transportation industry remains a significant contributor to greenhouse gas emissions, highlighting the requirement for intelligent systems to enhance vehicle energy efficiency. The intellectual property rights of developed systems should be protected by patents. However, there is no patent overview of eco-driving intelligent systems. Unlike a scientific article, a patent documentation indicates both novelty and commercialization potential of an inventor. To address this research gap, this paper provides a patent overview of eco-driving intelligent systems and algorithms. 424 patents in the Google Patent database are analyzed. The patent analysis results show that the top three Cooperative Patent Classifications are: Y02T - climate change mitigation technologies related to transportation (50.7%), B60W - Conjoint control of vehicle subunits of different types or different functions (34.4%) and B60L - Propulsion of electrically-propelled vehicles (20.2%). 219 patents were filed after 2016 when deep learning became popular and can be categorized into five groups: vehicle energy management, smart driving, ecological and sustainable driving, fuel consumption reduction, and driving behavior optimization. Furthermore, all 219 patents involve the physical components of the intelligent system and/or novel machine learning/deep learning algorithms. Moreover, over 70% of them are granted by the China National Intellectual Property Administration.
Fully Decentralized Design of Initialization-free Distributed Network Size Estimation
Authors: Donggil Lee, Taekyoo Kim, Seungjoon Lee, Hyungbo Shim
Date: 2024-01-15 00:00:00

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network.
Low-cost, Lightweight Electronic Flow Regulators for Throttling Liquid Rocket Engines
Authors: Vint Lee, Sohom Roy
Date: 2024-01-15 00:00:00

For small-scale liquid rockets, pressure-fed systems are commonly favoured due to their simplicity and low weight. In such systems, accurate regulation of both tank and injector pressures over a wide range of upstream pressures is critical $-$ more accurate regulation allows for higher engine efficiency and minimal tank mass, thus improving flight performance. However, existing methods such as dome-loaded pressure regulators are inflexible, or require extensive characterization to function accurately. These methods also suffer from limited orifice size, droop, and slow reaction times, making them unsuitable for throttling by adjusting pressures in flight, which are increasingly important as propulsively landing rockets become more common. To overcome these challenges, we designed an electronic pressure regulator (eReg), a multi-input multi-output system utilising closed loop feedback to accurately control downstream pressures. Our design is simple, low-cost and robust: with a single ball valve actuated by a motor, we regulate both gaseous pressurant and cryogenic liquid propellant at high flow rates (1.14 kg/s of liquid; 0.39 kg/s of gas) and upstream pressures (310 bar). Using 2 eRegs to regulate propellant tank pressures, and 2 eRegs for regulating propellant flow to the engine, we demonstrated our system's ability, in a static fire test, to regulate pressures accurately (within 0.2 bar) while simultaneously throttling our engine. To the best of our knowledge, this is the first time any undergraduate team has successfully throttled a liquid bipropellant engine.
AMC'24 "Analysis and Synthesis of the Disturbance Observer-based Robust Force Control Systems in State Space"
Authors: Sariyildiz Emre
Date: 2024-01-15 00:00:00

This paper extends the result of my previous papers on the analysis and synthesis of disturbance observer based robust control systems in state space.
Fairness-aware Photovoltaic Generation Limits for Voltage Regulation in Power Distribution Networks using Conservative Linear Approximations
Authors: Rahul K. Gupta, Paprapee Buason, Daniel K. Molzahn
Date: 2024-01-15 00:00:00
Tasks: Fairness, Stochastic Optimization

This paper proposes a framework for fairly curtailing photovoltaic (PV) plants in response to the over-voltage problem in PV-rich distribution networks. The framework imposes PV generation limits to avoid overvoltages. These limits are computed a day ahead of real-time operations by solving an offline stochastic optimization problem using forecasted scenarios for PV generation and load demand. The framework minimizes the overall curtailment while considering fairness by reducing disparities in curtailments among different PV owners. We model the distribution grid constraints using a conservative linear approximation (CLA) of the AC power flow equations which is computed using a set of sampled power injections from the day-ahead predicted scenarios. The proposed framework is numerically validated on a CIGRE benchmark network interfaced with a large number of PV plants. We compare the performance of the proposed framework versus an alternative formulation that does not incorporate fairness considerations. To this end, we assess tradeoffs between fairness, as quantified with the Jain Fairness Index (JFI), and the total curtailed energy.
Delayed finite-dimensional observer-based control of 2D linear parabolic PDEs
Authors: Pengfei Wang, Emilia Fridman
Date: 2024-01-14 00:00:00

Recently, a constructive method was suggested for finite-dimensional observer-based control of 1D linear heat equation, which is robust to input/output delays. In this paper, we aim to extend this method to the 2D case with general time-varying input/output delays (known output delay and unknown input delay) or sawtooth delays (that correspond to network-based control). We use the modal decomposition approach and consider boundary or non-local sensing together with non-local actuation, or Neumann actuation with non-local sensing. To compensate the output delay that appears in the infinite-dimensional part of the closed-loop system, for the first time for delayed PDEs we suggest a vector Lyapunov functional combined with the recently introduced vector Halanay inequality. We provide linear matrix inequality (LMI) conditions for finding the observer dimension and upper bounds on delays that preserve the exponential stability. We prove that the LMIs are always feasible for large enough observer dimension and small enough upper bounds on delays. A numerical example demonstrates the efficiency of our method and shows that the employment of vector Halanay's inequality allows for larger delays than the classical scalar Halanay inequality for comparatively large observer dimension.
Robust Data-Driven Predictive Control for Unknown Linear Time-Invariant Systems
Authors: Kaijian Hu, Tao Liu
Date: 2024-01-14 00:00:00

This paper presents a new robust data-driven predictive control scheme for unknown linear time-invariant systems by using input-state-output or input-output data based on whether the state is measurable. To remove the need for the persistently exciting (PE) condition of a sufficiently high order on pre-collected data, a set containing all systems capable of generating such data is constructed. Then, at each time step, an upper bound of a given objective function is derived for all systems in the set, and a feedback controller is designed to minimize this bound. The optimal control gain at each time step is determined by solving a set of linear matrix inequalities. We prove that if the synthesis problem is feasible at the initial time step, it remains feasible for all future time steps. Unlike current data-driven predictive control schemes based on behavioral system theory, our approach requires less stringent conditions for the pre-collected data, facilitating easier implementation. Further, the proposed predictive control scheme features an infinite prediction horizon, potentially resulting in superior overall control performance compared to existing methods with finite prediction horizons. The effectiveness of our proposed methods is demonstrated through application to an unknown and unstable batch reactor.
A matrix pencil approach to the Morgan's problem
Authors: Dimitris Vafiadis
Date: 2024-01-13 00:00:00

The problem of decoupling a nonsquare state space system by state feedback with singular input transformation is considered. The problem is solved by conducting a finite search for decouplable square systems, appropriately derived from the original. Decoupling feedback on any of these systems defines the decoupling feedback for the original. The issue of fixed poles is also considered and the possibility of selecting the uncontrollable poles is investigated.
An Analytical Framework for Modeling and Synthesizing Malicious Attacks on ACC Vehicles
Authors: Shian Wang
Date: 2024-01-12 00:00:00

While emerging adaptive cruise control (ACC) technologies are making their way into more vehicles, they also expose a vulnerability to potential malicious cyberattacks. Previous research has typically focused on constant or stochastic attacks without explicitly addressing their malicious and covert characteristics. As a result, these attacks may inadvertently benefit the compromised vehicles, inconsistent with real-world scenarios. In contrast, we establish an analytical framework to model and synthesize a range of candidate attacks, offering a physical interpretation from the attacker's standpoint. Specifically, we introduce a mathematical framework that describes mixed traffic scenarios, comprising ACC vehicles and human-driven vehicles (HDVs), grounded in car-following dynamics. Within this framework, we synthesize and integrate a class of false data injection attacks into ACC sensor measurements, influencing traffic flow dynamics. As a first-of-its-kind study, this work provides an analytical characterization of attacks, emphasizing their malicious and stealthy attributes while explicitly accounting for vehicle driving behavior, thereby yielding a set of candidate attacks with physical interpretability. To demonstrate the modeling process, we perform a series of numerical simulations to holistically assess the effects of attacks on car-following dynamics, traffic efficiency, and vehicular fuel consumption. The primary findings indicate that strategically synthesized candidate attacks can cause significant disruptions to the traffic flow while altering the driving behavior of ACC vehicles in a subtle fashion to remain stealthy, which is supported by a series of analytical results.