pwc-archive/papers-with-abstracts huggingface dataset:
Impedance-based Root-cause Analysis: Comparative Study of Impedance Models and Calculation of Eigenvalue Sensitivity
Authors: Yue Zhu, Yunjie Gu, Yitong Li, Timothy C. Green
Date: 2022-04-04 00:00:00
Tasks: Sensitivity, State Space Models

Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available. For tracing the root causes of poor damping and tuning modes of the system, the sensitivity of the modes to components and parameters are needed. The so-called critical admittance-eigenvalue sensitivity based on nodal admittance model has provided a partial solution but omits meaningful directional information. The alternative whole-system impedance model yields participation factors of shunt-connected apparatus with directional information that allows separate tuning for damping and frequency, yet do not cover series-connected components. This paper formalises the relationships between the two forms of impedance models and between the two forms of root-cause analysis. The calculation of system eigenvalue sensitivity in impedance models is further developed, which fills the gaps of previous research and establishes a complete theory of impedance-based root-cause analysis. The theoretical relationships and the tuning of parameters have been illustrated with a three-node passive network, a modified IEEE 14-bus network and a modified NETS-NYPS 68-bus network, showing that tools can be developed for tuning of IBR-rich power systems where only black-box impedance models are available.
An Isolated Gate Driver for Multi-Active Bridges with Soft Switching
Authors: Ferdinand Grimm, Pouya Kolahian, John Wood, Richard Bucknall, Mehdi Baghdadi
Date: 2022-04-04 00:00:00

The design of gate drivers is an important topic in power converter topologies that can help reduce switching losses and increase power density. Gate driving techniques that offer zero-voltage switching and/or zero current switching have recently been successfully proposed for different modular multilevel converters such as the cascaded H bridge. Previous papers on other multilevel converters such as the multi-active bridge, however, do not sufficiently assess the topics of gate driver design for this topology. This work presents a novel isolated gate driver architecture tailored to the multi-active bridge topology. Zero voltage switching is then achieved using two multi-winding transformers. The advantages of the proposed topology are not only a reduction of switching losses but also reduced component count. The topology is evaluated on a prototype using experimental results. It was shown using simulation and experiments that the proposed topology has a high efficiency while providing compact power packaging. Especially for converters with many levels, the proposed topology is therefore advantageous compared to existing solutions.
An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars
Authors: Rudolf Reiter, Florian Messerer, Markus Schratter, Daniel Watzenig, Moritz Diehl
Date: 2022-04-04 00:00:00
Tasks: Trajectory Prediction

This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data.
Distributed online estimation of biophysical neural networks
Authors: Thiago B. Burghi, Timothy O'Leary, Rodolphe Sepulchre
Date: 2022-04-04 00:00:00

In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models. Neural systems learn by adjusting intrinsic and synaptic weights in a distributed fashion, with neuronal membrane voltages carrying information from neighbouring neurons in the network. We show that this learning principle can be used to design an adaptive observer based on a decentralized learning rule that greatly reduces the number of observer states required for exponential convergence of parameter estimates. This novel design is relevant for biological, biomedical and neuromorphic applications.
Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction
Authors: Farhad Ghanipoor, Carlos Murguia, Peyman Mohajerin Esfahani, Nathan van de Wouw
Date: 2022-04-04 00:00:00
Tasks: State Estimation

In this paper, we present a methodology for actuator and sensor fault estimation in nonlinear systems. The method consists in augmenting the system dynamics with an approximated ultra-local model (a finite chain of integrators) for the fault vector and constructing a Nonlinear Unknown Input Observer (NUIO) for the augmented dynamics. Then, fault reconstruction is reformulated as a robust state estimation problem in the augmented state (true state plus fault-related state). We provide sufficient conditions that guarantee the existence of the observer and stability of the estimation error dynamics (asymptotic stability of the origin in the absence of faults and ISS guarantees in the faulty case). Then, we cast the synthesis of observer gains as a semidefinite program where we minimize the L2-gain from the model mismatch induced by the approximated fault model to the fault estimation error. Finally, simulations are given to illustrate the performance of the proposed methodology.
Circuit Model Reduction with Scaled Relative Graphs
Authors: Thomas Chaffey, Alberto Padoan
Date: 2022-04-04 00:00:00
Tasks: Continued fraction, model

Continued fractions are classical representations of complex objects (for example, real numbers) as sums and inverses of simpler objects (for example, integers). The analogy in linear circuit theory is a chain of series/parallel one-ports: the port behavior is a continued fraction containing the port behaviors of its elements. Truncating a continued fraction is a classical method of approximation, which corresponds to deleting the circuit elements furthest from the port. We apply this idea to chains of series/parallel one-ports composed of arbitrary nonlinear relations. This gives a model reduction method which automatically preserves properties such as incremental positivity. The Scaled Relative Graph (SRG) gives a graphical representation of the original and truncated port behaviors. The difference of these SRGs gives a bound on the approximation error, which is shown to be competitive with existing methods.
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning
Authors: S M Nahid Mahmud, Moad Abudia, Scott A Nivison, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2022-04-04 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The objective of this research is to enable safety-critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learning optimal policies via trial and error, i.e., traditional reinforcement learning, is difficult to implement in safety-critical systems, particularly when task restarts are unavailable. Safe model-based reinforcement learning techniques based on a barrier transformation have recently been developed to address this problem. However, these methods rely on full state feedback, limiting their usability in a real-world environment. In this work, an output-feedback safe model-based reinforcement learning technique based on a novel barrier-aware dynamic state estimator has been designed to address this issue. The developed approach facilitates simultaneous learning and execution of safe control policies for safety-critical linear systems. Simulation results indicate that barrier transformation is an effective approach to achieve online reinforcement learning in safety-critical systems using output feedback.
Concurrent learning in high-order tuners for parameter identification
Authors: Justin H. Le, Andrew R. Teel
Date: 2022-04-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High-order tuners are algorithms that show promise in achieving greater efficiency than classic gradient-based algorithms in identifying the parameters of parametric models and/or in facilitating the progress of a control or optimization algorithm whose adaptive behavior relies on such models. For high-order tuners, robust stability properties, namely uniform global asymptotic (and exponential) stability, currently rely on a persistent excitation (PE) condition. In this work, we establish such stability properties with a novel analysis based on a Matrosov theorem and then show that the PE requirement can be relaxed via a concurrent learning technique driven by sampled data points that are sufficiently rich. We show numerically that concurrent learning may greatly improve efficiency. We incorporate reset methods that preserve the stability guarantees while providing additional improvements that may be relevant in applications that demand highly accurate parameter estimates at relatively low additional cost in computation.
Verifying Weak and Strong k-Step Opacity in Discrete-Event Systems
Authors: Jiří Balun, Tomáš Masopust
Date: 2022-04-04 00:00:00

Opacity is an important system-theoretic property expressing whether a system may reveal its secret to a passive observer (an intruder) who knows the structure of the system but has only limited observations of its behavior. Several notions of opacity have been discussed in the literature, including current-state opacity, k-step opacity, and infinite-step opacity. We investigate weak and strong k-step opacity, the notions that generalize both current-state opacity and infinite-step opacity, and ask whether the intruder is not able to decide, at any instant, when respectively whether the system was in a secret state during the last k observable steps. We design a new algorithm verifying weak k-step opacity, the complexity of which is lower than the complexity of existing algorithms and does not depend on the parameter k, and show how to use it to verify strong k-step opacity by reducing strong k-step opacity to weak k-step opacity. The complexity of the resulting algorithm is again better than the complexity of existing algorithms and does not depend on the parameter k.
Switched Max-Plus Linear-Dual Inequalities: Application in Scheduling of Multi-Product Processing Networks
Authors: Davide Zorzenon, Jan Komenda, Jörg Raisch
Date: 2022-04-03 00:00:00
Tasks: Scheduling

P-time event graphs are discrete event systems suitable for modeling processes in which tasks must be executed in predefined time windows. Their dynamics can be represented by systems of linear dynamical inequalities in the max-plus algebra and its dual, the min-plus algebra, referred to as max-plus linear-dual inequalities (LDIs). We define a new class of models called switched LDIs (SLDIs), which allow to switch between different modes of operations, each corresponding to an LDI, according to an infinite sequence of modes called schedule. In this paper, we focus on the analysis of SLDIs when the schedule is fixed and periodic. We show that SLDIs can model single-robot multi-product processing networks, in which every product has different processing requirements and corresponds to a specific mode of operation. Based on the analysis of SLDIs, we propose an algorithm to compute minimum and maximum cycle times for these processes that improves the time complexity of other existing approaches.
Control Co-design of a Hydrokinetic Turbine with Open-loop Optimal Control
Authors: Boxi Jiang, Mohammad Reza Amini, Yingqian Liao, Joaquim R. R. A. Martins, Jing Sun
Date: 2022-04-03 00:00:00

This paper introduces a control co-design (CCD) framework to simultaneously explore the physical parameters and control spaces for a hydro-kinetic turbine (HKT) rotor optimization. The optimization formulation incorporates a coupled dynamic-hydrodynamic model to maximize the rotor power efficiency for various time-variant flow profiles. The open-loop optimal control is applied for maximum power tracking, and the blade element momentum theory (BEMT) is used to model the hydrodynamics. Case studies with different control constraints are investigated for CCD. Sensitivity analyses were conducted with respect to different flow profiles and initial geometries. Comparisons are made between CCD and the sequential process, with physical design followed by a control design process under the same conditions. The results demonstrate the benefits of CCD and reveal that, with control constraints, CCD leads to increased energy production compared to the design obtained from the sequential design process.
Learning-Based Safe Motion Control of Vehicle Ski-Stunt Maneuvers
Authors: Feng Han, Jingang Yi
Date: 2022-04-03 00:00:00

This paper presents a safety guaranteed control method for an autonomous vehicle ski-stunt maneuver, that is, a vehicle moving with two one-side wheels. To capture the vehicle dynamics precisely, a Gaussian process model is used as additional correction to the nominal model that is obtained from physical principles. We construct a probabilistic control barrier function (CBF) to guarantee the planar motion safety. The CBF and the balance equilibrium manifold are enforced as the constraints into a safety critical control form. Under the proposed control method, the vehicle avoids the obstacle collision and safely maintain the balance for autonomous ski-stunt maneuvers. We conduct numerical simulation validation to demonstrate the control design. Preliminary experiment results are also presented to confirm the learning-based motion control using a scaled RC truck for autonomous ski-stunt maneuvers.
Hierarchical Frequency Control of Hybrid Power Plants Using Frequency Response Observer
Authors: Qian Long, Kaushik Das, Poul Sørensen
Date: 2022-04-03 00:00:00

Frequency control (FC) enables utility-scale grid-connected hybrid power plants (HPPs) to operate in compliance with grid code requirements while to capture value streams from provision of frequency control services (FCSs). In this paper, a novel hierarchical FC approach is proposed to allow HPPs to provide three types of FCSs, namely fast frequency response (FFR), frequency containment response (FCR) and frequency restoration response (FRR). To accommodate state-of-the-art fast FC, controllers for fast FCSs, such as FFR and FCR, are implemented at asset controllers, while controllers for slow FCSs like FRR are implemented at plant controllers or the HPP controller (HPPC). Control counteraction issue, which arises across control hierarchy, is then discussed. To solve this issue, an innovative frequency response observer (FROB) is proposed. Inspired by the concept of disturbance observer (DOB), FROB at plant controllers and the HPPC accurately estimates frequency response initiated at asset controllers, and the obtained estimation is used for control compensation at plant controllers and the HPPC to avoid control counteraction. This scheme achieves robust performance even when there are system uncertainties existing in HPPs, such as parameter uncertainty, unknown control malfunction, and time-varying communication delays. The proposed approach is implemented in a power system dynamic model in MATLAB/Simulink to highlight its effectiveness and robustness.
On Polynomially Solvable Constrained Input Selections for Fixed and Switched Linear Structured Systems
Authors: Yuan Zhang, Yuanqing Xia, Shenyu Liu, Zhongqi Sun
Date: 2022-04-03 00:00:00

This paper investigates two related optimal input selection problems for fixed (non-switched) and switched structured systems. More precisely, we consider selecting the minimum cost of inputs from a prior set of inputs, and selecting the inputs of the smallest possible cost with a bound on their cardinality, all to ensure system structural controllability. Those problems have attracted much attention recently; unfortunately, they are NP-hard in general. In this paper, it is found that, if the input structure satisfies certain `regularizations', which are characterized by the proposed restricted total unimodulairty notion, those problems can be solvable in polynomial time via linear programming (LP) relaxations. Particularly, the obtained characterizations depend only on the incidence matrix relating the inputs and the source strongly connected components (SCC) of the system structure, irrespective of how the inputs actuate states within the same SCC. They cover all the currently known polynomially solvable cases (such as the dedicated input case), and contain many new cases unexploited in the past, among which the source-SCC separated input (SSSI) constraint is highlighted. Further, for switched systems, the obtained polynomially solvable condition (namely the joint SSSI constraint) does not require each of the subsystems to satisfy the SSSI constraint. We achieve these by first formulating those problems as equivalent integer linear programmings (ILPs), and then proving total unimodularity of the corresponding constraint matrices. This property allows us to solve those ILPs efficiently via LP-relaxation. We also discuss solutions obtained via LP-relaxation and LP-rounding in the general case. Several examples are given to illustrate the obtained theoretical results.
Modelling and Control of Production Systems based on Observed Inter-event Times: An Analytical and Empirical Investigation (Ph.D. Thesis)
Authors: Nima Manafzadeh Dizbin
Date: 2022-04-03 00:00:00

Technological advances allow manufacturers to collect and access data from a production system effectively. The objective of data collection is to deploy the collected data in developing decision support systems for performance evaluation, problem identification, and production control. The goal of this dissertation is to investigate how the collected data can be used to evaluate performance and optimize manufacturing systems, analytically and empirically. In the first part of the thesis, I investigate how can the collected data from the shop-floor be used in the efficient control and design of manufacturing systems? To investigate the impact of possible dependency in the inter-event times on the optimal control and performance measures of the system, a manufacturing system that is controlled by using a single-threshold production control policy is analyzed. It is shown that ignoring autocorrelation in inter-arrival or service times leads to overestimation (underestimation) of the optimal threshold level for negatively (positively) correlated processes. Then, it is shown that the optimal control policy of a production/inventory system with correlated inter-arrival and service times modeled as Markovian Arrival Processes is a state-dependent threshold policy. In the second part of the thesis, an exploratory data analysis is conducted by using a large industrial dataset related to the flow of the products at a semiconductor wafer fabrication. Then, the cycle times of the products are predicted using Machine Learning methods, and the performance of different prediction algorithms is assessed. The analytical and empirical results presented in this dissertation show that effective use of collected data from a manufacturing system enables controlling the system effectively and predicting its main performance measures accurately.
3D Path Following and L1 Adaptive Control for Underwater Vehicles
Authors: Nicholas Rober, Maxwell Hammond, Venanzio Cichella, Juan E. Martin, Pablo Carrica
Date: 2022-04-02 00:00:00

This paper addresses the problem of guidance and control of underwater vehicles. A multi-level control strategy is used to determine (1) outer-loop path-following commands and (2) inner-loop actuation commands. Specifically, a line-of-sight path-following algorithm is used to guide the vehicle along a three-dimensional path, and an $\mathcal{L}_1$ adaptive control algorithm is used to determine the low-level rudder commands to accomplish path following. The performance bounds of these outer- and inner-loop control algorithms are presented. Numerical results obtained using a physics-based Simulink model are used to aid in visualization of the control algorithm's performance.
A probabilistic scheduling algorithm for networked control systems
Authors: Meghna Singh, Atreyee Kundu
Date: 2022-04-02 00:00:00
Tasks: Scheduling

This paper deals with the design of scheduling logics for Networked Control Systems (NCSs) whose communication networks have limited capacity. We assume that only a subset of the plants can communicate with their controllers at any time instant. Our contributions are twofold. First, we present a probabilistic algorithm to design scheduling logics that, under certain conditions on the plant and controller dynamics and the capacity of the network, ensure stochastic stability of each plant in an NCS. Second, given the plant dynamics and the capacity of the network, we design static state-feedback controllers such that the conditions for stability under our scheduling logics are satisfied. The main apparatus for our analysis is a Markovian jump linear system representation of the individual plants in an NCS. Our stability conditions involve sets of matrix inequalities. We present numerical experiments to demonstrate our results.
A Networked Competitive Multi-Virus SIR Model: Analysis and Observability
Authors: Ciyuan Zhang, Sebin Gracy, Tamer Basar, Philip E. Pare
Date: 2022-04-01 00:00:00
Tasks: State Estimation

This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network. First, we provide a sufficient condition for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. We present a sufficient condition for the model to be locally observable. We propose a Luenberger observer for the system state estimation and show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out.
Behavioral Feedback for Optimal LQG Control
Authors: Abed AlRahman Al Makdah, Vishaal Krishnan, Vaibhav Katewa, Fabio Pasqualetti
Date: 2022-04-01 00:00:00
Tasks: State Estimation

In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective. Motivated by the suitability of behavioral models for data-driven control, we begin with a reformulation of the LQG problem in the space of input-output behaviors and obtain a complete characterization of the optimal solutions. In particular, we show that the optimal LQG controller can be expressed as a static behavioral-feedback gain, thereby eliminating the need for dynamic state estimation characteristic of state space methods. The static form of the optimal LQG gain also makes it amenable to its computation by gradient descent, which we investigate via numerical experiments. Furthermore, we highlight the advantage of this approach in the data-driven control setting of learning the optimal LQG controller from expert demonstrations.