pwc-archive/papers-with-abstracts huggingface dataset:
Robust remote estimation over the collision channel in the presence of an intelligent jammer
Authors: Xu Zhang, Marcos M. Vasconcelos
Date: 2022-04-01 00:00:00

We consider a sensor-receiver pair communicating over a wireless channel in the presence of a jammer who may launch a denial-of-service attack. We formulate a zero-sum game between a coordinator that jointly designs the transmission and estimation policies, and the jammer. We consider two cases depending on whether the jammer can sense the channel or not. We characterize a saddle-point equilibrium for the class of symmetric and unimodal probability density functions when the jammer cannot sense the channel. If the jammer can sense if the channel is being used, we provide an efficient algorithm that alternates between iterations of Projected Gradient Ascent and the Convex-Concave Procedure to find approximate First-order Nash-Equilibria. Our numerical results show that in certain cases the jammer may decide to launch a denial-of-service attack with the goal of deceiving the receiver even when the sensor decides not to transmit.
To Explore or Not to Explore: Regret-Based LTL Planning in Partially-Known Environments
Authors: Jianing Zhao, Keyi Zhu, Mingyang Feng, Xiang Yin
Date: 2022-04-01 00:00:00

In this paper, we investigate the optimal robot path planning problem for high-level specifications described by co-safe linear temporal logic (LTL) formulae. We consider the scenario where the map geometry of the workspace is partially-known. Specifically, we assume that there are some unknown regions, for which the robot does not know their successor regions a priori unless it reaches these regions physically. In contrast to the standard game-based approach that optimizes the worst-case cost, in the paper, we propose to use regret as a new metric for planning in such a partially-known environment. The regret of a plan under a fixed but unknown environment is the difference between the actual cost incurred and the best-response cost the robot could have achieved if it realizes the actual environment with hindsight. We provide an effective algorithm for finding an optimal plan that satisfies the LTL specification while minimizing its regret. A case study on firefighting robots is provided to illustrate the proposed framework. We argue that the new metric is more suitable for the scenario of partially-known environment since it captures the trade-off between the actual cost spent and the potential benefit one may obtain for exploring an unknown region.
Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC
Authors: Johannes Pohlodek, Bruno Morabito, Christian Schlauch, Pablo Zometa, Rolf Findeisen
Date: 2022-03-25 00:00:00
Tasks: BIG-bench Machine Learning, Code Generation, Model Predictive Control, parameter estimation

Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model-based approaches. Thanks to recent technological advancements in digitalization, machine learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model-based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model-based control and optimization is increasing rapidly. This paper outlines the basic ideas and principles behind an easy-to-use Python toolbox that allows to quickly and efficiently solve machine-learning-supported optimization, model predictive control, and estimation problems. The toolbox leverages state-of-the-art machine learning libraries to train components used to define the problem. It allows to efficiently solve the resulting optimization problems. Machine learning can be used for a broad spectrum of tasks, ranging from model predictive control for stabilization, setpoint tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick code generation for embedded MPC applications. HILO-MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives
Authors: Samik Sadhu, Hynek Hermansky
Date: 2022-03-31 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

How important are different temporal speech modulations for speech recognition? We answer this question from two complementary perspectives. Firstly, we quantify the amount of phonetic \textit{information} in the modulation spectrum of speech by computing the mutual information between temporal modulations with frame-wise phoneme labels. Looking from another perspective, we ask - which speech modulations an Automatic Speech Recognition (ASR) system prefers for its operation. Data-driven weights are learned over the modulation spectrum and optimized for an end-to-end ASR task. Both methods unanimously agree that speech information is mostly contained in slow modulation. Maximum mutual information occurs around 3-6 Hz which also happens to be the range of modulations most preferred by the ASR. In addition, we show that the incorporation of this knowledge into ASRs significantly reduces their dependency on the amount of training data.
Population Games With Erlang Clocks: Convergence to Nash Equilibria For Pairwise Comparison Dynamics
Authors: Semih Kara, Nuno C. Martins, Murat Arcak
Date: 2022-04-01 00:00:00
Tasks: valid

The prevailing methodology for analyzing population games and evolutionary dynamics in the large population limit assumes that a Poisson process (or clock) inherent to each agent determines when the agent can revise its strategy. Hence, such an approach presupposes exponentially distributed inter-revision intervals, and is inadequate for cases where each strategy entails a sequence of sub-tasks (sub-strategies) that must be completed before a new revision time occurs. This article proposes a methodology for such cases under the premise that a sub-strategy's duration is exponentially-distributed, leading to Erlang distributed inter-revision intervals. We assume that a so-called pairwise-comparison protocol captures the agents' revision preferences to render our analysis concrete. The presence of sub-strategies brings on additional dynamics that is incompatible with existing models and results. Our main contributions are twofold, both derived for a deterministic approximation valid for large populations. We prove convergence of the population's state to the Nash equilibrium set when a potential game generates a payoff for the strategies. We use system-theoretic passivity to determine conditions under which this convergence is guaranteed for contractive games.
Nonlinear VRFT with LASSO
Authors: Alexandre Sanfelici Bazanella, Diego Eckhard
Date: 2022-04-01 00:00:00

Virtual Reference Feedback Tuning (VRFT) is a well known and very successful data-driven control design method. It has been initially conceived for linear plants and this original formulation has been much explored in the literature, besides having already found many practical applications. A nonlinear version of VRFT has been proposed early on, but not much explored later on. In this paper we highlight various issues involved in the application of nonlinear VRFT and propose the inclusion of L1 regularization in its formulation. We illustrate by means of two simple examples the critical role played by two aspects: the L1 regularization and the choice of dictionary used to describe the nonlinearity of the controller.
Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design
Authors: Max Bolderman, Mircea Lazar, Hans Butler
Date: 2022-04-01 00:00:00

Model-based feedforward control improves tracking performance of motion systems, provided that the model describing the inverse dynamics is of sufficient accuracy. Model sets, such as neural networks (NNs) and physics-guided neural networks (PGNNs) are typically used as flexible parametrizations that enable accurate identification of the inverse system dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics directly. However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance. In order to push performance further, it is therefore crucial to account for noise when performing the identification. To address this problem, this paper proposes the use of a forward system identification using (PG)NNs from noisy data. Afterwards, two methods are proposed for inverting PGNNs to design a feedforward controller for high-precision motion control. The developed methodology is validated on a real-life industrial linear motor, where it showed significant improvements in tracking performance with respect to the direct inverse identification.
Algebraic connectivity of layered path graphs under node deletion
Authors: Ryusei Yoshise, Kaoru Yamamoto
Date: 2022-04-01 00:00:00

This paper studies the relation between node deletion and algebraic connectivity for graphs with a hierarchical structure represented by layers. To capture this structure, the concepts of layered path graph and its (sub)graph cone are introduced. The problem is motivated by a mobile robot formation control guided by a leader. In particular, we consider a scenario in which robots may leave the network resulting in the removal of the nodes and the associated edges. We show that the existence of at least one neighbor in the upper layer is crucial for the algebraic connectivity not to deteriorate by node deletion.
Impact of spatiotemporal heterogeneity in heat pump loads on generation and storage requirements
Authors: Claire E. Halloran, Filiberto Fele, Malcolm D. McCulloch
Date: 2022-04-01 00:00:00

This paper investigates how spatiotemporal heterogeneity in inflexible residential heat pump loads affects the need for storage and generation in the electricity system under business-as-usual and low-carbon emissions budgets. Homogeneous and heterogeneous heat pump loads are generated using population-weighted average and local temperature, respectively, assuming complete residential heat pump penetration. The results of a storage and generation optimal expansion model with network effects for spatiotemporally homogeneous and heterogeneous load profiles are compared. A case study is performed using a 3-bus network of London, Manchester, and Glasgow in Britain for load and weather data for representative weeks. Using heterogeneous heating demand data changes storage sizing: under a business-as-usual budget, 26% more total storage is built on an energy and power basis, and this storage is distributed among all of the buses in the heterogeneous case. Under a low-carbon budget, total energy storage at all buses increases 2 times on an energy basis and 40% on a power basis. The energy to power ratio of storage at each bus also increases when accounting for heterogeneity; this change suggests that storage will be needed to provide energy support in addition to power support for electric heating in high-renewable power systems. Accounting for heterogeneity also increases modeled systems costs, particularly capital costs, because of the need for higher generation capacity in the largest load center and coincidence of local peak demand at different buses. These results show the importance of accounting for heat pump load heterogeneity in power system planning.
Self-triggered MPC robust to bounded packet loss via a min-max approach: extended version
Authors: Stefan Wildhagen, Matthias Pezzutto, Luca Schenato, Frank Allgöwer
Date: 2022-04-01 00:00:00
Tasks: Model Predictive Control

Networked Control Systems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law. A method that allows to consider hard constraints on the communication traffic as well as on states and inputs is self-triggered model predictive control (MPC). In this scheme, the optimal length of the sampling interval is determined proactively using predictions of the system behavior. However, previous formulations of self-triggered MPC have neglected the widespread phenomenon of packet loss, such that these approaches might fail in practice. In this paper, we present a novel self-triggered MPC scheme which is robust to bounded packet loss by virtue of a min-max optimization problem. We prove recursive feasibility, constraint satisfaction and convergence to the origin for any possible packet loss realization consistent with the boundedness constraint, and demonstrate the advantages of the proposed scheme in a numerical example.
Towards gain tuning for numerical KKL observers
Authors: Mona Buisson-Fenet, Lukas Bahr, Valery Morgenthaler, Florent Di Meglio
Date: 2022-04-01 00:00:00
Tasks: regression, Sensitivity

This paper presents a first step towards tuning observers for general nonlinear systems. Relying on recent results around Kazantzis-Kravaris/Luenberger (KKL) observers, we propose an empirical criterion to guide the calibration of the observer, by trading off transient performance and sensitivity to measurement noise. We parametrize the gain matrix and evaluate this criterion over a family of observers for different parameter values. We then use neural networks to learn the mapping between the observer and the nonlinear system, and present a novel method to sample the state-space efficiently for nonlinear regression. We illustrate the merits of this approach in numerical simulations.
The role of living laboratories in unlocking the potential of low-carbon energy technologies on the journey to net-zero
Authors: Zhong Fan, Jun Cao, Taskin Jamal, Chris Fogwill, Cephas Samende, Zoe Robinson, Fiona Polack, Mark Ormerod, Sharon George, Adam Peacock, David Healey
Date: 2022-04-01 00:00:00

We demonstrate the potential role of one of the largest at scale multi-vector Smart Energy Network Demonstrator (SEND).
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Authors: Noel Csomay-Shanklin, Andrew J. Taylor, Ugo Rosolia, Aaron D. Ames
Date: 2022-04-01 00:00:00
Tasks: Model Predictive Control

Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with different controllers running at multiple time scales. Yet traditional constructive methods for nonlinear controller synthesis typically "flatten" this hierarchy, focusing on a single time scale, and thereby limited the ability to make rigorous guarantees on constraint satisfaction that hold for the entire system. In this work we seek to address the stabilization of constrained nonlinear systems through a \textit{multi-rate} control architecture. This is accomplished by iteratively planning continuous reference trajectories for a nonlinear system using a linearized model and Model Predictive Control (MPC), and tracking said trajectories using the full-order nonlinear model and Control Lyapunov Functions (CLFs). Connecting these two levels of control design in a way that ensures constraint satisfaction is achieved through the use of \textit{B\'{e}zier curves}, which enable planning continuous trajectories respecting constraints by planning a sequence of discrete points. Our framework is encoded via convex optimization problems which may be efficiently solved, as demonstrated in simulation.
Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines
Authors: Armin Norouzi, Saeid Shahpouri, David Gordon, Alexander Winkler, Eugen Nuss, Dirk Abel, Jakob Andert, Mahdi Shahbakhti, Charles Robert Koch
Date: 2022-04-01 00:00:00
Tasks: Model Predictive Control

The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of machine learning (ML) and model predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of an Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5 Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the \nox~emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NOx emissions at a constant load by the imitative controller is similar to that of the online optimized MPC compared to the Cummins production controller. In addition, the imitative controller requires 50 times less computation time compared to that of the online MPC optimization.
Deep Learning based Model Predictive Control for Compression Ignition Engines
Authors: Armin Norouzi, Saeid Shahpouri, David Gordon, Alexander Winkler, Eugen Nuss, Dirk Abel, Jakob Andert, Mahdi Shahbakhti, Charles Robert Koch
Date: 2022-03-31 00:00:00
Tasks: Deep Learning, Model Predictive Control

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (\nox) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time.
Gallium Oxide Heterojunction Diodes for Improved High-Temperature Performance
Authors: Shahadat H. Sohel, Ramchandra Kotecha, Imran S Khan, Karen N. Heinselman, Sreekant Narumanchi, M Brooks Tellekamp, Andriy Zakutayev
Date: 2022-03-31 00:00:00
Tasks: Vocal Bursts Intensity Prediction

${\beta}$-Ga${_2}$O${_3}$ based semiconductor devices are expected to have significantly improved high-power and high-temperature performance due to its ultra-wide bandgap of close to 5 eV. However, the high-temperature operation of these ultra-wide-bandgap devices is usually limited by the relatively low 1-2 eV built-in potential at the Schottky barrier with most high-work-function metals. Here, we report heterojunction p-NiO/n-${\beta}$-Ga${_2}$O${_3}$ diodes fabrication and optimization for high-temperature device applications, demonstrating a current rectification ratio of more than 10${^6}$ at 410{\deg}C. The NiO heterojunction diode can achieve higher turn-on voltage and lower reverse leakage current compared to the Ni-based Schottky diode fabricated on the same single crystal ${\beta}$-Ga${_2}$O${_3}$ substrate, despite charge transport dominated by interfacial recombination. Electrical characterization and device modeling show that these advantages are due to a higher built-in potential and additional band offset. These results suggest that heterojunction p-n diodes based on ${\beta}$-Ga${_2}$O${_3}$ can significantly improve high-temperature electronic device and sensor performance.
On-line Estimation of Stability and Passivity Metrics
Authors: Shirantha Welikala, Hai Lin, Panos J. Antsaklis
Date: 2022-03-31 00:00:00

We consider the problem of on-line evaluation of critical characteristic parameters such as the L_2-gain (L2G), input feedforward passivity index (IFP) and output feedback passivity index (OFP) of non-linear systems using their input-output data. Typically, having an accurate measure of such "system indices" enables the application of systematic control design techniques. Moreover, if such system indices can efficiently be evaluated on-line, they can be exploited to device intelligent controller reconfiguration and fault-tolerant control techniques. However, the existing estimation methods of such system indices (i.e., L2G, IFP and OFP) are predominantly off-line, computationally inefficient, and require a large amount of actual or synthetically generated input-output trajectory data under some specific initial/terminal conditions. On the other hand, the existing on-line estimation methods take an averaging-based approach, which may be sub-optimal, computationally inefficient and susceptible to estimate saturation. In this paper, to overcome these challenges (in the on-line estimation of system indices), we establish and exploit several interesting theoretical results on a particular class of fractional function optimization problems. For comparison purposes, the details of an existing averaging-based approach are provided for the same on-line estimation problem. Finally, several numerical examples are discussed to demonstrate the proposed on-line estimation approach and to highlight our contributions.