pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance
Authors: Ziqing Zhu, Xiang Gao, Siqi Bu, Ka Wing Chan, Bin Zhou, Shiwei Xia
Date: 2023-10-17 00:00:00

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The dispatch of MGCs is the key challenge to be tackled to ensure their secure and economic operation. Currently, there is a lack of optimization method that can achieve a trade-off among top-priority requirements of MGCs' dispatch, including fast computation speed, optimality, multiple objectives, and risk mitigation against uncertainty. In this paper, a novel Multi-Objective, Risk-Sensitive, and Online Trust Region Policy Optimization (RS-TRPO) Algorithm is proposed to tackle this problem. First, a dispatch paradigm for autonomous MGs in the MGC is proposed, enabling them sequentially implement their self-dispatch to mitigate potential conflicts. This dispatch paradigm is then formulated as a Markov Game model, which is finally solved by the RS-TRPO algorithm. This online algorithm enables MGs to spontaneously search for the Pareto Frontier considering multiple objectives and risk mitigation. The outstanding computational performance of this algorithm is demonstrated in comparison with mathematical programming methods and heuristic algorithms in a modified IEEE 30-Bus Test System integrated with four autonomous MGs.
Analysis of potential flow networks: Variations in transport time with $discrete$, $continuous$, and $selfish$ operation
Authors: Varghese Kurian, Sridharakumar Narasimhan
Date: 2023-10-17 00:00:00
Tasks: continuous-control, Continuous Control

In potential flow networks, the equilibrium flow rates are usually not proportional to the demands and flow control elements are required to regulate the flow. The control elements can broadly be classified into two types - discrete and continuous. Discrete control elements can have only two operational states: fully open or fully closed. On the other hand, continuous control elements may be operated in any intermediate position in addition to the fully open and fully closed states. Naturally, with their increased flexibility, continuous control elements can provide better network performance, but $to~what~extent$? We consider a class of branched networks with a single source and multiple sinks. The potential drop across edges ($\Delta H$) is assumed to be proportional to the $n^{th}$ power of flow rate ($Q$), i.e., $\Delta H=kQ^n$ , ($n>=1$). We define $\textbf{R}$ as the ratio of minimal operational times required to transport a given quantum of material with either type of control element and show that $1\leq \textbf{R}\leq m^{\left(1-1/n\right)}$, where $m$ is the maximum depth of the network. The results point to the role of network topology in the variations in operational time. Further analysis reveals that the selfish operation of a network with continuous control valves has the same bounds on the price of anarchy.
Network-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures
Authors: Nanduni Nimalsiri, Elizabeth Ratnam
Date: 2023-10-17 00:00:00
Tasks: Distributed Optimization

This paper proposes a distributed optimization-based algorithm for electric vehicle (EV) charging and discharging, incorporating EV customer economics and distribution network constraints enforced on an unbalanced distribution grid. Building on a consensus-based alternating direction method of multipliers (ADMM), the algorithm is designed such that EVs coordinate by means of exchanging limited information with their neighboring EVs in a connected communication network. Specifically, an iterative routine is executed, whereby EVs cooperatively determine their charge-discharge profiles that maintain the distribution grid voltages and transformer core temperatures within safe operating limits. Importantly, the algorithm is robust against communication failures potentially arising in real-world implementations. Numerical simulations are conducted to verify the efficacy of the proposed EV charging algorithm in terms of network-aware operation and communication-failure tolerant operation.
Path Following Control of Automated Vehicle Considering Uncertainties and Disturbances with Parametric Varying
Authors: Dan Shen
Date: 2023-10-17 00:00:00
Tasks: Autonomous Driving, Collision Avoidance, Model Predictive Control

Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. Most PFCs did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, and norm-bounded external disturbances, the system states and control matrices are modified.
Neuromorphic Place Cells
Authors: Zhaoqi Chen, Ralph Etienne-Cummings
Date: 2023-10-16 00:00:00

A neuromorphic SLAM system shows potential for more efficient implementation than its traditional counterpart. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, vector cells, and place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells. The system consists of a theta chip with 128 units and an FPGA encoding 4 networks for vector cells formation that provides the capability for tracking on a 11 by 11 place cell grid. Experimental results validate the robustness of our model when suffering from 18% standard deviation from mathematical models induced by variations of analog circuits. We provide a foundation for implementing dynamic neuromorphic SLAM systems for nonhomogeneous mapping and inspirations for the formation of spatial cells in biology.
Stabilization of uncertain linear distributed delay systems with dissipativity constraints
Authors: Qian Feng, Sing Kiong Nguang
Date: 2023-10-16 00:00:00

This paper examines the problem of stabilizing linear distributed delay systems with nonlinear distributed delay kernels and dissipativity constraints. Specifically, the nonlinear distributed kernel includes functions such as polynomials, trigonometric and exponential functions. By constructing a Liapunov-Krasovski\v{i} functional related to the distributed kernels, sufficient conditions for the existence of a state feedback controller which stabilizes the uncertain distributed delay systems with dissipativity constraints are given in terms of linear matrix inequalities (LMIs). In contrast to existing methods, the proposed scenario is less conservative or requires a smaller number of decision variables based on the application of a newly derived integral inequality. Finally, numerical examples are presented to demonstrate the validity and effectiveness of the proposed methodology.
A Tri-Level Optimization Model for Interdependent Infrastructure Network Resilience Against Compound Hazard Events
Authors: Matthew R. Oster, Ilya Amburg, Samrat Chatterjee, Daniel A. Eisenberg, Dennis G. Thomas, Feng Pan, Auroop R. Ganguly
Date: 2023-10-16 00:00:00

Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks -- encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model -- a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.
Current imbalance in dissimilar parallel-connected batteries and the fate of degradation convergence
Authors: Andrew Weng, Hamidreza Movahedi, Clement Wong, Jason B. Siegel, Anna Stefanopoulou
Date: 2023-10-16 00:00:00

This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within a charge or discharge cycle. These dynamics are represented by a first-order equivalent circuit model and validated against experimental data. To demonstrate how current and SOC imbalance leads to cell degradation, we developed a successive update scheme in which the inter-cycle imbalance dynamics update the intra-cycle degradation dynamics, and vice versa. Using this framework, we demonstrate that current imbalance can cause convergent degradation trajectories, consistent with previous reports. However, we also demonstrate that different degradation assumptions, such as those associated with SOC imbalance, may cause divergent degradation. We finally highlight the role of different cell chemistries, including different OCV function nonlinearities, on system behavior, and derive analytical bounds on the SOC imbalance using Lyapunov analysis.
Distributed Differential Graphical Game for Control of Double-Integrator Multi-Agent Systems with Input Delay
Authors: Hossein B. Jond
Date: 2023-10-16 00:00:00

This paper studies cooperative control of noncooperative double-integrator multi-agent systems (MASs) with input delay on connected directed graphs in the context of a differential graphical game (DGG). In the distributed DGG, each agent seeks a distributed information control policy by optimizing an individual local performance index (PI) of distributed information from its graph neighbors. The local PI, which quadratically penalizes the agent's deviations from cooperative behavior (e.g., the consensus here), is constructed through the use of the graph Laplacian matrix. For DGGs for double-integrator MASs, the existing body of literature lacks the explicit characterization of Nash equilibrium actions and their associated state trajectories with distributed information. To address this issue, we first convert the N-player DGG with m communication links into m coupled optimal control problems (OCPs), which, in turn, convert to the two-point boundary-value problem (TPBVP). We derive the explicit solutions for the TPBV that constitute the explicit distributed information expressions for Nash equilibrium actions and the state trajectories associated with them for the DGG. An illustrative example verifies the explicit solutions of local information to achieve fully distributed consensus.
K-SMPC: Koopman Operator-Based Stochastic Model Predictive Control for Enhanced Lateral Control of Autonomous Vehicles
Authors: Jin Sung Kim, Ying Shuai Quan, Chung Choo Chung
Date: 2023-10-16 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control

This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Thus, we use the Koopman operator to represent the nonlinear dynamics of a vehicle in dynamic lane-keeping situations. The Extended Dynamic Mode Decomposition (EDMD) method is adopted to approximate the Koopman operator in a finite-dimensional space for practical implementation. We consider the modeling error of the approximated Koopman operator in the EDMD method. Then, we design K-SMPC to tackle the Koopman modeling error, where the error is handled as a probabilistic signal. The recursive feasibility of the proposed method is investigated with an explicit first-step state constraint by computing the robust control invariant set. A high-fidelity vehicle simulator, i.e., CarSim, is used to validate the proposed method with a comparative study. From the results, it is confirmed that the proposed method outperforms other methods in tracking performance. Furthermore, it is observed that the proposed method satisfies the given constraints and is recursively feasible.
A Stiffness-Oriented Model Order Reduction Method for Low-Inertia Power Systems
Authors: Simon Muntwiler, Ognjen Stanojev, Andrea Zanelli, Gabriela Hug, Melanie N. Zeilinger
Date: 2023-10-16 00:00:00

This paper presents a novel model order reduction technique tailored for power systems with a large share of inverter-based energy resources. Such systems exhibit an increased level of dynamic stiffness compared to traditional power systems, posing challenges for time-domain simulations and control design. Our approach involves rotation of the coordinate system of a linearized system using a transformation matrix derived from the real Jordan canonical form, leading to mode decoupling. The fast modes are then truncated in the rotated coordinate system to obtain a lower-order model with reduced stiffness. Applying the same transformation to the original nonlinear system results in an approximate separation of slow and fast states, which can be truncated to reduce the stiffness. The resulting reduced-order model demonstrates an accurate time-domain performance, the slow eigenvalues of the linearized system are correctly preserved, and a reduction in the model stiffness is achieved, allowing for accurate integration with increased step size. Our methodology is assessed in detail for a 3-bus system with generation units involving grid-forming/following converters and synchronous machines, where it allows for a computational speed-up of up to 100x compared to the original system. Several standard larger test systems are also considered.
Passivity and Decentralized Stability Conditions for Grid-Forming Converters
Authors: Xiuqiang He, Florian Dörfler
Date: 2023-10-15 00:00:00

We prove that the popular grid-forming control, i.e., dispatchable virtual oscillator control (dVOC), also termed complex droop control, exhibits output-feedback passivity in its large-signal model, featuring an explicit and physically meaningful passivity index. Using this passivity property, we derive decentralized stability conditions for the transient stability of dVOC in multi-converter grid-connected systems, beyond prior small-signal stability results. The decentralized conditions are of practical significance, particularly for ensuring the transient stability of renewable power plants under grid disturbances.
Quantitative Stability Conditions for Grid-Forming Converters With Complex Droop Control
Authors: Xiuqiang He, Linbin Huang, Irina Subotić, Verena Häberle, Florian Dörfler
Date: 2023-10-15 00:00:00

In this paper, we analytically study the transient stability of grid-connected converters with grid-forming complex droop control, also known as dispatchable virtual oscillator control. We prove theoretically that complex droop control, as a state-of-the-art grid-forming control, always possesses steady-state equilibria whereas classical droop control does not. We provide quantitative conditions for complex droop control maintaining transient stability (global asymptotic stability) under grid disturbances, which is beyond the well-established local (non-global) stability for classical droop control. For the transient instability of complex droop control, we reveal that the unstable trajectories are bounded, manifesting as limit cycle oscillations. Moreover, we extend our stability results from second-order grid-forming control dynamics to full-order system dynamics that additionally encompass both circuit electromagnetic transients and inner-loop dynamics. Our theoretical results contribute an insightful understanding of the transient stability and instability of complex droop control and offer practical guidelines for parameter tuning and stability guarantees.
Study on the Time Domain Precision Evolution Mechanism of CNC Machine Tool Feed Systems Based on Acceleration and Deceleration Capability Indicator
Authors: Xuesong Wang, Yi Zhou, Dongsheng Zhang
Date: 2023-10-15 00:00:00
Tasks: Philosophy

The escalating demand for high-speed and high-precision machining in machine tool feed system has brought to the forefront the challenge of its design method. Currently, existing methodologies struggle to ascertain compliance with dynamic performance requirements during the design phase, often resulting in either excessive or insufficient design. Therefore, there is an urgent need for research focused on feed system design methods that directly address time domain dynamic precision. The dynamic precision of the feed system is influenced by the motor, mechanical structure, motion processes, and control system. However, existing studies on the impact mechanisms of electromechanical matching on feed system precision often overlook the roles of control and motion processes. This paper innovatively proposes the need to consider the coupling effects among subsystems, directing the optimization design of CNC machine tool feed systems towards time domain dynamic precision. Furthermore, it introduces acceleration and deceleration capability as a key indicator of electromechanical matching. Following the decoupling of control system parameters, this study elucidates the influence mechanisms of electromechanical matching on the overall dynamic performance of the feed system under various motion processes. This research offers a novel design philosophy and theoretical foundation for the optimization of CNC machine tool feed systems.
Exploring Damping Effect of Inner Control Loops for Grid-Forming VSCs
Authors: Liang Zhao, Xiongfei Wang, Zheming Jin
Date: 2023-10-14 00:00:00

This paper presents an analytical approach to explore the damping effect of inner loops on grid-forming converters. First, an impedance model is proposed to characterize the behaviors of inner loops, thereby illustrating their influence on output impedance shaping. Then, based on the impedance representation, the complex torque coefficient method is employed to assess the contribution of inner loops to system damping. The interactions among inner loops, outer loops, and the ac grid are analyzed. It reveals that inner loops shape the electrical damping torque coefficient and consequently influence both synchronous and sub-synchronous oscillation modes. The virtual admittance and current control-based inner-loop scheme is employed to illustrate the proposed analytical approach. The case study comprises the analysis of impedance profiles, the analysis of damping torque contributed by inner loops under various grid strengths, and the comparison between dq-frame and {\alpha}\b{eta}-frame realizations of inner loops. Finally, simulation and experimental tests collaborate with theoretical approaches and findings.
Trajectory Tracking for Tilted Hexarotors with Concurrent Attitude Regulation
Authors: Marco Perin, Massimiliano Bertoni, Giulia Michieletto, Roberto Oboe, Angelo Cenedese
Date: 2023-10-14 00:00:00
Tasks: Position

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.
Deep Nonlinear Adaptive Control for Unmanned Aerial Systems Operating under Dynamic Uncertainties
Authors: Zachary Lamb, Zachary I. Bell, Matthew Longmire, Jared Paquet, Prashant Ganesh, Ricardo Sanfelice
Date: 2023-10-14 00:00:00

Recent literature in the field of machine learning (ML) control has shown promising theoretical results for a Deep Neural Network (DNN) based Nonlinear Adaptive Controller (DNAC) capable of achieving trajectory tracking for nonlinear systems. Expanding on this work, this paper applies DNAC to the Attitude Control System (ACS) of a quadrotor and shows improvement to attitude control performance under disturbed flying conditions where the model uncertainty is high. Moreover, these results are noteworthy for ML control because they were achieved with no prior training data and an arbitrary system dynamics initialization; simply put, the controller presented in this paper is practically modelless, yet yields the ability to force trajectory tracking for nonlinear systems while rejecting significant undesirable model disturbances learned through a DNN. The combination of ML techniques to learn a system's dynamics and the Lyapunov analysis required to provide stability guarantees leads to a controller with applications in safety-critical systems that may undergo uncertain model changes, as is the case for most aerial systems. Experimental findings are analyzed in the final section of this paper, and DNAC is shown to outperform the trajectory tracking capabilities of PID, MRAC, and the recently developed Deep Model Reference Adaptive Control (DMRAC) schemes.