pwc-archive/papers-with-abstracts huggingface dataset:
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Authors: Venkatraman Renganathan, Sleiman Safaoui, Aadi Kothari, Benjamin Gravell, Iman Shames, Tyler Summers
Date: 2022-01-05 00:00:00
Tasks: Model Predictive Control, Motion Planning

Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity. Robots with nonlinear dynamics and complex sensing modalities operating in an uncertain environment demand more careful consideration of how uncertainties propagate across stack layers. We propose a framework to integrate perception, motion planning, and control by explicitly incorporating perception and prediction uncertainties into planning so that risks of constraint violation can be mitigated. Specifically, we use a nonlinear model predictive control based steering law coupled with a decorrelation scheme based Unscented Kalman Filter for state and environment estimation to propagate the robot state and environment uncertainties. Subsequently, we use distributionally robust risk constraints to limit the risk in the presence of these uncertainties. Finally, we present a layered autonomy stack consisting of a nonlinear steering-based distributionally robust motion planning module and a reference trajectory tracking module. Our numerical experiments with nonlinear robot models and an urban driving simulator show the effectiveness of our proposed approaches.
Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits
Authors: Peng Hang, Chao Huang, Zhongxu Hu, Chen Lv
Date: 2022-01-05 00:00:00
Tasks: Decision Making

To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users. Under the proposed decision-making framework, in this work, a representative urban driving scenario, i.e. the unsignalized intersection, is investigated. Once the vehicle enters the focused zone, it will interact with other CAVs and make collaborative decisions. To evaluate the safety risk of surrounding vehicles and reduce the complexity of the decision-making algorithm, the driving risk assessment algorithm is designed with a Gaussian potential field approach. The decision-making cost function is constructed by considering the driving safety and passing efficiency of CAVs. Additionally, decision-making constraints are designed and include safety, comfort, efficiency, control and stability. Based on the cost function and constraints, the fuzzy coalitional game approach is applied to the decision-making issue of CAVs at unsignalized intersections. Two types of fuzzy coalitions are constructed that reflect both individual and social benefits. The benefit allocation in the two types of fuzzy coalitions is associated with the driving aggressiveness of CAVs. Finally, the effectiveness and feasibility of the proposed decision-making framework are verified with three test cases.
Driving Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections: A Differential Game Approach
Authors: Peng Hang, Chao Huang, Zhongxu Hu, Chen Lv
Date: 2022-01-05 00:00:00
Tasks: Autonomous Vehicles, Decision Making

Considering personalized driving preferences, a new decision-making framework is developed using a differential game approach to resolve the driving conflicts of autonomous vehicles (AVs) at unsignalized intersections. To realize human-like driving and personalized decision-making, driving aggressiveness is first defined for AVs. To improve driving safety, a Gaussian potential field model is built for collision risk assessment. Besides, in the proposed decision making framework, the collision risk assessment model is further used to reduce the computational complexity based on an event-triggered mechanism. In the construction of payoff function, both driving safety and passing efficiency are comprehensively considered, and the driving aggressiveness is also reflected. Two kinds of equilibrium solution to the differential game, i.e., the Nash equilibrium and Stackelberg equilibrium, are discussed and solved. Finally, the proposed decision making algorithm is tested through a hardware-in-the-loop testing platform, and its feasibility, effectiveness and real-time implementation performance are validated.
Bearing-based Autonomous Communication Relay Positioning under Field-of-View Constraints
Authors: Marco Fabris, Daniel Zelazo
Date: 2022-01-04 00:00:00
Tasks: Collision Avoidance, Position

This paper investigates the problem of communication relay establishment for multiple agent-based mobile units using a relay vehicle. The objective is to drive autonomously the relay vehicle to attain a position for communication relay establishment while maintaining the other vehicles inside of its field-of-view. A bearing-based control law is proposed for the relay drone and designed for both single and multiple agents. We also provide a collision avoidance scheme that ensures no collisions between the relay and other agents. Numerical simulations and experimental results are reported as well to show the efficacy of the proposed approach.
Adaptive model reduction and state estimation of agro-hydrological systems
Authors: Soumya R. Sahoo, Jinfeng Liu
Date: 2021-12-20 00:00:00
Tasks: State Estimation

Closed-loop irrigation can deliver a promising solution for precision irrigation. The accurate soil moisture (state) estimation is critical in implementing the closed-loop irrigation of agrohydrological systems. In general, the agricultural fields are high dimensional systems. Due to the high dimensionality for a large field, it is very challenging to solve an optimizationbased advanced state estimator like moving horizon estimation (MHE). This work addresses the aforementioned challenge and proposes a systematic approach for state estimation of large agricultural fields. We use a non-linear state-space model based on discretization of the cylindrical coordinate version of Richards equation to describe the agro-hydrological systems equipped with a central pivot irrigation system. We propose a structure-preserving adaptive model reduction method using trajectory-based unsupervised machine learning techniques. Furthermore, the adaptive MHE algorithm is developed based on an adaptive reduced model. The proposed algorithms are applied to a small simulated field to compare the performance of adaptive MHE over original MHE. Finally, the proposed approach is applied to a large-scale real agricultural field to test the effectiveness and superiority to address the current challenges. Extensive simulations are carried out to show the efficiency of the proposed approach.
Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors
Authors: Yitong Liu, Zhengshuo Li, Junbo Zhao
Date: 2021-12-20 00:00:00
Tasks: Computational Efficiency

To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model. It is applicable to both transmission and distribution systems and can achieve better robustness than the recent data-driven models. The key idea is to probabilistically constrain the worst-case errors through distributionally robust chance-constrained programming. It also allows guaranteeing the linearization accuracy for a chosen operating point. Comparison results with three recent LPF models demonstrate that the worst-case error of the RD-LPF model is significantly reduced over 2- to 70-fold while reducing the average error. A compromise between computational efficiency and accuracy can be achieved through different ambiguity sets and conversion methods.
Attention Based Communication and Control for Multi-UAV Path Planning
Authors: Hamid Shiri, Hyowoon Seo, Jihong Park, Mehdi Bennis
Date: 2021-12-20 00:00:00
Tasks: Decision Making

Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning. The ISHA mechanism is run by a communication helper collecting the state embeddings of UAVs and distributing an attention score vector to each UAV. The attention scores computed by ISHA identify how many interactions with other UAVs should be considered in each UAV's control decision-making. Simulation results corroborate that the ISHA-based communication and control framework achieves faster travel with lower inter-UAV collision risks than an MHA-aided baseline, particularly under limited communication resources.
On-line Estimation of the Parameters of the Windmill Power Coefficient
Authors: Alexey Bobtsov, Romeo Ortega, Stanislav Aranovskiy, Rafael Cisneros
Date: 2021-12-19 00:00:00

Wind turbines are often controlled to harvest the maximum power from the wind, which corresponds to the operation at the top of the bell-shaped power coefficient graph. Such a mode of operation may be achieved implementing an extremum seeking data-based strategy, which is an invasive technique that requires the injection of harmonic disturbances. Another approach is based on the knowledge of the analytic expression of the power coefficient function, an information usually unreliably provided by the turbine manufacturer. In this paper we propose a globally, exponentially convergent on-line estimator of the parameters entering into the windmill power coefficient function. This corresponds to the solution of an identification problem for a nonlinear, nonlinearly parameterized, underexcited system. To the best of our knowledge we have provided the first solution to this challenging, practically important, problem.
Data-Driven Reachability analysis and Support set Estimation with Christoffel Functions
Authors: Alex Devonport, Forest Yang, Laurent El Ghaoui, Murat Arcak
Date: 2021-12-18 00:00:00

We present algorithms for estimating the forward reachable set of a dynamical system using only a finite collection of independent and identically distributed samples. The produced estimate is the sublevel set of a function called an empirical inverse Christoffel function: empirical inverse Christoffel functions are known to provide good approximations to the support of probability distributions. In addition to reachability analysis, the same approach can be applied to general problems of estimating the support of a random variable, which has applications in data science towards detection of novelties and outliers in data sets. In applications where safety is a concern, having a guarantee of accuracy that holds on finite data sets is critical. In this paper, we prove such bounds for our algorithms under the Probably Approximately Correct (PAC) framework. In addition to applying classical Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models. The bound based on PAC-Bayes applies to a more general class of Christoffel functions than the VC dimension argument, and achieves greater sample efficiency in experiments.
Increasing the Efficiency of Photovoltaic Systems by Using Maximum Power Point Tracking (MPPT)
Authors: Alireza Tofigh Rihani, Majid Ghandchi
Date: 2021-12-18 00:00:00
Tasks: Point Tracking

Using Photovoltaic systems is gradually expanded by increasing energy demand. Abundance and availability of this energy, has turned to one of the most important sources of renewable energy. Unfortunately, photovoltaic systems have two big problems: first, those have very low energy conversion efficiency (in act between 12 and 42 percent under certain circumstances). Second, the power produced by the solar cell depends on nonlinear conditions such as solar radiation, temperature and charge feature. According to this, received power maximum of photovoltaic cells depends on different non-linear variables, it is necessary to be continuously traced, as maximum received power of the cell (by controller). In this research, the increasing efficiency of photovoltaic systems has been investigated by using Maximum Power Point Tracking (MPPT) in two different modes contained connected to the Grid and disconnected from the grid with simulation by MATLAB software. The obtained results showed that the proposed technique is able to improve the current, voltage and power output of photovoltaic cells.
Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences
Authors: Victor Fors, Björn Olofsson, Erik Frisk
Date: 2021-12-17 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle.
On w-Optimization of the Split Covariance Intersection Filter
Authors: Hao Li
Date: 2021-01-25 00:00:00

The split covariance intersection filter (split CIF) is a useful tool for general data fusion and has the potential to be applied in a variety of engineering tasks. An indispensable optimization step (referred to as w-optimization) involved in the split CIF concerns the performance and implementation efficiency of the Split CIF, but explanation on w-optimization is neglected in the paper [1] that provides a theoretical foundation for the Split CIF. This note complements [1] by providing a theoretical proof for the convexity of the w-optimization problem involved in the split CIF (convexity is always a desired property for optimization problems as it facilitates optimization considerably).
An Integrated Risk Assessment Process of Safety-Related Digital I&C Systems in Nuclear Power Plants
Authors: Hongbin Zhang, Han Bao, Tate Shorthill, Edward Quinn
Date: 2021-12-17 00:00:00

Upgrading the existing analog instrumentation and control (IC) systems to state-of-the-art digital IC (DIC) systems will greatly benefit existing light-water reactors (LWRs). However, the issue of software common cause failure (CCF) remains an obstacle in terms of qualification for digital technologies. Existing analyses of CCFs in I&C systems mainly focus on hardware failures. With the application and upgrading of new DIC systems, design flaws could cause software CCFs to become a potential threat to plant safety, considering that most redundancy designs use similar digital platforms or software in their operating and application systems. With complex multi-layer redundancy designs to meet the single failure criterion, these IC safety systems are of particular concern in U.S. Nuclear Regulatory Commission (NRC) licensing procedures. In Fiscal Year 2019, the Risk-Informed Systems Analysis (RISA) Pathway of the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DIC technologies for digital upgrades and designs. An integrated risk assessment for the DIC (IRADIC) process was proposed for this strategy to identify potential key digital-induced failures, implement reliability analyses of related digital safety IC systems, and evaluate the unanalyzed sequences introduced by these failures (particularly software CCFs) at the plant level. This paper summarizes these RISA efforts in the risk analysis of safety-related DIC systems at Idaho National Laboratory.
Distributed Nash Equilibrium Seeking for Noncooperative Games of High-Order Nonlinear Multi-Agent Systems Over Weight-Unbalanced Digraphs
Authors: Zhenhua Deng, Jin Luo
Date: 2021-12-16 00:00:00

In this paper, we investigate the noncooperative games of multi-agent systems. Different from existing noncooperative games, our formulation involves the high-order nonlinear dynamics of players, and the communication topologies among players are weight-unbalanced digraphs. Due to the high-order nonlinear dynamics and the weight-unbalanced digraphs, existing Nash equilibrium seeking algorithms cannot solve our problem. In order to seek the Nash equilibrium of the noncooperative games, we propose two distributed algorithms based on state feedback and output feedback, respectively. Moreover, we analyze the convergence of the two algorithms with the help of variational analysis and Lyapunov stability theory. By the two algorithms, the high-order nonlinear players exponentially converge to the Nash equilibrium. Finally, two simulation examples illustrate the effectiveness of the algorithms.
Reformulation of Matching Equation in Potential Energy Shaping
Authors: M. Reza J. Harandi, Hamid D. Taghirad
Date: 2021-12-16 00:00:00

Stabilization of an underactuated mechanical system may be accomplished by energy shaping. Interconnection and damping assignment passivity-based control is an approach based on total energy shaping by assigning desired kinetic and potential energy to the system. This method requires solving a partial differential equation (PDE) related to he potential energy shaping of the system. In this short paper, we focus on the reformulation of this PDE to be solved easier. For this purpose, under a certain condition that depends on the physical parameters and the controller gains, it is possible to merely solve the homogeneous part of potential energy PDE. Furthermore, it is shown that the condition may be reduced into a linear matrix inequality form. The results are applied to a number of benchmark systems.
An approach for the aggregation of power system controllers with different topologies
Authors: Jonas Pesente, Paulo Galassi, Leonardo Rodrigues, Felipe Crestani, Guilherme Justino, Rodrigo Ramos
Date: 2021-12-15 00:00:00

This paper proposes an approach to aggregate nonstructured power system controllers preserving the dynamical characteristics of the original devices. The method is based on linear operations that use the frequency response of the elements, resulting in an accurate input-output description of the equivalent controller when compared to the original ones. The developed method was applied to a model of the future interconnected Paraguayan-Argentinean power system to produce a dynamic equivalent used in a real-time simulator to test the special protection scheme needed for the safe operation of the this future system. Transient and small-signal stability studies presented matching simulation results in the time domain with significantly reduced computational burden and processing time.
Neural Network-based Power Flow Model
Authors: Thuan Pham, Xingpeng Li
Date: 2021-12-15 00:00:00
Tasks: model

Power flow analysis is used to evaluate the flow of electricity in the power system network. Power flow calculation is used to determine the steady-state variables of the system, such as the voltage magnitude/phase angle of each bus and the active/reactive power flow on each branch. The DC power flow model is a popular linear power flow model that is widely used in the power industry. Although it is fast and robust, it may lead to inaccurate line flow results for some transmission lines. Since renewable energy sources such as solar farms or offshore wind farms are usually located far away from the main grid, accurate line flow results on these critical lines are essential for power flow analysis due to the unpredictable nature of renewable energy. Data-driven methods can be used to partially address these inaccuracies by taking advantage of historical grid profiles. In this paper, a neural network (NN) model is trained to predict power flow results using historical power system data. Although the training process may take time, once trained, it is very fast to estimate line flows. A comprehensive performance analysis between the proposed NN-based power flow model and the traditional DC power flow model is conducted. It can be concluded that the proposed NN-based power flow model can find solutions quickly and more accurately than DC power flow model.
Inverse Optimal Safety Filters
Authors: Miroslav Krstic
Date: 2021-12-15 00:00:00

CBF-QP safety filters are pointwise minimizers of the control effort at a given state vector, i.e., myopically optimal at each time instant. But are they optimal over the entire infinite time horizon? What does it even mean for a controlled dynamic systems to be "optimally safe" as opposed to, conventionally "optimally stable"? When disturbances, deterministic and stochastic, have unknown upper bounds, how should safety be defined to allow a graceful degradation under disturbances? Can safety filters be designed to guarantee such weaker safety properties as well as the optimality of safety over the infinite time horizon? We pose and answer these questions for general systems affine in control and disturbances and illustrate the answers using several examples. In the process, using the existing QP safety filters, as well as more general safety-ensuring feedbacks, we generate entire families of safety filters which are optimal over the infinite horizon though they are conservative (favoring safety over `alertness') relative to the standard QP.
A Predictive Online Transient Stability Assessment with Hierarchical Generative Adversarial Networks
Authors: Rui Ma, Sara Eftekharnejad, Chen Zhong, Mustafa Cenk Gursoy
Date: 2021-12-15 00:00:00
Tasks: Time Series, Time Series Analysis

Online transient stability assessment (TSA) is essential for secure and stable power system operations. The growing number of Phasor Measurement Units (PMUs) brings about massive sources of data that can enhance online TSA. However, conventional data-driven methods require large amounts of transient data to correctly assess the transient stability state of a system. In this paper, a new data-driven TSA approach is developed for TSA with fewer data compared to the conventional methods. The data reduction is enabled by learning the dynamic behaviors of the historical transient data using generative and adversarial networks (GAN). This knowledge is used online to predict the voltage time series data after a transient event. A classifier embedded in the generative network deploys the predicted post-contingency data to determine the stability of the system following a fault. The developed GAN-based TSA approach preserves the spatial and temporal correlations that exist in multivariate PMU time series data. Hence, in comparison with the state-of-the-art TSA methods, it achieves a higher assessment accuracy using only one sample of the measured data and a shorter response time. Case studies conducted on the IEEE 118-bus system demonstrate the superior performance of the GAN-based method compared to the conventional data-driven techniques.