pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Cooperative Driving at Signal-Free Intersections with Polynomial-Time Complexity
Authors: Huaxin Pei, Yuxiao Zhang, Yi Zhang, Shuo Feng
Date: 2021-04-28 00:00:00

Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years. However, existing cooperative driving strategies either suffer from computational complexity or cannot guarantee global optimality. To fill this research gap, this paper proposes an optimal and computationally efficient cooperative driving strategy with the polynomial-time complexity. By modeling the conflict relations among the vehicles, the solution space of the cooperative driving problem is completely represented by a newly designed small-size state space. Then, based on dynamic programming, the globally optimal solution can be searched inside the state space efficiently. It is proved that the proposed strategy can reduce the time complexity of computation from exponential to a small-degree polynomial. Simulation results further demonstrate that the proposed strategy can obtain the globally optimal solution within a limited computation time under various traffic demand settings.
Boundary controlled irreversible port-Hamiltonian systems
Authors: Hector Ramirez, Yann Le Gorrec, Bernhard Maschke
Date: 2021-04-27 00:00:00

Boundary controlled irreversible port-Hamiltonian systems (BC-IPHS) on 1-dimensional spatial domains are defined by extending the formulation of reversible BC-PHS to irreversible thermodynamic systems controlled at the boundaries of their spatial domains. The structure of BC-IPHS has clear physical interpretation, characterizing the coupling between energy storing and energy dissipating elements. By extending the definition of boundary port variables of BC-PHS to deal with the dissipative terms, a set of boundary port variables are defined such that BC-IPHS are passive with respect to a given set of conjugated inputs and outputs. As for finite dimensional IPHS, the first and second principle are satisfied as a structural property. Several examples are given to illustrate the proposed approach.
Mitigation of Saturated Cut-sets During Multiple Outages to Enhance Power System Security
Authors: Reetam Sen Biswas, Anamitra Pal, Trevor Werho, Vijay Vittal
Date: 2021-04-27 00:00:00

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages.
Quadratic Optimization-Based Nonlinear Control for Protein Conformation Prediction
Authors: Alireza Mohammadi, Mark W. Spong
Date: 2021-04-27 00:00:00
Tasks: Protein Folding

Predicting the final folded structure of protein molecules and simulating their folding pathways is of crucial importance for designing viral drugs and studying diseases such as Alzheimer's at the molecular level. To this end, this paper investigates the problem of protein conformation prediction under the constraint of avoiding high-entropy-loss routes during folding. Using the well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a protein molecule, this paper formulates the protein conformation prediction as a pointwise optimal control synthesis problem cast as a quadratic program (QP). It is shown that the KCM torques in the protein folding literature can be utilized for defining a reference vector field for the QP-based control generation problem. The resulting kinetostatic control torque inputs will be close to the KCM-based reference vector field and guaranteed to be constrained by a predetermined bound; hence, high-entropy-loss routes during folding are avoided while the energy of the molecule is decreased.
Greedy Sensor Selection for Weighted Linear Least Squares Estimation under Correlated Noise
Authors: Keigo Yamada, Yuji Saito, Taku Nonomura, Keisuke Asai
Date: 2021-04-27 00:00:00

Optimization of sensor selection has been studied to monitor complex and large-scale systems with data-driven linear reduced-order modeling. An algorithm for greedy sensor selection is presented under the assumption of correlated noise in the sensor signals. A noise model is given using truncated modes in reduced-order modeling, and sensor positions that are optimal for generalized least squares estimation are selected. The determinant of the covariance matrix of the estimation error is minimized by efficient one-rank computations in both underdetermined and overdetermined problems. The present study also reveals that the objective function with correlated noise is neither submodular nor supermodular. Several numerical experiments are conducted using randomly generated data and real-world data. The results show the effectiveness of the selection algorithm in terms of accuracy in the estimation of the states of large-dimensional measurement data.
Neuromimetic Control -- A Linear Model Paradigm
Authors: John Baillieul, Zexin Sun
Date: 2021-04-27 00:00:00
Tasks: model

Stylized models of the neurodynamics that underpin sensory motor control in animals are proposed and studied. The voluntary motions of animals are typically initiated by high level intentions created in the primary cortex through a combination of perceptions of the current state of the environment along with memories of past reactions to similar states. Muscle movements are produced as a result of neural processes in which the parallel activity of large multiplicities of neurons generate signals that collectively lead to desired actions. Essential to coordinated muscle movement are intentionality, prediction, regions of the cortex dealing with misperceptions of sensory cues, and a significant level of resilience with respect to disruptions in the neural pathways through which signals must propagate. While linear models of feedback control systems have been well studied over decades, this paper proposes and analyzes a class of models whose aims are to capture some of the essential features of neural control of movement. Whereas most linear models of feedback systems entail a state component whose dimension is higher than the number of inputs or outputs, the work that follows will treat models in which the numbers of input and output channels greatly exceed the state dimension. While we begin by considering continuous time systems, the aim will be to treat systems whose evolution involves classes of inputs that emulate neural spike trains. Within the proposed class of models, the paper will study resilience to channel dropouts, the ways in which noise and uncertainty can be mitigated by an appropriate notion of consensus among noisy inputs, and finally, by a simple model in which binary activations of a multiplicity of input channels produce a dynamic response that closely approximates the dynamics of a prescribed linear system whose inputs are continuous functions of time.
Sufficient Lyapunov conditions for exponential mean square stability of discrete-time systems with markovian delays (extended version)
Authors: Anastasia Impicciatore, Maria Teresa Grifa, Pierdomenico Pepe, Alessandro D'Innocenzo
Date: 2021-04-26 00:00:00

This paper introduces sufficient Lyapunov conditions guaranteeing exponential mean square stability of discrete-time systems with markovian delays. We provide a transformation of the discrete-time system with markovian delays into a discrete-time Markov jump system. Then, we extend sufficient Lyapunov conditions existing for the global asymptotic stability of discrete-time systems with delays digraphs to the mean square stability of discrete-time systems with markovian delays. Finally, an example is provided to illustrate the efficiency and advantage of the proposed method.
Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds
Authors: Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jiri Srba
Date: 2021-04-26 00:00:00

Storm water detention ponds are used to manage the discharge of rainfall runoff from urban areas to nearby streams. Their purpose is to reduce the hydraulic impact and sediment loads of the receiving waters. Detention ponds are currently designed based on static controls: the output flow of a pond is capped at a fixed value. This is not optimal with respect to the current infrastructure capacity and for some detention ponds it might even violate current regulations set by the European Water Framework Directive. We apply formal methods to synthesize (i.e., derive automatically) a safe and optimal active controller. We model the storm water detention pond, including the urban catchment area and the rain forecasts, as a hybrid Markov decision process. Subsequently, we use the tool Uppaal Stratego to synthesize a control strategy minimizing the cost related to pollution (optimality) while guaranteeing no emergency overflow of the detention pond (safety). Simulation results for an existing pond show that Uppaal Stratego can learn optimal strategies that prevent emergency overflows, where the current static control is not always able to prevent it. At the same time, our approach can improve sedimentation during low rain periods.
Distributed Eco-Driving Algorithm of Vehicle Platoon Using Traffic Light and Road Slope Information
Authors: Yan Wang, Rong Su, Wei Wang, Xiaoxu Liu, Bohui Wang
Date: 2021-04-26 00:00:00

This paper investigates the problem of ecological driving (eco-driving) of vehicle platoons. To reduce the probability of the platoon avoiding red lights and increase fuel efficiency, a two-layer control architecture is proposed. The first layer is in charge of optimizing the leader's long-term motion profile using the traffic light and road slope information. The long-term planning model is defined based on the reachability analysis of the platoon to the green light windows. An event-triggered mechanism is proposed to operate the long-term planning model. The second layer is the short-term adaptation, in which the leader attempts to follow the planning motion profile in real time, while the follower keeps track of the nearest preceding vehicle and the leader, to preserve the desired inter-vehicular distances. A Newton's method-based algorithm is implemented to effectively solve both the long-term planning and short-term adaptation problems. The effectiveness of the proposed formulation and algorithm is illustrated by the simulations.
Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems based on Sample Identifying Complexity and Deciphering Time
Authors: Kaoru Teranishi, Tomonori Sadamoto, Aranya Chakrabortty, Kiminao Kogiso
Date: 2021-04-26 00:00:00
Tasks: Relation

In the state-of-the-art literature on cryptography and control theory, there has been no systematic methodology of constructing cyber-physical systems that can achieve desired control performance while being protected against eavesdropping attacks. In this paper, we tackle this challenging problem. We first propose two novel notions referred to as sample identifying complexity and sample deciphering time in an encrypted-control framework. The former explicitly captures the relation between the dynamical characteristics of control systems and the level of identifiability of the systems while the latter shows the relation between the computation time for the identification and the key length of a cryptosystem. Based on these two tractable new notions, we propose a systematic method for designing the both of an optimal key length to prevent system identification with a given precision within a given life span of systems, and of an optimal controller to maximize both of the control performance and the difficulty of the identification. The efficiency of the proposed method in terms of security level and realtime-ness is investigated through numerical simulations. To the best of our knowledge, this paper first connect the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective.
Intelligent Internal Temperature Control of Food in Standard Convection Ovens
Authors: Ramon Gloumakov
Date: 2021-04-25 00:00:00

This paper introduces a feedback-based temperature controller design for intelligent regulation of food internal temperature inside of standard convection ovens. Typical convection ovens employ an open-loop control system that requires a person to estimate the amount of time needed to cook foods in order to achieve desired internal temperatures. This approach, however, can result in undesired results with the final food temperatures being too high or too low due to the inherent difficulty in accurately predicting required cooking times without continuously measuring internal states and accounting for noise in the system. By implementing and introducing a feedback controller with a full-order Luenberger observer to create a closed-loop system, an oven can be instrumented to measure and regulate the internal temperatures of food in order to automatically control oven heat output and confidently achieve desired results.
Riemannian Trust-Region based Adaptive Kalman filter with unknown noise Covariance matrices
Authors: Rahul Moghe, Maruthi R. Akella, Renato Zanetti
Date: 2021-04-24 00:00:00

The problem of adaptive Kalman filtering for a discrete observable linear time-varying system with unknown noise covariance matrices is addressed in this paper. The measurement difference autocovariance method is used to formulate a linear least squares cost function containing the measurements and the process and measurement noise covariance matrices. Subsequently, a Riemannian trust-region optimization approach is designed to minimize the least squares cost function and ensure symmetry and positive definiteness for the estimates of the noise covariance matrices. The noise covariance matrix estimates, under sufficient excitation of the system, are shown to converge to their unknown true values. Saliently, the exponential stability and convergence guarantees for the proposed adaptive Kalman filter to the optimal Kalman filter with known noise covariance matrices is shown to be achieved under the relatively mild assumptions of uniform observability and uniform controllability. Numerical simulations on a linear time-varying system demonstrate the effectiveness of the proposed adaptive filtering algorithm.
Automata-based Controller Synthesis for Stochastic Systems: A Game Framework via Approximate Probabilistic Relations
Authors: Bingzhuo Zhong, Abolfazl Lavaei, Majid Zamani, Marco Caccamo
Date: 2021-04-23 00:00:00

In this work, we propose an abstraction and refinement methodology for the controller synthesis of discrete-time stochastic systems to enforce complex logical properties expressed by deterministic finite automata (a.k.a. DFA). Our proposed scheme is based on a notion of so-called $(\epsilon,\delta)$-approximate probabilistic relations, allowing one to quantify the similarity between stochastic systems modeled by discrete-time stochastic games and their corresponding finite abstractions. Leveraging this type of relations, the lower bound for the probability of satisfying the desired specifications can be well ensured by refining controllers synthesized over abstract systems to the original games. Moreover, we propose an algorithmic procedure to construct such a relation for a particular class of nonlinear stochastic systems with slope restrictions on the nonlinearity. The proposed methods are demonstrated on a quadrotor example, and the results indicate that the desired lower bound for the probability of satisfaction is guaranteed.
Adaptive Feedback Regulator for Powered Lower-Limb Exoskeleton under Model Uncertainty
Authors: Kirtankumar Thakkar, Victor Paredes, Ayonga Hereid
Date: 2021-04-23 00:00:00

This paper presents a neural network (NN) based adaptive feedback regulator to ensure the lateral and longitudinal stability and regulate the desired walking velocity of a lower-limb exoskeleton under model uncertainty. The traditional model-based controllers for lower-limb exoskeletons often fail to stabilize the robot or accurately track the desired behaviors under model uncertainties or external disturbances. This paper proposes a neural network (NN) based online adaptive regulator that compensates for the unknown changes in model parameters and external disturbances by modifying the nominal joint trajectory. A gradient descent-based delta rule is implemented to update the weights of a single layer NN, which can be efficiently performed online by design. We demonstrate the performance of the presented regulator on ATALANTE, a fully actuated lower limb exoskeleton designed for paraplegic patients. The simulation results show that the proposed approach noticeably improves stability and the tracking performance of the system, despite significant changes in model parameters and large adversarial pushes.
Generalized Euler-Lotka equation for correlated cell divisions
Authors: Simone Pigolotti
Date: 2021-01-07 00:00:00
Tasks: valid

Cell division times in microbial populations display significant fluctuations, that impact the population growth rate in a non-trivial way. If fluctuations are uncorrelated among different cells, the population growth rate is predicted by the Euler-Lotka equation, which is a classic result in mathematical biology. However, cell division times can be significantly correlated, due to physical properties of cells that are passed through generations. In this paper, we derive an equation remarkably similar to the Euler-Lotka equation which is valid in the presence of correlations. Our exact result is based on large deviation theory and does not require particularly strong assumptions on the underlying dynamics. We apply our theory to a phenomenological model of bacterial cell division in E.coli and to experimental data. We find that the discrepancy between the growth rate predicted by the Euler-Lotka equation and our generalized version is relatively small, but large enough to be measurable by our approach.
Shannon information criterion for low-high diversity transition in Moran and Voter models
Authors: Gabriella D. Franco, Flavia M. D. Marquitti, Lucas D. Fernandes, Dan Braha, Marcus A. M. de Aguiar
Date: 2021-04-25 00:00:00
Tasks: Diversity

Mutation and drift play opposite roles in genetics. While mutation creates diversity, drift can cause gene variants to disappear, especially when they are rare. In the absence of natural selection and migration, the balance between the drift and mutation in a well-mixed population defines its diversity. The Moran model captures the effects of these two evolutionary forces and has a counterpart in social dynamics, known as the Voter model with external opinion influencers. Two extreme outcomes of the Voter model dynamics are consensus and coexistence of opinions, which correspond to low and high diversity in the Moran model. Here we use a Shannon's information-theoretic approach to characterize the smooth transition between the states of consensus and coexistence of opinions in the Voter model. Mapping the Moran into the Voter model we extend the results to the mutation-drift balance and characterize the transition between low and high diversity in finite populations. Describing the population as a network of connected individuals we show that the transition between the two regimes depends on the network topology of the population and on the possible asymmetries in the mutation rates.
Fatigue Life Estimation of Structures under Statistically and Spectrally Similar Variable Amplitude Loading
Authors: He-Wen-Xuan Li, David Chelidze
Date: 2021-04-26 00:00:00

Engineering fatigue is a very prevalent and dangerous phenomenon that limits the useful life span of mechanical structures. It can be found in most engineered structures and machinery that are made from metallic or composite materials. The life prediction methodology under variable amplitude loading (VAL), however, has not reached maturity due to the complexity of the loading. This complexity results in load sequence effects, which significantly alter the fatigue life. Since most of the realistic loading experienced by engineered structures is of variable amplitude nature, and the corresponding responses at the defect site are highly irregular, characterization of the VAL and its resulting damage is paramount. Fatigue life estimation under VAL conventionally uses methods based on the Miner's Rule or the cumulative damage rule (CDR) in conjunction with the rainflow counting method. However, CDR does not account for the load interaction/sequence effects induced by local residual stress (or crack opening stress) variations.
Oscillations of the number of immune system cells in a space-velocity thermostatted kinetic theory model of tumor growth
Authors: Léon Masurel, Carlo Bianca, Annie Lemarchand
Date: 2021-04-28 00:00:00

The competition between cancer cells and immune system cells in inhomogeneous conditions is described at cell scale within the framework of the thermostatted kinetic theory. Cell learning is reproduced by increased cell activity during favorable interactions. The cell activity fluctuations are controlled by a thermostat. The direction of cell velocity is changed according to stochastic rules mimicking a dense fluid. We develop a kinetic Monte Carlo algorithm inspired from the direct simulation Monte Carlo (DSMC) method initially used for dilute gases. The evolution of an initially localized tumor is analyzed. Qualitatively different behaviors are observed as the field regulating activity fluctuations decreases. For high field values, i.e. efficient thermalization, cancer is controlled. For small field values, cancer rapidly and monotonously escapes from immunosurveillance. For the critical field value separating these two domains, the 3E's of immunotherapy are reproduced, with an apparent initial elimination of cancer, a long quasi-equilibrium period followed by large fluctuations, and the final escape of cancer, even for a favored production of immune system cells. For field values slightly smaller than the critical value, more regular oscillations of the number of immune system cells are spontaneously observed in agreement with clinical observations. The antagonistic effects that the stimulation of the immune system may have on oncogenesis are reproduced in the model by activity-weighted rate constants for the autocatalytic productions of immune system cells and cancer cells. Local favorable conditions for the launching of the oscillations are met in the fluctuating inhomogeneous system, able to generate a small cluster of immune system cells with larger activities than those of the surrounding cancer cells.