Advantages of OKID-ERA Identification in Control Systems. An Application to the Tennessee Eastman Plant

Data-driven OKID-ERA identification of the open-loop Tennessee Eastman plantis performed to obtain a linear model for control design purposes. Analysissuch as numerical conditioning, output response errors, and zero-pole mappingshighlight some definite advantages of the OKID-ERA approach when compared withmodels derived from typical linearization techniques. The plant under study isa recognized benchmark in the field of plant-wide control systems.Fil: Yapur, Sergio Federico. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

Models, metrics, and their formulas for typical electric power system resilience events

We define Poisson process models for outage and restore processes in power system resilience events in terms of their rates. These outage and restore processes easily yield the performance curves that track the evolution of resilience events, and their area, nadir and duration are standard resilience metrics. We derive explicit and intuitive formulas for these metrics for mean performance curves in terms of the model parameters; these parameters can be estimated from utility data. This clarifies the calculation of metrics of typical resilience events, and shows what they depend on. We give examples of the metric formulas using a typical model of transmission system outages with lognormal rate restoration, and also for constant rate and exponential rate restorations. We give similarly nice formulas for the area metric for empirical power system data

Features of Linear Models that May Compromise Model-Based, Plant-Wide Control Techniques: The Case of the Tennessee Eastman Plant

This work examines a set of features that impact the reliability of linearmodels within the context of plant-wide control design (PWC). The study case isthe Tennessee-Eastman (TE) plant. This benchmark problem is well-known forchallenging many control design approaches. Analyses involve eigenvalues,average errors between simulations, condition numbers, and loss of rank acrossfrequencies. These studies offer guidance for designing an effective plant-widecontrol system based on linear models.Fil: Yapur, Sergio Federico. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentin

Minimum-norm Sparse Perturbations for Opacity in Linear Systems

Opacity is a notion that describes an eavesdropper's inability to estimate a system's 'secret' states by observing the system's outputs. In this paper, we propose algorithms to compute the minimum sparse perturbation to be added to a system to make its initial states opaque. For these perturbations, we consider two sparsity constraints - structured and affine. We develop an algorithm to compute the global minimum-norm perturbation for the structured case. For the affine case, we use the global minimum solution of the structured case as initial point to compute a local minimum. Empirically, this local minimum is very close to the global minimum. We demonstrate our results via a running example.Comment: Submitted to Indian Control Conference, 2023 (6 pages

Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers

The investigation reported in this document focuses on identifying systems with symmetries using equivariant autoregressive reservoir computers. General results in structured matrix approximation theory are presented, exploring a two-fold approach. Firstly, a comprehensive examination of generic symmetry-preserving nonlinear time delay embedding is conducted. This involves analyzing time series data sampled from an equivariant system under study. Secondly, sparse least-squares methods are applied to discern approximate representations of the output coupling matrices. These matrices play a pivotal role in determining the nonlinear autoregressive representation of an equivariant system. The structural characteristics of these matrices are dictated by the set of symmetries inherent in the system. The document outlines prototypical algorithms derived from the described techniques, offering insight into their practical applications. Emphasis is placed on their effectiveness in the identification and predictive simulation of equivariant nonlinear systems, regardless of whether such systems exhibit chaotic behavior.Comment: The views expressed in the article do not necessarily represent the views of the National Commission of Banks and Insurance Companies of Hondura

An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic

As autonomous vehicles (AVs) become increasingly prevalent, their interaction with human drivers presents a critical challenge. Current AVs lack social awareness, causing behavior that is often awkward or unsafe. To combat this, social AVs, which are proactive rather than reactive in their behavior, have been explored in recent years. With knowledge of robot-human interaction dynamics, a social AV can influence a human driver to exhibit desired behaviors by strategically altering its own behaviors. In this paper, we present a novel framework for achieving human influence. The foundation of our framework lies in an innovative use of control barrier functions to formulate the desired objectives of influence as constraints in an optimal control problem. The computed controls gradually push the system state toward satisfaction of the objectives, e.g. slowing the human down to some desired speed. We demonstrate the proposed framework's feasibility in a variety of scenarios related to car-following and lane changes, including multi-robot and multi-human configurations. In two case studies, we validate the framework's effectiveness when applied to the problems of traffic flow optimization and aggressive behavior mitigation. Given these results, the main contribution of our framework is its versatility in a wide spectrum of influence objectives and mixed-autonomy configurations

Suppressing unknown disturbances to dynamical systems using machine learning

Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. In this Letter, we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with an example where a chaotic disturbance to the Lorenz system is identified and suppressed.Comment: 9 pages, 9 figures (including supplemental material

PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks

The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator -- PINNSim -- that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.Comment: submitted to the 23rd Power Systems Computation Conference (PSCC 2024