pwc-archive/papers-with-abstracts huggingface dataset:
Scale-free Distributed Cooperative Voltage Control of Inverter-based Microgrids with General Time-varying Communication Graphs
Authors: Donya Nojavanzadeh, Saeed Lotfifard, Zhenwei Liu, Ali Saberi, Anton A. Stoorvogel
Date: 2021-03-22 00:00:00

This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and fast plug and play operation in the presence of arbitrarily communication delays. The proposed scale-free distributed cooperative controller is independent of any information about the communication system and the size of the network (i.e., the number of distributed generators). The stability analysis of the proposed protocol is provided. The proposed method is simulated on the CIGRE medium voltage Microgrid test system. The simulation results demonstrate the feasibility of the proposed scale-free distributed nonlinear protocol for regulating the voltage of Microgrids in presence of communication failures, data packet loss, noise, and degradation.
An approximation for nonlinear differential-algebraic equations via singular perturbation theory
Authors: Yahao Chen, Stephan Trenn
Date: 2021-03-22 00:00:00

In this paper, we study jumps of nonlinear DAEs caused by inconsistent initial values. First, we propose a simple normal form called the index-1 nonlinear Weierstrass form (INWF) for nonlinear DAEs. Then we generalize the notion of consistency projector for linear DAEs to the nonlinear case. By an example, we compare our proposed nonlinear consistency projectors with two existing consistent initialization methods to show that the two existing methods are not coordinate-free, i.e., the consistent points calculated by the two methods are not invariant under nonlinear coordinates transformations. Next we propose a singular perturbed system approximation for nonlinear DAEs, which is an ordinary differential equation (ODE) with a small perturbation parameter, we show that the solutions of the proposed perturbation system approximate both the jumps resulting from the nonlinear consistency projectors and the $\mathcal C^1$-solutions of the DAE. At last, we use a numerical simulation of a nonlinear DAE model arising from an electric circuit to illustrate the effectiveness of the proposed singular perturbed system approximation of DAEs.
Moving from Linear to Conic Markets for Electricity
Authors: Anubhav Ratha, Pierre Pinson, Hélène Le Cadre, Ana Virag, Jalal Kazempour
Date: 2021-03-22 00:00:00

We propose a new forward electricity market framework that admits heterogeneous market participants with second-order cone strategy sets, who accurately express the nonlinearities in their costs and constraints through conic bids, and a network operator facing conic operational constraints. In contrast to the prevalent linear-programming-based electricity markets, we highlight how the inclusion of second-order cone constraints improves uncertainty-, asset- and network-awareness of the market, which is key to the successful transition towards an electricity system based on weather-dependent renewable energy sources. We analyze our general market-clearing proposal using conic duality theory to derive efficient spatially-differentiated prices for the multiple commodities, comprising of energy and flexibility services. Under the assumption of perfect competition, we prove the equivalence of the centrally-solved market-clearing optimization problem to a competitive spatial price equilibrium involving a set of rational and self-interested participants and a price setter. Finally, under common assumptions, we prove that moving towards conic markets does not incur the loss of desirable economic properties of markets, namely market efficiency, cost recovery and revenue adequacy. Our numerical studies focus on the specific use case of uncertainty-aware market design and demonstrate that the proposed conic market brings advantages over existing alternatives within the linear programming market framework.
Adaptive and Efficient Model Predictive Control for Booster Reentry
Authors: Joseph Chai, Eran Medagoda, Erkan Kayacan
Date: 2021-03-22 00:00:00
Tasks: Model Predictive Control

Engineering notes in Journal of Guidance, Control, and Dynamics does not have an abstract.
Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System
Authors: Jun Ma, Zilong Cheng, Haiyue Zhu, Xiaocong Li, Masayoshi Tomizuka, Tong Heng Lee
Date: 2021-03-22 00:00:00

Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features. The technology offers high-precision, contactless, dust/lubricant-free, multi-axis, and large-stroke positioning. In this work, we focus on the accurate and smooth tracking problem of a multi-axis magnetically levitated (maglev) planar positioning system for a specific S-curve reference trajectory. The floating characteristics and the multi-axis coupling make accurate identification of the system dynamics difficult, which lead to a challenge to design a high performance control system. Here, the tracking task is achieved by a 2-Degree of Freedom (DoF) controller consisting of a feedforward controller and a robust stabilizing feedback controller with a prescribed sparsity pattern. The approach proposed in this paper utilizes the basis of an H-infinity controller formulation and a suitably established convex inner approximation. Particularly, a subset of robust stabilizable controllers with prescribed structural constraints is characterized in the parameter space, and so thus the re-formulated convex optimization problem can be easily solved by several powerful numerical algorithms and solvers. With this approach, the robust stability of the overall system is ensured with a satisfactory system performance despite the presence of parametric uncertainties. Furthermore, experimental results clearly demonstrate the effectiveness of the proposed approach.
Switching Controller Synthesis for Delay Hybrid Systems under Perturbations
Authors: Yunjun Bai, Ting Gan, Li Jiao, Bican Xia, Bai Xue, Naijun Zhan
Date: 2021-03-22 00:00:00

Delays are ubiquitous in modern hybrid systems, which exhibit both continuous and discrete dynamical behaviors. Induced by signal transmission, conversion, the nature of plants, and so on, delays may appear either in the continuous evolution of a hybrid system such that the evolution depends not only on the present state but also on its execution history, or in the discrete switching between its different control modes. In this paper we come up with a new model of hybrid systems, called \emph{delay hybrid automata}, to capture the dynamics of systems with the aforementioned two kinds of delays. Furthermore, based upon this model we study the robust switching controller synthesis problem such that the controlled delay system is able to satisfy the specified safety properties regardless of perturbations. To the end, a novel method is proposed to synthesize switching controllers based on the computation of differential invariants for continuous evolution and backward reachable sets of discrete jumps with delays. Finally, we implement a prototypical tool of our approach and demonstrate it on some case studies.
Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads
Authors: Milan Jain, Soumya Kundu, Arnab Bhattacharya, Sen Huang, Vikas Chandan, Nikitha Radhakrishnan, Veronica Adetola, Draguna Vrabie
Date: 2021-03-21 00:00:00

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematic integration of zonal occupancy patterns to accurately identify short-term grid service opportunities. We evaluate the performance of the framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend, we demonstrate a way for the building operators to monitor the flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.
Cyber-Attack Detection in Socio-Technical Transportation Systems Exploiting Redundancies Between Physical and Social Data
Authors: Tanushree Roy, Sara Sattarzadeh, Satadru Dey
Date: 2021-03-21 00:00:00
Tasks: Cyber Attack Detection

Cyber-physical-social connectivity is a key element in Intelligent Transportation Systems (ITSs) due to the ever-increasing interaction between human users and technological systems. Such connectivity translates the ITSs into dynamical systems of socio-technical nature. Exploiting this socio-technical feature to our advantage, we propose a cyber-attack detection scheme for ITSs that focuses on cyber-attacks on freeway traffic infrastructure. The proposed scheme combines two parallel macroscopic traffic model-based Partial Differential Equation (PDE) filters whose output residuals are compared to make decision on attack occurrences. One of the filters utilizes physical (vehicle/infrastructure) sensor data as feedback whereas the other utilizes social data from human users' mobile devices as feedback. The Social Data-based Filter is aided by a fake data isolator and a social signal processor that translates the social information into usable feedback signals. Mathematical convergence properties are analyzed for the filters using Lyapunov's stability theory. Lastly, we validate our proposed scheme by presenting simulation results.
On the Role of Asymptomatic Carriers in Epidemic Spread Processes
Authors: Xiaoqi Bi, Carolyn L. Beck
Date: 2021-03-21 00:00:00
Tasks: parameter estimation

We present an epidemiological compartment model, SAIR(S), that explicitly captures the dynamics of asymptomatic infected individuals in an epidemic spread process. We first present a group model and then discuss networked versions. We provide an investigation of equilibria and stability properties for these models, and present simulation results illustrating the effects of asymptomatic-infected individuals on the spread of the disease. We also discuss local isolation effects on the epidemic dynamics in terms of the networked models. Finally, we provide initial parameter estimation results based on simple least-squares approaches and local test-site data.\par Keywords: Epidemic dynamics, networks, data-informed modeling, stability analysis, parameter estimation
Branching out into Structural Identifiability Analysis with Maple: Interactive Exploration of Uncontrolled Linear Time-Invariant Structures
Authors: Jason M. Whyte
Date: 2021-03-21 00:00:00
Tasks: Decision Making, Experimental Design

Suppose we wish to predict the behaviour of a physical system. We may choose to represent the system by model structure $S$ (a set of related mathematical models defined by parametric relationships between system variables), and a parameter set $\Theta$. Each parameter vector in $\Theta$ is associated with a completely specified model in $S$. We use $S$ with system observations in estimating the "true" (unknown) parameter vector. Inconveniently, multiple parameter vectors may cause $S$ to approximate the data equally well. If we cannot distinguish between such alternatives, and these lead to dissimilar predictions, we cannot confidently use $S$ in decision making. This result may render efforts in data collection and modelling fruitless. This outcome occurs when $S$ lacks the property of structural global identifiability (SGI). Fortunately, we can test various classes of structures for SGI prior to data collection. A non-SGI result may guide changes to our structure or experimental design towards obtaining a better outcome. We aim to assist the testing of structures for SGI through bespoke Maple 2020 procedures. We consider continuous-time, uncontrolled, linear time-invariant state-space structures. Here, the time evolution of the state-variable vector ${\bf x}$ is modelled by a system of constant-coefficient, ordinary differential equations. We utilise the "transfer function" approach, which is also applicable to the "compartmental" subclass (mass is conserved). Our use of Maple's "Explore" enables an interactive consideration of a parent structure and its variants, obtained as the user changes which components of ${\bf x}$ are observed, or have non-zero initial conditions. Such changes may influence the information content of the idealised output available for the SGI test, and hence, its result. Our approach may inform the interactive analysis of structures from other classes.
Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers
Authors: Erkan Kayacan, Thor I. Fossen
Date: 2021-03-21 00:00:00
Tasks: Self-Learning

This paper focuses on a novel feedback linearization control (FLC) law based on a self-learning disturbance observer (SLDO) to counteract mismatched uncertainties. The FLC based on BNDO (FLC-BNDO) demonstrates robust control performance only against mismatched time-invariant uncertainties while the FLC based on SLDO (FLC-SLDO) demonstrates robust control performance against mismatched time-invariant and -varying uncertainties, and both of them maintain the nominal control performance in the absence of mismatched uncertainties. In the estimation scheme for the SLDO, the BNDO is used to provide a conventional estimation law, which is used as being the learning error for the type-2 neuro-fuzzy system (T2NFS), and T2NFS learns mismatched uncertainties. Thus, the T2NFS takes the overall control of the estimation signal entirely in a very short time and gives unbiased estimation results for the disturbance. A novel learning algorithm established on sliding mode control theory is derived for an interval type-2 fuzzy logic system. The stability of the overall system is proven for a second-order nonlinear system with mismatched uncertainties. The simulation results show that the FLC-SLDO demonstrates better control performance than the traditional FLC, FLC with an integral action (FLC-I) and FLC-BNDO.
Sliding Mode Control for Systems with Mismatched Time-Varying Uncertainties via a Self-Learning Disturbance Observer
Authors: Erkan Kayacan
Date: 2021-03-21 00:00:00
Tasks: Self-Learning

This paper presents a novel Sliding Mode Control (SMC) algorithm to handle mismatched uncertainties in systems via a novel Self-Learning Disturbance Observer (SLDO). A computationally efficient SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a Neuro-Fuzzy Structure (NFS) work in parallel. In this framework, the NFS estimates the mismatched disturbances and becomes the leading disturbance estimator while the former feeds the learning error to the NFS to learn system behavior. The simulation results demonstrate that the proposed SMC based on SLDO (SMC-SLDO) ensures the robust control performance in the presence of mismatched time-varying uncertainties when compared to SMC, integral SMC (ISMC) and SMC based on a Basic Nonlinear Disturbance Observer (SMC-BNDO), and also remains the nominal control performance in the absence of mismatched uncertainties. Additionally, the SMC-SLDO not only counteracts mismatched time-varying uncertainties but also improve the transient response performance in the presence of mismatched time-invariant uncertainties. Moreover, the controller gain of the SMC-SLDO is required to be selected larger than the upper bound of the disturbance estimation error rather than the upper bound of the actual disturbance to guarantee the system stability which results in eliminating the chattering effects on the control signal.
Networked Supervisor Synthesis Against Lossy Channels with Bounded Network Delays as Non-Networked Synthesis
Authors: Liyong Lin, Yuting Zhu, Ruochen Tai, Simon Ware, Rong Su
Date: 2021-03-21 00:00:00

In this work, we study the problem of supervisory control of networked discrete event systems. We consider lossy communication channels with bounded network delays, for both the control channel and the observation channel. By a model transformation, we transform the networked supervisor synthesis problem into the classical (non-networked) supervisor synthesis problem (for non-deterministic plants), such that the existing supervisor synthesis tools can be used for synthesizing networked supervisors. In particular, we can use the (state-based) normality property for the synthesis of the supremal networked supervisors, whose existence is guaranteed by construction due to our consideration of command non-deterministic supervisors. The effectiveness of our approach is illustrated on a mini-guideway example that is adapted from the literature, for which the supremal networked supervisor has been synthesized in the synthesis tools SuSyNA and TCT.
Control and Simulation of a Grid-Forming Inverter for Hybrid PV-Battery Plants in Power System Black Start
Authors: Quan Nguyen, Mallikarjuna R. Vallem, Bharat Vyakaranam, Ahmad Tbaileh, Xinda Ke, Nader Samaan
Date: 2021-03-20 00:00:00

Power system restoration is an important part of system planning. Power utilities are required to maintain black start capable generators that can energize the transmission system and provide cranking power to non-blackstart capable generators. Traditionally, hydro and diesel units are used as black start capable generators. With the increased penetration of bulk size solar farms, inverter based generation can play an important role in faster and parallel black start thus ensuring system can be brought back into service without the conventional delays that can be expected with limited black start generators. Inverter-based photovoltaic (PV) power plants have advantages that are suitable for black start. This paper proposes the modeling, control, and simulation of a grid-forming inverter-based PV-battery power plant that can be used as a black start unit. The inverter control includes both primary and secondary control loops to imitate the control of a conventional synchronous machine. The proposed approach is verified using a test system modified from the IEEE 9-bus system in the time-domain electromagnetic transient simulation tool PSCAD. The simulation results shows voltage and frequency stability during a multi-step black-start and network energization process.
Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specifications
Authors: Cees F. Verdier, Niklas Kochdumper, Matthias Althoff, Manuel Mazo Jr
Date: 2020-06-07 00:00:00
Tasks: Form

We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial condition, we consider an (infinite and bounded) set of initial conditions. Candidate solutions are proposed using genetic programming, which evolves controllers based on a finite number of simulations. Subsequently, the best candidate is verified using reachability analysis; if the candidate solution does not satisfy the specification, an initial condition violating the specification is extracted as a counterexample. Based on this counterexample, candidate solutions are refined until eventually a solution is found (or a user-specified number of iterations is met). The resulting sampled-data controller is expressed as a closed-form expression, enabling both interpretability and the implementation in embedded hardware with limited memory and computation power. The effectiveness of our approach is demonstrated for multiple systems.
Probabilistic Performance Bounds for Randomized Sensor Selection in Kalman Filtering
Authors: Christopher I. Calle, Shaunak D. Bopardikar
Date: 2021-03-20 00:00:00

We consider the problem of randomly choosing the sensors of a linear time-invariant dynamical system subject to process and measurement noise. We sample the sensors independently and from the same distribution. We measure the performance of a Kalman filter by its estimation error covariance. Using tools from random matrix theory, we derive probabilistic bounds on the estimation error covariance in the semi-definite sense. We indirectly improve the performance of our Kalman filter for the maximum eigenvalue metric and show that under certain conditions the optimal sampling distribution that minimizes the maximum eigenvalue of the upper bound is the solution to an appropriately defined convex optimization problem. Our numerical results show the efficacy of the optimal sampling scheme in improving Kalman filter performance relative to the trivial uniform sampling distribution and a greedy sampling $\textit{with replacement}$ algorithm.
Comprehensive Analysis of Continuously Variable Series Reactor Using G-C Framework
Authors: Mohammadali Hayerikhiyavi, Aleksandar Dimitrovski
Date: 2021-03-20 00:00:00

Continuously Variable Series Reactor (CVSR) has the ability to regulate the reactance of an ac circuit using the magnetizing characteristics of its ferromagnetic core, shared by an ac and a dc winding to control power flow, damp oscillations and limit fault currents. In order to understand and utilize a CVSR in the power grid, it is essential to know all of its operational characteristics. The gyrator-capacitor approach has been applied to model electromagnetic coupling between the two circuits, controlled ac circuit and control dc circuit of the device. In this paper, we investigate some of the CVSR side behavior in terms of the induced voltage across the dc winding, flux density within the core's branches, and the power exchange between the two circuits during normal operation and fault conditions.
Water Need Models and Irrigation Decision Systems
Authors: Meriç Çetin, Senem Yıldız, Selami Beyhan
Date: 2021-03-20 00:00:00
Tasks: BIG-bench Machine Learning

Irrigation decision systems and water need models have been important research topics in agriculture since 90s. They improve the efficiency of crop yields, provide an appropriate use of water on the earth and so, prevent the water scarcity in some regions. In this paper, a comprehensive survey on water need models depending on crop growth and irrigation decision systems has been conducted based on mathematical maodelling. The following outcomes and solutions are the main contributions. Crop growth models and correspondingly water need models are suffer from un-modeled dynamics of the environment and lack of sensory devices. Literature review with the latest developments on water need models, irrigation decision systems, applied control methods and discussions are expected to be useful for the future strategies.