pwc-archive/papers-with-abstracts huggingface dataset:
Minimally Conservative Controlled-Invariant Set Synthesis Using Control Barrier Certificates
Authors: Naeim Ebrahimi Toulkani, Reza Ghabcheloo
Date: 2024-11-12 00:00:00

Finding a controlled-invariant set for a system with state and control constraints is crucial for safety-critical applications. However, existing methods often produce overly conservative solutions. This paper presents a method for generating controlled-invariant (safe) sets for nonlinear polynomial control-affine systems using Control Barrier Certificates (CBCs). We formulate CBC conditions as Sum-of-Squares (SOS) constraints and solve them via an SOS Program (SOSP). First, we generalize existing SOSPs for CBC synthesis to handle environments with complex unsafe state representations. Then, we propose an iterative algorithm that progressively enlarges the safe set constructed by the synthesized CBCs by maximizing boundary expansion at each iteration. We theoretically prove that our method guarantees strict safe set expansion at every step. Finally, we validate our approach with numerical simulations in 2D and 3D for single-input and multi-input systems. Empirical results show that the safe set generated by our method covers in most part a larger portion of the state space compared to two state-of-the-art techniques.
Modelling and Control of Subsonic Missile for Air-to-Air Interception
Authors: Rory Jenkins, Xinhua Wang
Date: 2024-11-12 00:00:00

Subsonic missiles play an important role in modern air-to-air combat scenarios - utilized by the F-35 Lightning II - but require complex Guidance, Navigation and Control systems to manoeuvre with 30G's of acceleration to intercept successfully. Challenges with mathematically modelling and controlling such a dynamic system must be addressed, high frequency noise rejected, and actuator delay compensated for. This paper aims to investigate the control systems necessary for interception. It also proposes a subsonic design utilizing literature and prior research, suggests aerodynamic derivatives, and analyses a designed 2D reduced pitch autopilot control system response against performances. The pitch autopilot model contains an optimized PID controller, 2nd order actuator, lead compensator and Kalman Filter, that rejects time varying disturbances and high frequency noise expected during flight. Simulation results confirm the effectiveness of the proposed method through reduction in rise time (21%), settle time (10%), and highlighted its high frequency deficiency with respect to the compensator integration. The actuator delay of 100ms has been negated by the augmented compensator autopilot controller so that it exceeds system performance requirements (1) & (3). However, (2) is not satisfied as 370% overshoot exists. This research confirms the importance of a lead compensator in missile GNC systems and furthers control design application through a specific configuration. Future research should build upon methods and models presented to construct and test an interception scenario.
Robust control for uncertain air-to-air missile systems
Authors: Joshua Farrington, Xinhua Wang
Date: 2024-11-12 00:00:00

Air-to-air missiles are used on many modern military combat aircraft for self-defence. It is imperative for the pilots using the weapons that the missiles hit their target first time. The important goals for a missile control system to achieve are minimising the time constant, overshoot, and settling time of the missile dynamics. The combination of high angles of attack, time-varying mass, thrust, and centre of gravity, actuator delay, and signal noise create a highly non-linear dynamic system with many uncertainties that is extremely challenging to control. A robust control system based on saturated sliding mode control is proposed to overcome the time-varying parameters and non-linearities. A lag compensator is designed to overcome actuator delay. A second-order filter is selected to reduce high-frequency measurement noise. When combined, the proposed solutions can make the system stable despite the existence of changing mass, centre of gravity, thrust, and sensor noise. The system was evaluated for desired pitch angles of 0{\deg} to 90{\deg}. The time constant for the system stayed below 0.27s for all conditions, with satisfactory performance for both settling time and overshoot.
Longitudinal dynamic modelling and control for a quad-tilt rotor UAV
Authors: William Smith, Xinhua Wang
Date: 2024-11-12 00:00:00

Tilt rotor aircraft combine the benefits of both helicopters and fixed wing aircraft, this makes them popular for a variety of applications, including Search and Rescue and VVIP transport. However, due to the multiple flight modes, significant challenges with regards to the control system design are experienced. The main challenges with VTOL aircraft, comes during the dynamic phase (mode transition), where the aircraft transitions from a hover state to full forwards flight. In this transition phase the aerodynamic lift and torque generated by the wing/control surfaces increases and as such, the rotor thrust, and the tilt rate must be carefully considered, such that the height and attitude remain invariant during the mode transition. In this paper, a digital PID controller with the applicable digital filter and data hold functions is designed so that a successful mode transition between hover and forwards flight can be ascertained. Finally, the presented control system for the tilt-rotor UAV is demonstrated through simulations by using the MATLAB software suite. The performance obtained from the simulations confirm the success of the implemented methods, with full stability in all three degrees of freedom being demonstrated.
Constructive RNNs: An Error-Recurrence Perspective on Time-Variant Zero Finding Problem Solving Under Uncertainty
Authors: Mingxuan Sun, Xing Li, Han Wang
Date: 2024-11-12 00:00:00

When facing time-variant problems in analog computing, the desirable RNN design requires finite-time convergence and robustness with respect to various types of uncertainties, due to the time-variant nature and difficulties in implementation. It is very worthwhile to explore terminal zeroing neural networks, through examining and applying available attracting laws. In this paper, from a control-theoretic point of view, an error recurrence system approach is presented by equipping with uncertainty compensation in the pre-specified error dynamics, capable of enhancing robustness properly. Novel rectifying actions are designed to make finite-time settling so that the convergence speed and the computing accuracy of time-variant computing can be improved. Double-power and power-exponential rectifying actions are respectively formed to construct specific models, while the particular expressions of settling time function for the former are presented, and for the latter the proximate settling-time estimations are given, with which the fixed-time convergence of the corresponding models is in turn established. Moreover, the uncertainty compensation by the signum/smoothing-signum techniques are adopted for finite-duration stabilization. Theoretical results are presented to demonstrate effectiveness (involving fixed-time convergence and robustness) of the proposed computing schemes for the time-variant QP problem solving.
Lateral String Stability in Autonomous & Connected Vehicle Platoons
Authors: Neelkamal Somisetty, Swaroop Darbha
Date: 2024-11-12 00:00:00

This paper addresses the lateral control of Autonomous and Connected Vehicles (ACVs) in a platoon executing an Emergency Lane Change (ELC) maneuver. These maneuvers are typically triggered by emergency signals from the front or rear of the platoon in response to the need to avoid obstacles or allow other vehicles to pass. The study assumes that ACVs maintain reliable connectivity, enabling each following vehicle to access GPS position traces of both the lead and immediately preceding vehicles in the platoon. We demonstrate that lateral string stability in the ACV platoon can be achieved using communicated information solely from the lead and preceding vehicles. Additionally, we present a lateral control framework for ACVs, which helps track a discretized preview of the trajectory constructed from the communicated data. This framework involves constructing two distinct trajectories based on the preview data from the lead and preceding vehicles, calculating the associated errors and lateral control actions for each, and then integrating these to generate a steering command. Numerical results validate the effectiveness of the proposed lateral control scheme.
Differentiable Optimization-based Control Policy with Convergence Analysis
Authors: Yuexin Bian, Jie Feng, Yuanyuan Shi
Date: 2024-11-12 00:00:00

Real-world system control requires both high-performing and interpretable controllers. Model-based control policies have gained popularity by using historical data to learn system costs and dynamics before implementation. However, this two-phase approach prevents these policies from achieving optimal control as the metrics that we train these models (e.g., mean squared errors) often differ from the actual control system cost. In this paper, we present DiffOP, a Differentiable Optimization-based Policy for optimal control. In the proposed framework, control actions are derived by solving an optimization, where the control cost function and system's dynamics can be parameterized as neural networks. Our key technical innovation lies in developing a hybrid optimization algorithm that combines policy gradients with implicit differentiation through the optimization layer, enabling end-to-end training with the actual cost feedback. Under standard regularity conditions, we prove DiffOP converges to stationary points at a rate of $O(1/K)$. Empirically, DiffOP achieves state-of-the-art performance in both nonlinear control tasks and real-world building control.
Two-Layer Attention Optimization for Bimanual Coordination
Authors: Justin Ting, Jing Shuang Li
Date: 2024-11-12 00:00:00

Bimanual tasks performed by human agents present unique optimal control considerations compared to cyberphysical agents. These considerations include minimizing attention, distributing attention across two isolated hands, and coordinating the two hands to reach a broader goal. In this work, we propose a two-layer controller that captures these considerations. The upper layer solves an attention distribution problem, while the two lower layer controllers (one per hand) tracks a trajectory using the solution given by the upper layer. We introduce a formulation of the attention controller where attention is a vector that is bound within a hyperbolic feasible region, which is determined by specifications of the task the lower layer controllers. This two-layer controller is used to optimize a single-player game of pong, where the agent must rally the ball between two paddles for as long as possible. We find that adding an attention layer on top of the lower controllers allows the agent to coordinate the left and right hands, which minimizes attention and control effort over the course of the rallying task.
Machine Learning Based Cyber System Restoration for IEC 61850 Based Digital Substations
Authors: Kuchan Park, Mansi Girdhar, Junho Hong, Wencong Su, Akila Herath, Chen-Ching Liu
Date: 2024-11-11 00:00:00

Substation Automation Systems (SAS) that adhere to the International Electrotechnical Commission (IEC) 61850 standard have already been widely implemented across various on-site local substations. However, the digitalization of substations, which involves the use of cyber system, inherently increases their vulnerability to cyberattacks. This paper proposes the detection of cyberattacks through an anomaly-based approach utilizing Machine Learning (ML) methods within central control systems of the power system network. Furthermore, when an anomaly is identified, mitigation and restoration strategies employing concurrent Intelligent Electronic Devices (CIEDs) are utilized to ensure robust substation automation system operations. The proposed ML model is trained using Sampled Value (SV) and Generic Object Oriented Substation Event (GOOSE) data from each substation within the entire transmission system. As a result, the trained ML models can classify cyberattacks and normal faults, while the use of CIEDs contributes to cyberattack mitigation, and substation restoration.
Circulating Currents in Electric Machines: Positive Impact of The End Windings Length on Losses
Authors: Taha El Hajji, Antti Lehikoinen, Anouar Belahcen
Date: 2024-11-11 00:00:00

Circulating currents occurring in windings of electric machines received rising interest recent years. Circulating currents represent unwanted currents flowing between parallel-connected conductors. This phenomenon is due to various reasons such as asymmetries in the winding and differences in electric potential between parallel-connected conductors. This effect occurs both at no-load and on-load conditions, and always lead to uneven distribution of the current between the parallel conductors, therefore leading to higher losses, as proven in the authors' previous work. Circulating currents are occurring mainly due to asymmetries and electric potential difference in the active part, meaning that long end windings are advantageous to mitigate the effect of circulating currents. Losses due to circulating currents decrease at a rate proportional to the inverse square of the end windings length. The aim of this paper is to mathematically prove this property and present a case study application in an electric machine.
Two-Stage Stochastic Optimization for Low-Carbon Dispatch in a Combined Energy System
Authors: Manling Hu, Manqi Xu, Dunnan Liu
Date: 2024-11-11 00:00:00
Tasks: Scheduling, Stochastic Optimization

While wind and solar power contribute to sustainability, their intermittent nature poses challenges when integrated into the grid. To mitigate these issues, renewable energy can be combined with coal fired power and hydropower sources to stabilize the energy system, with battery storage serving as a backup source to smooth the total output. This study develops a low carbon dispatch model for a combined energy system using a two stage stochastic optimization approach. The model incorporates a carbon trading mechanism to regulate emissions and addresses the uncertainty in wind and solar outputs by clustering output curves into typical scenarios to derive a joint distribution. In the initial stage of scheduling, decisions are made regarding the unit commitment for coal fired power plants. The second stage optimizes the expected operation cost of other energy generation sources. The feasibility of the model is demonstrated by comparing the results of stochastic and deterministic scenarios through simulation. Analysis of different carbon prices further explores the impact of the carbon trading mechanism on the system's operation cost.
FlexiGen: Stochastic Dataset Generator for Electric Vehicle Charging Energy Flexibility
Authors: Bernardo Cabral, Tiago Fonseca, Clarisse Sousa, Luis Lino Ferreira
Date: 2024-11-11 00:00:00

Electric vehicles (EVs) and renewable energy sources (RES) are vital components of sustainable energy systems, yet their uncoordinated integration can pose substantial challenges to grid stability, such as unmanaged peak loads and energy balance issues. Vehicle-to-Grid (V2G), offer a promising solution to address these challenges by enabling bidirectional energy flow between EVs and the grid. As such, EVs can be used in advances Demand Response (DR) strategies to optimize energy use and mitigate the intermittency of renewable generation. To reach such advantages, optimization algorithms need data on EV energy flexibility, such as charging patterns and usage preferences. However, data collection remains constrained by challenges such as high costs, user engagement, data privacy concerns, and limited access to open-source datasets on EV energy flexibility. This paper presents FlexiGen an open-source stochastic dataset generator tool designed to overcome the data limitations in EV flexibility for V2G and V1G DR applications. FlexiGen generates synthetic datasets encompassing realist EV usage patterns, behaviours and flexibility scenarios for household and office routines. To generate these datasets, FlexiGen uses a series of configurable probabilistic variables, such as stochastic user routines, traffic conditions, charger types, car average electricity consumption and State of Charge (SoC). The generated datasets include an hourly routine with the EV State of connection, Destination Charger, Estimated Departure Time, Required SOC at Departure, Estimated Arrival Time, and Estimated SOC at Arrival. Accompanying this publication an example dataset is generated for 3 households with 1 EV each, and 1 office building with 3 EVs. The generated dataset is analyzed and discussed on the paper and published alongside the open-source code for FlexiGen tool.
Co-Scheduling of Energy and Production in Discrete Manufacturing Considering Decision-Dependent Uncertainties
Authors: Yiyuan Pan, Zhaojian Wang
Date: 2024-11-11 00:00:00
Tasks: Scheduling

Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncertainties (DDUs) pose additional challenges in finding optimal production strategies, as the signature or the shape of the uncertainty set cannot be determined before solving the model. However, existing research does not account for decision-dependent uncertainties (DDUs) present in discrete manufacturing; moreover, current algorithms for solving models with DDUs suffer from high computational complexity, making them unsuitable for the real-time control requirements of modern industry. To this end, we proposed an energy-production co-scheduling model for discrete manufacturing, taking into account decision-dependent uncertainties (DDUs). Subsequently, we devised a method for transforming the constraints associated with DDUs into linear form ones. Finally, we designed a novel algorithm based on the column-and-constraint generation (C&CG) algorithm and undertook a theoretical analysis of its performance of convergence and algorithmic complexity. A real-world engine assembly line was used to test our model and algorithm. Simulation results demonstrate that our method significantly reduces production costs and achieves better frequency regulation performance.
Scalable Distributed Least Squares Algorithm for Linear Algebraic Equations via Scheduling
Authors: Shenyu Liu
Date: 2024-11-11 00:00:00
Tasks: Scheduling

In this work, we propose a novel discrete-time distributed algorithm for finding least squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the network is assumed to know some rows of the coefficient matrix and the corresponding entries in the observation vector. Unlike typical distributed algorithms, our approach considers communication bandwidth limits, allowing agents to transmit only a portion of their ``guessed" solution, independent of its dimension. A periodic scheduling protocol determines which portion is transmitted at each iteration. Assuming a small fixed step size and a diagonalizable algorithm matrix, we prove that agents' ``guessed" solutions converge exponentially to a least squares solution. For cases where the observation vectors are time-varying, a modified algorithm guarantees practical convergence, with tracking error bounded by the single-step variation in the observation vector. Simulations on both small-scale and medium-scale examples validate the feasibility of our algorithms.
Modeling and Detection of Critical Slowing Down in Epileptic Dynamics
Authors: Yuzhen Qin, Marcel van Gerven
Date: 2024-11-11 00:00:00

Epilepsy is a common neurological disorder characterized by abrupt seizures. Although seizures may appear random, they are often preceded by early warning signs in neural signals, notably, critical slowing down, a phenomenon in which the system's recovery rate from perturbations declines when it approaches a critical point. Detecting these markers could enable preventive therapies. This paper introduces a multi-stable slow-fast system to capture critical slowing down in epileptic dynamics. We construct regions of attraction for stable states, shedding light on how dynamic bifurcations drive pathological oscillations. We derive the recovery rate after perturbations to formalize critical slowing down. A novel algorithm for detecting precursors to ictal transitions is presented, along with a proof-of-concept event-based feedback control strategy to prevent impending pathological oscillations. Numerical studies are conducted to validate our theoretical findings.
A System Parametrization for Direct Data-Driven Analysis and Control with Error-in-Variables
Authors: Felix Brändle, Frank Allgöwer
Date: 2024-11-11 00:00:00

In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman-Morrison-Woodbury formula to transform the problem into a linear fractional transformation (LFT) with unknown measurement errors and disturbances as uncertainties. For bounded uncertainties, we apply robust control techniques to derive a guaranteed upper bound on the H2-norm of the unknown true system. To this end, a single semidefinite program (SDP) needs to be solved, with complexity that is independent of the amount of data. Furthermore, we exploit the signal-to-noise ratio to provide a data-dependent condition, that characterizes whether the proposed parametrization can be employed. The modular formulation allows to extend this framework to controller synthesis with different performance criteria, input-output settings, and various system properties. Finally, we validate the proposed approach through a numerical example.
Robust Sliding Mode Control for Air-to-Air Missile
Authors: Eerik Cockin, Xinhua Wang
Date: 2024-11-11 00:00:00

Within the missile guidance and control system the autopilot must overcome an array of variables and uncertainties to maintain tracking trajectory. A large uncertainty explored in this paper is the difference between the assumed flight dynamics, the controller design relies on, and the true flight dynamics the missile experiences. To capture these differences experimental wind tunnel data was used to represent real life aerodynamics whereas a low fidelity wing and tube structure was used for the controller dynamics. Other variables affecting controller performance are also quantified and explored in this paper, such as the changing mass, center of gravity, dynamic pressure, actuator bandwidth and sensor noise. A second order sliding mode controller utilizing an exponential reaching law was developed to overcome the cumulative uncertainties. The designed controller is capable of a 0.2s settling time and a 3% overshoot in ideal conditions. The controller relies on measurements of both dynamic pressure and angle of attack, when a 10% and 2% respective noise is introduced at 200Hz, the controller maintains a 5% steady state error and a time constant of 0.29s. The exponential reaching law provides superior chattering mitigation over traditional techniques like the tanh function, with no loss in controller performance.