pwc-archive/papers-with-abstracts huggingface dataset:
A Self-Learning Disturbance Observer for Nonlinear Systems in Feedback-Error Learning Scheme
Authors: Erkan Kayacan, Joshua M. Peschel, Girish Chowdhary
Date: 2021-03-27 00:00:00
Tasks: Self-Learning

This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory. The SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a T2NFS work in parallel. In this scheme, the latter learns uncertainties and becomes the leading estimator whereas the former provides the learning error to the T2NFS for learning system dynamics. A learning algorithm established on SMC theory is derived for an interval type-2 fuzzy logic system. In addition to the stability of the learning algorithm, the stability of the SLDO and the stability of the overall system are proven in the presence of time-varying disturbances. Thanks to learning process by the T2NFS, the simulation results show that the SLDO is able to estimate time-varying disturbances precisely as distinct from the basic nonlinear disturbance observer (BNDO) so that the controller based on the SLDO ensures robust control performance for systems with time-varying uncertainties, and maintains nominal performance in the absence of uncertainties.
An Online Feedback-Based Linearized Power Flow Model for Unbalanced Distribution Networks
Authors: Rui Cheng, Zhaoyu Wang, Yifei Guo
Date: 2021-03-27 00:00:00

The non-linearity and non-convexity of power flow models and the phase coupling challenge the analysis and optimization of unbalanced distribution networks. To tackle the challenges, this paper proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model, and updates the model parameters via online feedback by leveraging the instantaneous measured voltages and load consumption at the previous time step. Its closed-loop nature can asymptotically mitigate the model mismatch, thus lending itself to a good performance. In addition, exploiting the connection structure of unbalanced radial distribution networks, we provide a unified matrix-vector compact form of the proposed linearized power flow model. Finally, the numerical tests on the IEEE 123-bus test system validate the effectiveness and superiority of the proposed model. A simple optimal power flow case is also provided to illustrate the application of the online feedback-based linearized model.
Backup Plan Constrained Model Predictive Control
Authors: Hunmin Kim, HyungJin Yoon, Wenbin Wan, Naira Hovakimyan, Lui Sha, Petros Voulgaris
Date: 2021-03-27 00:00:00
Tasks: Computational Efficiency, model, Model Predictive Control

This article proposes a new safety concept: backup plan safety. The backup plan safety is defined as the ability to complete one of the alternative missions in the case of primary mission abortion. To incorporate this new safety concept in control problems, we formulate a feasibility maximization problem that adopts additional (virtual) input horizons toward the alternative missions on top of the input horizon toward the primary mission. Cost functions for the primary and alternative missions construct multiple objectives, and multi-horizon inputs evaluate them. To address the feasibility maximization problem, we develop a multi-horizon multi-objective model predictive path integral control (3M) algorithm. Model predictive path integral control (MPPI) is a sampling-based scheme that can help the proposed algorithm deal with nonlinear dynamic systems and achieve computational efficiency by parallel computation. Simulations of the aerial vehicle and ground vehicle control problems demonstrate the new concept of backup plan safety and the performance of the proposed algorithm.
Personalized Adaptive Cruise Control and Impacts on Mixed Traffic
Authors: Mehmet Ozkan, Yao Ma
Date: 2021-03-26 00:00:00

This paper presents a personalized adaptive cruise control (PACC) design that can learn driver behavior and adaptively control the semi-autonomous vehicle (SAV) in the car-following scenario, and investigates its impacts on mixed traffic. In mixed traffic where the SAV and human-driven vehicles share the road, the SAV's driver can choose a PACC tuning that better fits the driver's preferred driving behaviors. The individual driver's preferences are learned through the inverse reinforcement learning (IRL) approach by recovering a unique cost function from the driver's demonstrated driving data that best explains the observed driving style. The proposed PACC design plans the motion of the SAV by minimizing the learned unique cost function considering the short preview information of the preceding human-driven vehicle. The results reveal that the learned driver model can identify and replicate the personalized driving behaviors accurately and consistently when following the preceding vehicle in a variety of traffic conditions. Furthermore, we investigated the impacts of the PACC with different drivers on mixed traffic by considering time headway, gap distance, and fuel economy assessments. A statistical investigation shows that the impacts of the PACC on mixed traffic vary among tested drivers due to their intrinsic driving preferences.
Statistical Modeling of the Human Body as an Extended Antenna
Authors: Thomas Wilding, Erik Leitinger, Ulrich Muehlmann, Klaus Witrisal
Date: 2021-03-25 00:00:00
Tasks: Position

In this paper we investigate the possibility of modeling a single antenna alone and in close proximity to a physical object by means of discrete point source scatterers. The scatter point model allows joint modeling of a physical antenna and the human body as a single extended object with direction dependent scattering coefficients for the scatter points. We introduce the term extended antenna describing antenna and human body together. To investigate the identifiability of the model parameters we make use of ultrawideband channel measurements and accurate ground truth position and orientation measurements obtained with an optical tracking system. By comparing measurements of the antenna attached directly to the user with measurements for the antenna without the user nearby, we show the shadowing and scattering effects of the human body and the antenna.
Verifying Compositional Refinement of Assume/Guarantee Contracts using Linear Programming
Authors: Miel Sharf, Bart Besselink, Karl Henrik Johansson
Date: 2021-03-25 00:00:00

Verifying specifications for large-scale modern engineering systems can be a time-consuming task, as most formal verification methods are limited to systems of modest size. Recently, contract-based design and verification has been proposed as a modular framework for specifications, and linear-programming-based techniques have been presented for verifying that a given system satisfies a given contract. In this paper, we extend this assume/guarantee framework by presenting necessary and sufficient conditions for a collection of contracts on individual components to refine a contract on the composed system. These conditions can be verified by solving linear programs, whose number grows linearly with the number of specifications defined by the contracts. We exemplify the tools developed using a case study considering safety in a car-following scenario, where noise and time-varying delay are considered.
Decentralized Coordination Between Economic Dispatch and Demand Response in Multi-Energy Systems
Authors: Zishun Liu, Shanying Zhu, Jinming Xu, Cailian Chen
Date: 2021-03-25 00:00:00

In this paper, we investigate the problem of coor?dination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs. Since multiple energy sources are coupled through energy hubs (EHs), the supply-demand constraints are nonconvex. To deal with this issue, we propose a linearization method to transform the coordination problem to a convex social welfare optimization one. Then a decentralized algorithm based on parallel Alternating Direction Method of Multipliers (ADMM) and dynamic average tracking protocol is developed, where each agent could only make decisions based on information from their neighbors. Moreover, by using variational inequality and Lyapunov-based techniques, we show that our algorithm could always converge to the global optimal solution. Finally, a case study on the modified IEEE 14-bus network verifies the feasibility and effectiveness of our algorithm.
Constrained Deep Learning Based Nonlinear Model Predictive Control
Authors: Farshid Asadi
Date: 2021-03-24 00:00:00
Tasks: Deep Learning, model, Model Predictive Control

Learning-based model predictive control (MPC) is an approach designed to reduce the computational cost of MPC. In this paper, a constrained deep neural network (DNN) design is proposed to learn MPC policy for nonlinear systems. Using constrained training of neural networks, MPC constraints are enforced effectively. Furthermore, recursive feasibility and robust stability conditions are derived for the learning-based MPC approach. Additionally, probabilistic feasibility and optimality empirical guarantees are provided for the learned control policy. The proposed algorithm is implemented on the Furuta pendulum and control performance is demonstrated and compared with the exact MPC and the normally trained learning-based MPC. The results show superior control performance and constraint satisfaction of the proposed approach.
Ripple-Type Control for Enhancing Resilience of Networked Physical Systems
Authors: Manish K. Singh, Guido Cavraro, Andrey Bernstein, Vassilis Kekatos
Date: 2021-03-24 00:00:00
Tasks: Vocal Bursts Type Prediction

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a "hotline" communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.
Distributed Newton-like Algorithms and Learning for Optimized Power Dispatch
Authors: Tor Anderson
Date: 2021-03-24 00:00:00
Tasks: Distributed Optimization

This thesis explores a particular class of distributed optimization methods for various separable resource allocation problems, which are of high interest in a wide array of multi-agent settings. A distinctly motivating application for this thesis is real-time power dispatch of distributed energy resources for providing frequency control in a distribution grid or microgrid with high renewable energy penetration. In this application, it is paramount that agent data be shared as sparsely as possible in the interest of conserving user privacy, and it is required that algorithms scale gracefully as the network size increases to the order of thousands or millions of resources and devices. Distributed algorithms are naturally well-poised to address these challenges, in contrast to more traditional centralized algorithms which scale poorly and require global access to information. The class of distributed optimization methods explored here can be broadly described as Newton-like or second-order, implying utilization of second-derivative information of the cost functions, in contrast to well-studied gradient-based or first-order methods. We consider three formulations of separable resource-allocation problems and develop a Newton-like algorithm for each. The analysis and simulation studies in the subsequent chapters demonstrate the advantages of our approaches over existing methods; most commonly, we note that convergence rates are substantially improved. We supplement our algorithm development for these three problem formulations with a network design technique, in which we can construct a maximally-connected network by adding some edges to the underlying communication graph, and a real demonstration of distributed algorithms on a large set of heterogeneous devices on the UC San Diego microgrid.
A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multi-rotor UAV
Authors: Connor J Boss, Vaibhav Srivastava
Date: 2021-03-24 00:00:00
Tasks: Position

Using the context of trajectory estimation and tracking for multi-rotor unmanned aerial vehicles (UAVs), we explore the challenges in applying high-gain observers to highly dynamic systems. The multi-rotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multi-rotor and the position of the reference system. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multi-rotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multi-rotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion.
Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles
Authors: Baldomero Coll-Perales, Joschua Schulte-Tigges, Michele Rondinone, Javier Gozalvez, Michael Reke, Dominik Matheis, Thomas Walter
Date: 2021-03-24 00:00:00
Tasks: Management

Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.
Flatness-based MPC for underactuated surface vessels in confined areas
Authors: Simon Helling, Max Lutz, Thomas Meurer
Date: 2021-03-24 00:00:00
Tasks: Collision Avoidance

A two-phase model predictive controller (MPC) is proposed for underactuated surface vessel operation in confined environments. For general driving maneuvers (phase one) the ship's geometry is not considered explicitly while in more restricted areas (stage two) which occur, e.g., in mooring maneuvers, the ship's geometry is approximated to ensure collision avoidance. To remove the dynamical constraint in the problem setup, the differential flatness of the fully actuated system is exploited and the flat outputs are parameterized using B-spline functions. Underactuated behavior is retained by means of inequality constraints that are imposed on the non-controllable input. In an effort to solve the MPC, a static nonlinear optimization problem is formulated and feasibility w.r.t. obstacles and actuator constraints is ensured at collocation points. Static obstacles are considered as constructive solid geometry functions in the MPC which also takes into account disturbances induced by wind.
A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty
Authors: Monimoy Bujarbaruah, Ugo Rosolia, Yvonne R. Stürz, Francesco Borrelli
Date: 2021-03-23 00:00:00

We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertainty is modeled as an additive disturbance and an additive error on the system dynamics matrices. Set based bounds for each component of the model uncertainty are assumed to be known. We separate the constraint tightening strategy into two parts, depending on the length of the MPC horizon. For a horizon length of one, the robust MPC problem is solved exactly, whereas for other horizon lengths, the model uncertainty is over-approximated with a net-additive component. The resulting MPC controller guarantees robust satisfaction of state and input constraints in closed-loop with the uncertain system. With appropriately designed terminal components and an adaptive horizon strategy, we prove the controller's recursive feasibility and stability of the origin. With numerical simulations, we demonstrate that our proposed approach gains up to 15x online computation speedup over a tube MPC strategy, while stabilizing about 98$\%$ of the latter's region of attraction.
Consistency Analysis of the Closed-loop SRIVC Estimator
Authors: Siqi Pan, James S. Welsh, Rodrigo A. Gonzalez, Cristian R. Rojas
Date: 2021-03-23 00:00:00

The Consistency of the Closed-Loop Simplified Refined Instrumental Variable method for Continuous-time system (CLSRIVC) is analysed based on sampled data. It is proven that the CLSRIVC estimator is not consistent when a continuous-time controller is used in the closed-loop.
Adaptive Fractional-Order Sliding Mode Controller with Neural Network Compensator for an Ultrasonic Motor
Authors: Xiaolong Chen, Wenyu Liang, Han Zhao, Abdullah Al Mamun
Date: 2021-03-23 00:00:00

Ultrasonic motors (USMs) are commonly used in aerospace, robotics, and medical devices, where fast and precise motion is needed. Remarkably, sliding mode controller (SMC) is an effective controller to achieve precision motion control of the USMs. To improve the tracking accuracy and lower the chattering in the SMC, the fractional-order calculus is introduced in the design of an adaptive SMC in this paper, namely, adaptive fractional-order SMC (AFOSMC), in which the bound of the uncertainty existing in the USMs is estimated by a designed adaptive law. Additionally, a short memory principle is employed to overcome the difficulty of implementing the fractional-order calculus on a practical system in real-time. Here, the short memory principle may increase the tracking errors because some information is lost during its operation. Thus, a compensator according to the framework of Bellman's optimal control theory is proposed so that the residual errors caused by the short memory principle can be attenuated. Lastly, experiments on a USM are conducted, which comparative results verify the performance of the designed controller.
Stochastic Hybrid Approximation for Uncertainty Management in Gas-Electric Systems
Authors: Conor O' Malley, Gabriela Hug, Line Roald
Date: 2021-03-23 00:00:00
Tasks: Management, Stochastic Optimization

Gas-fired generators, with their ability to quickly ramp up and down their electricity production, play an important role in managing renewable energy variability. However, these changes in electricity production translate into variability in the consumption of natural gas, and propagate uncertainty from the electric grid to the natural gas system. To ensure that both systems are operating safely, there is an increasing need for coordination and uncertainty management among the electricity and gas networks. A challenging aspect of this coordination is the consideration of natural gas dynamics, which play an important role at the time scale of interest, but give rise to a set of non-linear and non-convex equations that are hard to optimize over even in the deterministic case. Many conventional methods for stochastic optimization cannot be used because they either incorporate a large number of scenarios directly or require the underlying problem to be convex. To address these challenges, we propose using a Stochastic Hybrid Approximation algorithm to more efficiently solve these problems and investigate several different variants of this algorithm. In a case study, we demonstrate that the proposed technique is able to quickly obtain high quality solutions and outperforms existing benchmarks such as Generalized Benders Decomposition. We demonstrate that coordinated uncertainty management that accounts for the gas system can significantly reduce both electric and gas system load shed in stressed conditions.
On the Integration of Battery Electric Buses into Urban Bus Networks
Authors: Nicolas Dirks, Maximilian Schiffer, Grit Walther
Date: 2021-03-22 00:00:00

Cities all around the world struggle with urban air quality due to transportation related emissions. In public transport networks, replacing internal combustion engine buses by electric buses provides an opportunity to improve air quality. Hence, many bus network operators currently ask for an optimal transformation plan to integrate battery electric buses into their fleet. Ideally, this plan also considers the installation of necessary charging infrastructure to ensure a fleet's operational feasibility. Against this background, we introduce an integrated modeling approach to determine a cost-optimal, long-term, multi-period transformation plan for integrating battery electric buses into urban bus networks. Our model connects central strategic and operational decisions. We minimize total cost of ownership and analyze potential reductions of nitrogen oxide emissions. Our results base on a case study of a real-world bus network and show that a comprehensive integration of battery electric buses is feasible and economically beneficial. By analyzing the impact of battery capacities and charging power on the optimal fleet transformation, we show that medium-power charging facilities combined with medium-capacity batteries are superior to networks with low-power or high-power charging facilities.