pwc-archive/papers-with-abstracts huggingface dataset:
Mode transition control of large-size tiltrotor aircraft
Authors: George Kirst, Xinhua Wang
Date: 2024-11-11 00:00:00

Tiltrotors are an aircraft concept with the ability to rotate their rotors freely, achieving vertical take-off and fast forward flight. The combination of helicopter and fixed-wing flight into one aircraft provides versatility in mission selection, yet challenges persist in their construction and control. Tiltrotor aircraft can operate in three primary modes: helicopter, fixed-wing, and transition, with the transition mode facilitating the shift between helicopter and fixed-wing flight. However, control within this transition region is inherently challenging due to its non-linear nature, hence tiltrotors have been predominantly limited to military applications. Thus, this paper aims to explore transition mode control for a large-size tiltrotor aircraft, tailored to civil applications. A novel, large-sized, tiltrotor concept is presented, accompanied by a derived mathematical model describing the aircrafts behaviours. A PID control method has been used to control the height, pitch, and velocity variations within the transition mode with secondary control loop developed to control the tilt angle during transition. The derived model and control are then implemented within a MATLAB simulation, where the control method was iterated to improve performance. The results show a full transition was achieved in under 14 seconds, where altitude variations were kept below 10 metres. Though the transition mode control was successful, a collective look at the data showcases issues with assumptions as well as thrust discontinuities. The implications of these results are discussed, with suggested improvements proposed for future work.
Data-Driven Control of Large-Scale Networks with Formal Guarantees: A Small-Gain Free Approach
Authors: Behrad Samari, Amy Nejati, Abolfazl Lavaei
Date: 2024-11-11 00:00:00

This paper offers a data-driven divide-and-conquer strategy to analyze large-scale interconnected networks, characterized by both unknown mathematical models and interconnection topologies. Our data-driven scheme treats an unknown network as an interconnection of individual agents (a.k.a. subsystems) and aims at constructing their symbolic models, referred to as discrete-domain representations of unknown agents, by collecting data from their trajectories. The primary objective is to synthesize a control strategy that guarantees desired behaviors over an unknown network by employing local controllers, derived from symbolic models of individual agents. To achieve this, we leverage the concept of alternating sub-bisimulation function (ASBF) to capture the closeness between state trajectories of each unknown agent and its data-driven symbolic model. Under a newly developed data-driven compositional condition, we then establish an alternating bisimulation function (ABF) between an unknown network and its symbolic model, based on ASBFs of individual agents, while providing correctness guarantees. Despite the sample complexity in existing work being exponential with respect to the network size, we demonstrate that our divide-and-conquer strategy significantly reduces it to a linear scale with respect to the number of agents. We also showcase that our data-driven compositional condition does not necessitate the traditional small-gain condition, which demands precise knowledge of the interconnection topology for its fulfillment. We apply our data-driven findings to two benchmarks comprising unknown networks with an arbitrary, a-priori undefined number of agents and unknown interconnection topologies.
Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach
Authors: Weinan Gao, Zhong-Ping Jiang, Tianyou Chai
Date: 2024-11-11 00:00:00

In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty.
An exact active sensing strategy for a class of bio-inspired systems
Authors: Debojyoti Biswas, Eduardo D. Sontag, Noah J. Cowan
Date: 2024-11-10 00:00:00

We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude periodic forcing function akin to so-called "active sensing" in biology, together with nonlinear output feedback. Our analysis shows that this approach leads to the emergence of an exponentially stable limit cycle. These findings offer a provably stable active sensing strategy and may thus help to rationalize the active sensing movements made by animals as they perform certain motor behaviors.
DERs-Aided Blackstart and Load Restoration Framework for Distribution Systems Considering Synchronization and Frequency Security Constraints
Authors: Salish Maharjan, Cong Bai, Han Wang, Yiyun Yao, Fei Ding, Zhaoyu Wang
Date: 2024-11-10 00:00:00

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids, sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated microgrids. In DER-aided blackstart, the continuous operation of these microgrids is uncertain due to the finite energy capacity of commonly used blackstart units, such as battery energy storage (BES)-based grid-forming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between microgrids and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition was developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottom-up blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. The proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.
Decentralized Bus Voltage Restoration for DC Microgrids
Authors: Nabil Mohammed, Shehab Ahmed, Charalambos Konstantinou
Date: 2024-11-10 00:00:00

Regulating the voltage of the common DC bus, also referred to as the load bus, in DC microgrids is crucial for ensuring reliability and maintaining the nominal load voltage, which is essential for protecting sensitive loads from voltage variations. Stability and reliability are thereby enhanced, preventing malfunctions and extending the lifespan of sensitive loads (e.g., electronic devices). Voltage drops are caused by resistances of feeders connecting converters to the common DC bus, resulting in a reduced DC bus voltage compared to the nominal/desired value. Existing techniques to restore this voltage in DC microgrids are mainly centralized and rely on secondary control layers. These layers sense the common DC bus voltage, compare it to the nominal value, and utilize a PI controller to send corrections via communication links to each converter. In this paper, a local and straightforward approach to restoring the bus voltage in DC microgrids is presented, ensuring regulation in a decentralized manner. Voltage drops across resistances of feeders are compensated by an additional control loop feedback within each converter, based on the converter output current and feeder resistance. The proposed approach is verified through simulation and hardware-in-the-loop results, eliminating the need for communication links and hence increasing reliability and reducing cybersecurity threats.
Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
Authors: Xiaocan Li, Xiaoyu Wang, Ilia Smirnov, Scott Sanner, Baher Abdulhai
Date: 2024-11-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Traffic Signal Control

Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. However, effective signal control inherently requires coordination across a broader spatial scope, as the effect of upstream traffic should influence signal control decisions at downstream intersections, impacting a large area in the traffic network. Although agent communication using neural network-based feature extraction can implicitly enhance spatial awareness, it significantly increases the learning complexity, adding an additional layer of difficulty to the challenging task of control in deep reinforcement learning. To address the issue of learning complexity and myopic traffic pressure definition, our work introduces a novel concept based on Markov chain theory, namely \textit{multi-hop upstream pressure}, which generalizes the conventional pressure to account for traffic conditions beyond the immediate upstream links. This farsighted and compact metric informs the deep reinforcement learning agent to preemptively clear the multi-hop upstream queues, guiding the agent to optimize signal timings with a broader spatial awareness. Simulations on synthetic and realistic (Toronto) scenarios demonstrate controllers utilizing multi-hop upstream pressure significantly reduce overall network delay by prioritizing traffic movements based on a broader understanding of upstream congestion.
Time-delayed Dynamic Mode Decomposition for families of periodic trajectories in Cislunar Space
Authors: Sriram Narayanan, Mohamed Naveed Gul Mohamed, Indranil Nayak, Suman Chakravorty, Mrinal Kumar
Date: 2024-11-10 00:00:00

In recent years, the development of the Lunar Gateway and Artemis missions has renewed interest in lunar exploration, including both manned and unmanned missions. This interest necessitates accurate initial orbit determination (IOD) and orbit prediction (OP) in this domain, which faces significant challenges such as severe nonlinearity, sensitivity to initial conditions, large state-space volume, and sparse, faint, and unreliable measurements. This paper explores the capability of data-driven Koopman operator-based approximations for OP in these scenarios. Three stable periodic trajectories from distinct cislunar families are analyzed. The analysis includes theoretical justification for using a linear time-invariant system as the data-driven surrogate. This theoretical framework is supported by experimental validation. Furthermore, the accuracy is assessed by comparing the spectral content captured to period estimates derived from the fast Fourier transform (FFT) and Poincare-like sections.
One controller to rule them all
Authors: Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga, Simone Formentin
Date: 2024-11-10 00:00:00
Tasks: All, In-Context Learning

Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as intended? With the rise of in-context learning and powerful architectures like Transformers, this might be possible, and we want to show it. In this work, within the model reference framework, we hence propose the first in-context learning-based approach to design a unique contextual controller for an entire class of dynamical systems rather than focusing on just a single instance. Our promising numerical results show the possible advantages of the proposed paradigm, paving the way for a shift from the "one-system-one-controller" control design paradigm to a new "one-class-one-controller" logic.
Stability Analysis of Distributed Estimators for Large-Scale Interconnected Systems: Time-Varying and Time-Invariant Cases
Authors: Zhongyao Hu, Bo Chen, Jianzheng Wang, Daniel W. C. Ho, Wen-An Zhang, Li Yu
Date: 2024-11-10 00:00:00

This paper studies a distributed estimation problem for time-varying/time-invariant large-scale interconnected systems (LISs). A fully distributed estimator is presented by recursively solving a distributed modified Riccati equation (DMRE) with decoupling variables. By partitioning the LIS based on the transition matrix's block structure, it turns out that the stability of the subsystem is independent of the global LIS if the decoupling variable is selected as the number of out-neighbors. Additionally, it is revealed that any LIS can be equivalently represented by a Markov system. Based on this insight, we show that the stability decoupling above can also be achieved if the decoupling variable equals the number of in-neighbors. Then, the distributed estimator is proved to be stable if the DMRE remains uniformly bounded. When the LIS is considered time-invariant, and by analyzing the spectral radius of a linear operator, it is proved that the DMRE is uniformly bounded if and only if a linear matrix inequality is feasible. Based on the boundedness result, we also show that the distributed estimator converges to a unique steady state for any initial condition. Finally, simulations verify the effectiveness of the proposed methods.
Smooth Zone Barrier Lyapunov Functions for Nonlinear Constrained Control Systems
Authors: Hamed Rahimi Nohooji, Holger Voos
Date: 2024-11-09 00:00:00

This paper introduces the Smooth Zone Barrier Lyapunov Function (s-ZBLF) for output and full-state constrained nonlinear control systems. Unlike traditional BLF methods, where control effort continuously increases as the state moves toward the constraint boundaries, the s-ZBLF method keeps the control effort nearly zero near the origin, with a more aggressive increase as the system approaches the boundary. However, unlike previous works where control effort was zero within a predefined safe region around the origin, the s-ZBLF overcomes the disadvantage of discontinuous control activation by providing a smooth, gradual increase in control effort as the state nears the constraints. This smooth transition improves continuity in the control response and enhances stability by reducing chattering. Additionally, the s-ZBLF provides the advantage of minimal control effort in regions far from the constraints, reducing energy consumption and actuator wear. Two forms of the s-ZBLF, logarithmic-based and rational-based, are presented. Theoretical analysis guarantees that all system states remain within the defined constraints, ensuring boundedness and stability of the closed-loop system. Simulation results validate the effectiveness of the proposed method in handling constrained nonlinear systems.
Why has advanced commercial HVAC control not yet achieved its promise?
Authors: Gregor P. Henze, Kevin J. Kircher, James E. Braun
Date: 2024-11-09 00:00:00

Over the last two decades, research and development efforts have shown that advanced control of heating, ventilation, and air conditioning (HVAC) equipment in commercial buildings can improve energy efficiency, reduce emissions, and turn buildings into active participants in the power grid. Despite these efforts, advanced commercial HVAC control has not yet seen widespread adoption. In this paper, we argue that the research community can help companies deploy advanced HVAC control at speed and scale by reorienting research efforts toward clearly demonstrating the business case for adoption. To support this argument, we draw on findings from the 2023 Intelligent Building Operations Workshop, which brought together researchers, entrepreneurs, and representatives from industry and government to discuss current business offerings, state-of-the-art field demonstrations, barriers to adoption, and future directions.
A New 8/14 Two-Phase Switched Reluctance Motor with Improved Performance
Authors: Gholamreza Davarpanah, Hossein Shirzad, Jawad Faiz
Date: 2024-11-09 00:00:00

Despite their simple and robust structure, low cost, and simple cooling system, switched reluctance motors (SRMs) face the challenge of low mean torque. A possible solution is to change the structure of SRMs. This article introduces an innovative combination of the number of rotor teeth and stator teeth of a two-phase switch reluctance motor (TPSRM) with eight teeth for the stator and fourteen teeth for the rotor. As a result of its unique design, which has a short path for passing the main flux, it requires less magnetomotive force. This leads to less core and copper loss, resulting in increased efficiency. Each tooth of the stator in a phase develops a positive torque during the rotation of the rotor, which increases the torque and consequently increases the mean torque of the proposed TPSRM. A current hysteresis control (CHC) is simulated by 2D FEM for the proposed 8/14 TPSRM and the conventional 8/12 TPSRM under the same mechanical load on the shaft to get a current hysteresis reference of 15A at the nominal speed of 600 rpm. To verify the novelty and advantages of the suggested TPSRM, it is compared with the conventional 8/12 TPSRM in terms of mean and peak torque, torque density, and core and copper losses were compared. Lastly, the proposed 8/14 TPSRM is shown to have better performance than the conventional 8/12 TPSRM.
Decentralized Semantic Communication and Cooperative Tracking Control for a UAV Swarm over Wireless MIMO Fading Channels
Authors: Minjie Tang, Chenyuan Feng, Tony Q. S. Quek
Date: 2024-11-09 00:00:00
Tasks: Semantic Communication

This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for both the internal interactions among the cooperative follower UAVs and the imperfections inherent in the MIMO channels that interlink the leader and follower UAVs. Building on this model, we incorporate the power costs of the UAVs and formulate the communication and cooperative tracking control challenge as a drift-plus-penalty optimization problem. We then derive a closed-form optimal solution that maintains a decentralized semantic architecture, dynamically adjusting to the tracking error costs and local channel conditions within the swarm. Employing Lyapunov drift analysis, we establish closed-form sufficient conditions for the stabilization of the UAV swarm's tracking performance. Numerical results demonstrate the significant enhancements in our proposed scheme over various state-of-the-art methods.
Behavior-Aware Efficient Detection of Malicious EVs in V2G Systems
Authors: Ruixiang Wu, Xudong Wang, Tongxin Li
Date: 2024-11-09 00:00:00
Tasks: Decision Making

With the rapid development of electric vehicles (EVs) and vehicle-to-grid (V2G) technology, detecting malicious EV drivers is becoming increasingly important for the reliability and efficiency of smart grids. To address this challenge, machine learning (ML) algorithms are employed to predict user behavior and identify patterns of non-cooperation. However, the ML predictions are often untrusted, which can significantly degrade the performance of existing algorithms. In this paper, we propose a safety-enabled group testing scheme, \ouralg, which combines the efficiency of probabilistic group testing with ML predictions and the robustness of combinatorial group testing. We prove that \ouralg is $O(d)$-consistent and $O(d\log n)$-robust, striking a near-optimal trade-off. Experiments on synthetic data and case studies based on \textsc{ACN-Data}, a real-world EV charging dataset validate the efficacy of \ouralg for efficiently detecting malicious users in V2G systems. Our findings contribute to the growing field of algorithms with predictions and provide insights for incorporating distributional ML advice into algorithmic decision-making in energy and transportation-related systems.
Cyber-Physical Security of Vehicles: Zero Dynamics Attacks Against Vehicle's Lateral Dynamics
Authors: Ghadeer Shaaban, Hassen Fourati, Alain Kibangou, Christophe Prieur, Mohammad Pirani
Date: 2024-11-08 00:00:00

Modern vehicles have evolved from mechanical systems to complex and connected ones controlled by numerous digital computers interconnected through internal networks. While this development has improved their efficiency and safety, it also brings new potential risks, particularly cyber-attacks. Several studies have explored the security of vehicle dynamics against such threats. Among these dynamics, the vehicle's lateral dynamics are crucial for maintaining stability and control during turns and maneuvers, making them a key focus of research. However, only a few recent studies have specifically investigated the security of lateral dynamics. This paper explores the potential for zero dynamics attacks on the vehicle's lateral dynamics, where the attacker can remain undetected by leaving no trace on the system's outputs. Three scenarios are studied: when the output includes yaw rate, lateral acceleration, and their combination. These two critical measurements of a vehicle's lateral motion are accessible through the inertial measurement units (IMU) in every vehicle. For each scenario, the impact of zero dynamics attacks on system performance is analyzed and illustrated through simulations. Finally, the paper provides recommendations for securing vehicles' lateral dynamics against such attacks.
Modelling, design and control of middle-size tilt-rotor quadrotor
Authors: Theodore Nye-Matthew, Xinhua Wang
Date: 2024-11-08 00:00:00

This paper explores the mathematical modelling and 3D design of a tilt-rotor quadrotor aircraft. The aircraft is a VTOL design and has capacity for one pilot. The design incorporates a part manual part automatic computerised flight control system and hybrid powertrain providing energy to eight ducted contrarotating propellers. Analysis of controllability was performed using the parameters derived from the developed model for the take-off phase of flight. The aircraft is of a lightweight design and is intended to fill a niche in the aviation market with potential for civilian and military applications. The aircraft has multiple redundancies in the instance of propeller failure. The viability of a hybrid powerplant is explored by combining industry standard gas turbine technology with electrical motor and battery systems. The combination of these systems results in a safe, versatile aircraft that can operate at different levels of automation depending on environmental factors or phase of flight. Simulation confirms the design of the aircraft.