pwc-archive/papers-with-abstracts huggingface dataset:
CEG4N: Counter-Example Guided Neural Network Quantization Refinement
Authors: João Batista P. Matos Jr., Iury Bessa, Edoardo Manino, Xidan Song, Lucas C. Cordeiro
Date: 2022-07-09 00:00:00
Tasks: Quantization

Neural networks are essential components of learning-based software systems. However, their high compute, memory, and power requirements make using them in low resources domains challenging. For this reason, neural networks are often quantized before deployment. Existing quantization techniques tend to degrade the network accuracy. We propose Counter-Example Guided Neural Network Quantization Refinement (CEG4N). This technique combines search-based quantization and equivalence verification: the former minimizes the computational requirements, while the latter guarantees that the network's output does not change after quantization. We evaluate CEG4N~on a diverse set of benchmarks, including large and small networks. Our technique successfully quantizes the networks in our evaluation while producing models with up to 72% better accuracy than state-of-the-art techniques.
Reinforcement Learning$\unicode{x2013}$Based Transient Response Shaping for Microgrids
Authors: Ashwin Venkataramanan, Ali Mehrizi-Sani
Date: 2022-07-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work explores the usage of a supplementary controller for improving the transient performance of inverter$\unicode{x2013}$based resources (IBR) in microgrids. The supplementary controller is trained using a reinforcement learning (RL)$\unicode{x2013}$based algorithm to minimize transients in a power converter connected to a microgrid. The controller works autonomously to issue adaptive, intermediate set points based on the current state and trajectory of the observed or tracked variable. The ability of the designed controller to mitigate transients is verified on a medium voltage test system using PSCAD/EMTDC.
Multi-agent systems with CBF-based controllers -- collision avoidance and liveness from instability
Authors: Mrdjan Jankovic, Mario Santillo, Yan Wang
Date: 2022-07-11 00:00:00
Tasks: Collision Avoidance

Assuring system stability is typically a major control design objective. In this paper, we present a system where instability provides a crucial benefit. We consider multi-agent collision avoidance using Control Barrier Functions (CBF) and study trade-offs between safety and liveness -- the ability to reach a destination without large detours or gridlock. We compare two standard decentralized policies, with only the local (host) control available, to co-optimization policies (PCCA and CCS) where everyone's (virtual) control action is available. The co-optimization policies compute control for everyone even though they lack information about others' intentions. For comparison, we use a Centralized, full information policy as the benchmark. One contribution of this paper is proving feasibility for the Centralized, PCCA, and CCS policies. Monte Carlo simulations show that decentralized, host-only control policies and CCS lack liveness while the PCCA policy performs as well as the Centralized. Next, we explain the observed results by considering two agents negotiating the passing order through an intersection. We show that the structure and stability of the resulting equilibria correlates with the observed propensity to gridlock -- the policies with unstable equilibria avoid gridlocks while those with stable ones do not.
State estimation with the Interacting Multiple Model (IMM) method
Authors: Sebastian Dingler
Date: 2022-07-11 00:00:00
Tasks: State Estimation, valid

For model-based estimation methods, the modeling that is as close to reality as possible makes a vital estimation result. In simple applications, it is sufficient to model a system with a single state space model. However, there are applications in which a system changes its behavior deterministically or stochastically. A previously defined model then describes the behavior of the system only inaccurately or is even no longer valid. The state of the art is to use more than one system models in parallel and to derive a suitable system estimate from them. In the literature, this is generally referred to as the Multiple Model (MM) method. Depending on the application and requirements, different methods exist for this purpose, which determine a single state estimate from a set of models. A frequently used representative of these methods is the Interacting Multiple Model (IMM) method which will be presented in this paper.
Risk assessment and optimal allocation of security measures under stealthy false data injection attacks
Authors: Sribalaji C. Anand, André M. H. Teixeira, Anders Ahlén
Date: 2022-07-11 00:00:00

This paper firstly addresses the problem of risk assessment under false data injection attacks on uncertain control systems. We consider an adversary with complete system knowledge, injecting stealthy false data into an uncertain control system. We then use the Value-at-Risk to characterize the risk associated with the attack impact caused by the adversary. The worst-case attack impact is characterized by the recently proposed output-to-output gain. We observe that the risk assessment problem corresponds to an infinite non-convex robust optimization problem. To this end, we use dissipative system theory and the scenario approach to approximate the risk-assessment problem into a convex problem and also provide probabilistic certificates on approximation. Secondly, we consider the problem of security measure allocation. We consider an operator with a constraint on the security budget. Under this constraint, we propose an algorithm to optimally allocate the security measures using the calculated risk such that the resulting Value-at-risk is minimized. Finally, we illustrate the results through a numerical example. The numerical example also illustrates that the security allocation using the Value-at-risk, and the impact on the nominal system may have different outcomes: thereby depicting the benefit of using risk metrics.
On the Convergence of the Backward Reachable Sets of Robust Controlled Invariant Sets For Discrete-time Linear Systems
Authors: Zexiang Liu, Necmiye Ozay
Date: 2022-07-11 00:00:00

This paper considers discrete-time linear systems with bounded additive disturbances, and studies the convergence properties of the backward reachable sets of robust controlled invariant sets (RCIS). Under a simple condition, we prove that the backward reachable sets of an RCIS are guaranteed to converge to the maximal RCIS in Hausdorff distance, with an exponential convergence rate. When all sets are represented by polytopes, this condition can be checked numerically via a linear program. We discuss how the developed condition generalizes the existing conditions in the literature for (controlled) invariant sets of systems without disturbances (or without control inputs).
Performance Bounds for Cooperative Localisation in the Starlink Network
Authors: Calum Spring-Turner, Raj Thilak Rajan
Date: 2022-07-11 00:00:00
Tasks: Astronomy, Collision Avoidance

Mega-constellations in Low Earth Orbit have the potential to revolutionise worldwide internet access. The concomitant potential of these mega-constellations to impact space sustainability, however, has prompted concern from space actors as well as provoking concern in the ground-based astronomy community. Increasing the knowledge of the orbital state of satellites in mega-constellations improves space situations awareness, reducing the need for collision avoidance manoeuvres and allowing astronomers to prepare better observational mitigation strategies. In this paper, we create a model of Phase 1 of Starlink, one of the more well-studied megaconstellations, and investigate the potential of cooperative localisation using time-ofarrival measurements from the optical inter-satellite links in the constellation. To this end, we study the performance of any unbiased estimator for localisation, by calculating the instantaneous Cram$\acute{\text{e}}$r-Rao bound for two situations; one in which inter-satellite measurements and measurements from ground stations were considered, and one in which only relative navigation from inter-satellite measurements were considered. Our results show that localisation determined from a combination of inter-satellite measurements and ground stations can have at best an an average RMSE of approximately 10.15 metres over the majority of a satellite's orbit. Relative localisation using only inter-satellite measurements has a slightly poorer performance with an average RMSE of 10.68 metres. The results show that both anchored and anchorless inter-satellite cooperative localisation are dependent on the constellation's geometry and the characteristics of the inter-satellite links, both of which could inform the use of relative navigation in large satellite constellations in future.
Estimating the Future Need of Balancing Power Based on Long-Term Power System Market Simulations
Authors: Henrik Nordström, Lennart Söder, Robert Eriksson
Date: 2022-07-11 00:00:00

With increasing penetration of variable renewable energy sources (vRES) and a higher rate of electrification in the society, there will be future challenges in maintaining a continuous balance between electricity supply and demand. To investigate the possibility of different technologies to provide balancing services, to dimension future balancing services and design technical requirements for future balancing services, the future need of balancing power must first be known. Future power systems are often analysed by performing simulations capturing the energy produced, consumed or transmitted for different power system components with resolution of one trading period (TP), here called TP energy simulations. However, these simulations do not fully capture the continuous intra-TP power balance that must be kept at every time instance. In this paper, we propose a model to perform high-resolution intra-TP simulations to estimate the need of balancing power based on TP energy simulations. The model is applied to the Swedish system operator (SO) Svenska kraftn\"at's TP energy simulations of a scenario with high vRES penetration and a highly electrified society in year 2045. The results show that a considerable need of balancing power will occur frequently, where faster ramping of components tend to increase the need of balancing power while assuming that the transmission reliability margin (TRM) is used to net imbalances clearly decreases the need of balancing power.
Encrypted extremum seeking for privacy-preserving PID tuning as-a-Service
Authors: Nils Schlüter, Matthias Neuhaus, Moritz Schulze Darup
Date: 2022-07-10 00:00:00
Tasks: Privacy Preserving

Wireless communication offers many benefits for control such as substantially reduced deployment costs, higher flexibility, as well as easier data access. It is thus not surprising that smart and wireless sensors and actuators are increasingly used in industry. With these enhanced possibilities, exciting new technologies such as Control-as-a-Service arise, where (for example) controller design or tuning based on input-output-data can be outsourced to a cloud or mobile device. This implies, however, that sensitive plant information may become available to service providers or, possibly, attackers. Against this background, we focus on privacy-preserving optimal PID tuning as-a-Service here. In particular, we combine homomorphic encryption with extremum seeking in order to provide a purely data-driven and confidential tuning algorithm. The encrypted realization requires several adaptions of established extremum seekers. These encompass relative parameter updates, stochastic gradient approximations, and a normalized objective function. As a result, and as illustrated by various numerical examples, the proposed encrypted extremum seeker is able to tune PID controllers for a wide variety of plants without being too conservative.
Sparse and Safe Frequency Regulation for Inverter Intensive Microgrids
Authors: Junhui Zhang, Lizhi Ding, Xiaonan Lu, Wenyuan Tang
Date: 2022-07-10 00:00:00

This paper developed a novel control approach for the sparse and safe frequency regulation for inverter intensive microgrids (MGs). In the scenario, the inverters and external grids are expected to reach a synchronized desired frequency under regulations. To this end, the active power set-point acting as a control from a high-level controller is designed while considering two important performance metrics "sparsity" and "safety", which are to reduce the information exchange between controllers and ensure that the frequency keeps in safety regions during the whole operation process. Our proposed control design framework allows the sparse linear feedback controller (SLFC) to be unified with a family of conditions for safe control using control barrier functions. A quadratic programming (QP) problem is then constructed, and the real-time control policy is obtained by solving the QP problem. Importantly, we also found that the real-time control for each inverter depends on the cross-layer communication network topology which is the union of the one between controllers from SLFC and the one determined by the power flow network. Furthermore, our approach has been validated through extensive numerical simulations.
A Privacy-Preserving Energy Management System for Cooperative Multi-Microgrid Networks
Authors: Carlos Ceja-Espinosa, Mehrdad Pirnia, Claudio A. Cañizares
Date: 2022-07-10 00:00:00
Tasks: energy management, Management, Privacy Preserving

This paper presents an Energy Management System (EMS) that considers power exchanges between a set of interconnected microgrids (MGs) and the main grid, in the context of Multi-MG (MMG) systems. The model is first formulated as a centralized optimization problem, which is then decomposed into subproblems corresponding to each MG, using Lagrangian relaxation, and solved through a distributed approach using a subgradient method. The proposed model determines the power exchanges minimizing the operation cost of each MG, considering grid constraints and preserving the privacy of each MG by not revealing their generation cost and demand information. The distributed approach is validated with respect to the centralized problem, and various case studies are presented to demonstrate the performance of the proposed approach, comparing the costs of the MGs operating individually and cooperatively. The results show that all MGs in the MMG system improve their cost as consequence of the power exchanges, thus demonstrating the advantages of interconnecting MGs.
Towards Achieving Thermal Comfort through Physiologically Cloud based controlled HVAC System
Authors: Isibor Kennedy Ihianle, Pedro Machado, Kayode Owa, David Ada Adama
Date: 2022-07-09 00:00:00
Tasks: Management

Thermal comfort in shared spaces is essential to occupants well-being and necessary in the management of energy consumption. Existing thermal control systems for indoor shared spaces adjust temperature set points mechanically, making it difficult to intelligently achieve thermal comfort for all. Recent studies have shown that thermal comfort in a shared space is difficult to achieve due to individual preferences and the inability of occupants to reach a thermal compromise on temperature set points. This paper proposes a thermal comfort system to automatically adjust the temperature set-points in a shared space whilst recognising individual preferences. The control strategy of the proposed system is based on an algorithm to adjust the temperature set point of the shared space using the individual thermal preferences and predicted thermal comfort value of the occupants. The thermal preferences of the occupants are determined first and used as part of the occupants profile, which is mapped to thermal comfort values predicted from the occupants measured physiological data and environmental data. A consensus is reached by the algorithm to find the optimal temperature set-point, which takes into account individual thermal preferences and their physiological responses.
Deep Reinforcement Learning for Long-Term Voltage Stability Control
Authors: Hannes Hagmar, Le Anh Tuan, Robert Eriksson
Date: 2022-07-09 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (DRL) is a machine learning-based method suited for complex and high-dimensional control problems. In this study, a real-time control system based on DRL is developed for long-term voltage stability events. The possibility of using system services from demand response (DR) and energy storage systems (ESS) as control measures to stabilize the system is investigated. The performance of the DRL control is evaluated on a modified Nordic32 test system. The results show that the DRL control quickly learns an effective control policy that can handle the uncertainty involved when using DR and ESS. The DRL control is compared to a rule-based load shedding scheme and the DRL control is shown to stabilize the system both significantly faster and with lesser load curtailment. Finally, when testing and evaluating the performance on load and disturbance scenarios that were not included in the training data, the robustness and generalization capability of the control were shown to be effective.
Koopman-Model Predictive Control with Signal Temporal Logic Specifications for Temperature Regulation of a Warm-Water Supply System
Authors: Ryo Miyashita, Yoshihiko Susuki, Atsushi Ishigame
Date: 2022-07-09 00:00:00
Tasks: Model Predictive Control

Control of warm-water supply for dialysis treatment in a hospital environment is typical of safety-critical control problems. In order to guarantee the continuity of warm-water supply satisfying physical specifications for a wide range of operating conditions, it is inevitable to consider the nonlinearity involved in a dynamic model of a warm-water supply system for the control design. In this paper, we propose to incorporate control specifications described by signal temporal logic, which is a temporal logic with semantics over finite-time signals in formal methods, into the so-called Koopman-Model Predictive Control (MPC) as a novel technique of nonlinear MPC based on the Koopman operator framework for nonlinear systems. This enables us to generate a sequence of optimal inputs such that the controlled state of a nonlinear system can satisfy the specifications. The proposal is applied to the temperature regulation of warm-water supply, and its effectiveness is established numerically.
Cyber-Physical Attack Leveraging Subsynchronous Resonance
Authors: Bosong Li, Baosen Zhang, Daniel S. Kirschen
Date: 2022-07-08 00:00:00

This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system. If attackers were able to take over the control of a battery energy storage device, they could modulate the injection of this device at a frequency that matches one of the sub-synchronous resonance frequencies of a generator. Small changes in injection might be sufficient to excite one of these mechanical resonances, resulting in metal fatigue and ultimately a catastrophic failure in the shaft of the generator. Using a state-space model of the electromechanical system, the paper develops transfer functions linking the magnitude of the malicious injections to the magnitude of oscillations in the speed and angle of the various masses connected to the shaft. Numerical results from a two-area power system demonstrate the existence of vulnerable frequencies and show that damaging mechanical oscillations can be triggered without causing easily detectable signals at the generator terminals.
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents
Authors: Nathaniel Hamilton, Kyle Dunlap, Taylor T Johnson, Kerianne L Hobbs
Date: 2022-07-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning

Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows. To combat the safety concerns surrounding the freedom given to RL agents while training, there has been an increase in work concerning Safe Reinforcement Learning (SRL). However, these new and safe methods have been held to less scrutiny than their unsafe counterparts. For instance, comparisons among safe methods often lack fair evaluation across similar initial condition bounds and hyperparameter settings, use poor evaluation metrics, and cherry-pick the best training runs rather than averaging over multiple random seeds. In this work, we conduct an ablation study using evaluation best practices to investigate the impact of run time assurance (RTA), which monitors the system state and intervenes to assure safety, on effective learning. By studying multiple RTA approaches in both on-policy and off-policy RL algorithms, we seek to understand which RTA methods are most effective, whether the agents become dependent on the RTA, and the importance of reward shaping versus safe exploration in RL agent training. Our conclusions shed light on the most promising directions of SRL, and our evaluation methodology lays the groundwork for creating better comparisons in future SRL work.
Guarding a Translating Line with an Attached Defender
Authors: Goutam Das, Daigo Shishika
Date: 2022-07-08 00:00:00

In this paper we consider a Target-guarding differential game where the Defender must protect a linearly moving line segment by intercepting the Attacker who tries to reach it. In contrast to common Target-guarding problems, we assume that the Defender is attached to the Target and moves along with it. This assumption affects the Defender's maximum speed depending on its heading direction. A zero-sum differential game of degree for the Attacker-winning scenario is studied, where the payoff is defined to be the distance between the two agents at the time of reaching the Target. We derive the equilibrium strategies and the Value function by leveraging the solution for the infinite-length Target scenario. The zero-level set of this Value function provides the barrier surface that divides the state space into Defender-winning and Attacker-winning regions. We present simulation results at the end to demonstrate the theoretical results.
On the Application of Agile Project Management Techniques, V-Model and Recent Software Tools in Postgraduate Theses Supervision
Authors: Pouria Sarhadi, Wasif Naeem, Karen Fraser, David Wilson
Date: 2022-07-06 00:00:00
Tasks: Management

Due to the nature of most postgraduate theses in control engineering and their similarities to industrial and software engineering projects, invoking novel project control techniques could be effective. In recent decades, agile techniques have attracted popularity thanks to their attributes in delivering successful projects. Hence exploiting those methods in education and thesis supervision of engineering topics can facilitate the process. On the other hand, because of the limitations imposed by the CoVid19 pandemic, the integration of well-established online tools in collaborative education is noteworthy. This paper proposes an application of the agile project management method for the supervision of postgraduate students' theses in the general field of engineering. The study extends a Scrum technique combined with approved systems engineering and team working tools such as Jira Software, Microsoft Teams, and Git version control (Github website). A custom designed V-model to nail an outstanding thesis is presented. The overall blended method is beneficial to provide feedback and self-assessment aid for the students and the supervisors. Employing this technique has shown promising progress in easing the supervision of students whilst helping them to manage their projects.