pwc-archive/papers-with-abstracts huggingface dataset:
Infinite-Horizon Optimal Wireless Control Over Shared State-Dependent Fading Channels for IIoT Systems
Authors: Shuling Wang, Peizhe Li, Shanying Zhu, Cailian Chen, Xinping Guan
Date: 2024-08-28 00:00:00

Heterogeneous systems consisting of a multiloop wireless control system (WCS) and a mobile agent system (MAS) are ubiquitous in Industrial Internet of Things systems. Within these systems, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise its performance, requiring joint coordination between the WCS and MAS. Such coordination introduces different time steps and hybrid state spaces consisting of logical components and continuous components. This paper focuses on the infinite-horizon optimal control of MAS to ensure the performance of WCS while minimizing an average cost for the heterogeneous system subject to safety constraints. A state-dependent fading channel is modeled to capture interference among transmission links, as well as the effects of mobile agents' movements on successful wireless transmission. In order to address the heterogeneous system dynamics, the optimal control problem is formulated as the optimal constrained set stabilization of the MAS by establishing a necessary and sufficient condition for the Lyapunov-like performance of WCS with the expected decay rates. Using the semi-tensor product of matrices, a constrained optimal state transition graph is constructed to encode the constrained system dynamics as well as objective function, which further reduces the problem into a minimum-mean cycle problem for the graph. By studying the properties of the graph, the feasibility is proven, and an effective algorithm is proposed for the construction of optimal input sequences. An illustrative example is provided to demonstrate effectiveness of the proposed method.
Compact Multi-Service Antenna for Sensing and Communication Using Reconfigurable Complementary Spiral Resonator
Authors: Ali Raza, Rasool Keshavarz, Eryk Dutkiewicz, Negin Shariati
Date: 2024-08-28 00:00:00

In this paper, a compact multi-service antenna (MSA) is presented for sensing and communication using a reconfigurable complementary spiral resonator. A three turns complementary spiral resonator (3-CSR) is inserted in the ground plane of a modified patch antenna to create a miniaturized structure. Two Positive-Intrinsic-Negative (PIN) diodes (D1, D2) are also integrated with the 3-CSR to achieve frequency reconfiguration. The proposed structure operates in three different modes i.e., dual-band joint communication and sensing antenna (JCASA), dual-band antenna, and single-band antenna. The required mode can be selected by changing the state of the PIN diodes. In mode-1, the first band (0.95-0.97 GHz) of the antenna is dedicated to sensing by using frequency domain reflectometry (FDR), while the second band (1.53-1.56 GHz) is allocated to communication. The sensing ability of the proposed structure is utilized to measure soil moisture using FDR. Based on the frequency shift, permittivity of the soil is observed to measure soil moisture. In mode-2 and mode-3, the structure operates as a standard dual and single band antenna, respectively, with a maximum gain of 1.5 dBi at 1.55 GHz. The proposed planar structure, with its simple geometry and a high sensitivity of 1.7%, is a suitable candidate for precision farming. The proposed structure is versatile and capable of being utilized as a single or dual-band antenna and also measuring permittivity of materials within the range of 1-20. Hence, it is adaptable to a range of applications.
Convergence Analysis of Gradient Flow for Overparameterized LQR Formulations
Authors: Arthur Castello B. de Oliveira, Milad Siami, Eduardo D. Sontag
Date: 2024-08-28 00:00:00

Motivated by the growing use of artificial intelligence (AI) tools in control design, this paper analyses the intersection between results from gradient methods for the model-free linear quadratic regulator (LQR), and linear feedforward neural networks (LFFNNs), More specifically, it looks into the case where one wants to find a LFFNN feedback that minimizes a LQR cost. This paper starts by analyzing the structure of the gradient expression for the parameters of each layer, which implies a key conservation law of the system. This conservation law is then leveraged to generalize existing results on boundedness and global convergence of solutions to critical points, and invariance of the set of stabilizing networks under the training dynamics. This is followed by an analysis of the case where the LFFNN has a single hidden layer, for which the paper proves that the training converges not only to the critical points, but to the optimal feedback control law for all but a set of Lebesgue measure zero of the initializations. These theoretical results are followed by an extensive analysis of a simple version of the problem -- the ``vector case'' -- proving the theoretical properties of accelerated convergence and small-input input-to-state stability (ISS) for this simpler example. Finally, the paper presents numerical evidence of faster convergence of the training of general LFFNNs when compared to non-overparameterized formulations, showing that the acceleration of the solution is observable even when the gradient is not explicitly computed, but estimated from evaluations of the cost function.
Hybrid Plant Models Call for a Different Plant Modelling Paradigm and a New Generation of Software (Heresy in the land of moles, fractions, & rigorous physical properties)
Authors: Vladimir Mahalec
Date: 2024-08-27 00:00:00
Tasks: Scheduling

This paper is an invitation to the process systems engineering community to change the paradigm for process plants. The goal is to achieve much easier convergence while retaining accuracy on par with the rigorous models. Accurate plant models of existing plants can be linear or much less nonlinear if they are based on mass component flows and stream properties per unit mass properties instead of molar flows and mole fractions. Accurate stream properties per unit mass can be calculated at stream specific conditions by linear approximations which in many instances eliminates mole fraction-based flash calculations. Hybrid data-driven node models fit naturally in this paradigm, since they used measured data, which is either in mass or in volumetric units, but never in moles. Instantiation of models at all levels of abstraction (planning, scheduling, optimization, and control models) from the same plant topology representation will ensure inheritance of solutions from mass-only to mass-and-energy to mass-and-energy-and-stream-properties, thereby ensuring consistency of solutions between these models. None of the existing software provides inheritance between different levels of plant abstraction (i.e. inheritance between models for different business applications) or different levels of abstractions per plant sections or per time periods, which motivates this exposition.
On Mobility Equity and the Promise of Emerging Transportation Systems
Authors: Heeseung Bang, Aditya Dave, Filippos N. Tzortzoglou, Shanting Wang, Andreas A. Malikopoulos
Date: 2024-08-27 00:00:00
Tasks: Fairness

This paper introduces a mobility equity metric (MEM) for evaluating fairness and accessibility in multi-modal intelligent transportation systems. The MEM simultaneously accounts for service accessibility and transportation costs across different modes of transportation and social demographics. We provide a data-driven validation of the proposed MEM to characterize the impact of various parameters in the metric across cities in the U.S. We subsequently develop a routing framework that aims to optimize MEM within a transportation network containing both public transit and private vehicles. Within this framework, a system planner provides routing suggestions to vehicles across all modes of transportation to maximize MEM. We evaluate our approach through numerical simulations, analyzing the impact of travel demands and compliance of private vehicles. This work provides insights into designing transportation systems that are not only efficient but also equitable, ensuring fair access to essential services across diverse populations.
Data-enabled Predictive Repetitive Control
Authors: Rogier Dinkla, Tom Oomen, Sebastiaan Mulders, Jan-Willem van Wingerden
Date: 2024-08-27 00:00:00
Tasks: LEMMA

Many systems are subject to periodic disturbances and exhibit repetitive behaviour. Model-based repetitive control employs knowledge of such periodicity to attenuate periodic disturbances and has seen a wide range of successful industrial implementations. The aim of this paper is to develop a data-driven repetitive control method. In the developed framework, linear periodically time-varying (LPTV) behaviour is lifted to linear time-invariant (LTI) behaviour. Periodic disturbance mitigation is enabled by developing an extension of Willems' fundamental lemma for systems with exogenous disturbances. The resulting Data-enabled Predictive Repetitive Control (DeePRC) technique accounts for periodic system behaviour to perform attenuation of a periodic disturbance. Simulations demonstrate the ability of DeePRC to effectively mitigate periodic disturbances in the presence of noise.
Applications in CityLearn Gym Environment for Multi-Objective Control Benchmarking in Grid-Interactive Buildings and Districts
Authors: Kingsley Nweye, Zoltan Nagy
Date: 2024-08-27 00:00:00
Tasks: Benchmarking, Model Predictive Control

It is challenging to coordinate multiple distributed energy resources in a single or multiple buildings to ensure efficient and flexible operation. Advanced control algorithms such as model predictive control and reinforcement learning control provide solutions to this problem by effectively managing a distribution of distributed energy resource control tasks while adapting to unique building characteristics, and cooperating towards improving multi-objective key performance indicator. Yet, a research gap for advanced control adoption is the ability to benchmark algorithm performance. CityLearn addresses this gap an open-source Gym environment for the easy implementation and benchmarking of simple rule-based control and advanced algorithms that has an advantage of modeling simplicity, multi-agent control, district-level objectives, and control resiliency assessment. Here we demonstrate the functionalities of CityLearn using 17 different building control problems that have varying complexity with respect to the number of controllable distributed energy resources in buildings, the simplicity of the control algorithm, the control objective, and district size.
Control-Informed Reinforcement Learning for Chemical Processes
Authors: Maximilian Bloor, Akhil Ahmed, Niki Kotecha, Mehmet Mercangöz, Calvin Tsay, Ehecactl Antonio Del Rio Chanona
Date: 2024-08-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement learning (RL) policies. The proposed approach augments deep RL agents with a PID controller layer, incorporating prior knowledge from control theory into the learning process. CIRL improves performance and robustness by combining the best of both worlds: the disturbance-rejection and setpoint-tracking capabilities of PID control and the nonlinear modeling capacity of deep RL. Simulation studies conducted on a continuously stirred tank reactor system demonstrate the improved performance of CIRL compared to both conventional model-free deep RL and static PID controllers. CIRL exhibits better setpoint-tracking ability, particularly when generalizing to trajectories outside the training distribution, suggesting enhanced generalization capabilities. Furthermore, the embedded prior control knowledge within the CIRL policy improves its robustness to unobserved system disturbances. The control-informed RL framework combines the strengths of classical control and reinforcement learning to develop sample-efficient and robust deep reinforcement learning algorithms, with potential applications in complex industrial systems.
Multitone PSK Modulation Design for Simultaneous Wireless Information and Power Transfer
Authors: Prerna Dhull, Graduate Student Member, Dominique Schreurs, Giacomo Paolini, Alessandra Costanzo, Mehran Abolhasan, Negin Shariati
Date: 2024-08-27 00:00:00

Far-field wireless power transfer, based on radio frequency (RF) waves, came into the picture to fulfill the power need of large Internet of Things (IoT) networks, the backbone of the 5G and beyond era. However, RF communication signals carry both information as well as energy. Therefore, recently, simultaneous wireless information and power transfer (SWIPT) has attracted much attention in order to wirelessly charge these IoT devices. In this paper, we propose a novel N -tone multitone phase shift keying (PSK) modulation scheme, taking advantage of the non-linearity of integrated receiver rectifier architecture. The main advantage of the proposed modulation scheme is the reduction in ripple voltage, introduced by the symbol transmission through phases. Achievable power conversion efficiency (PCE) and bit error rate (BER) at the output are considered to measure the efficacy of the proposed modulation scheme. Simulation results are verified by the measurements over the designed rectifier circuitry. The effect of symbol phase range, modulation order, and the number of tones are analyzed. In the future, this transmission scheme can be utilized to satisfy the data and power requirements of low-power Internet of Things sensor networks.
Fixed-time Disturbance Observer-Based MPC Robust Trajectory Tracking Control of Quadrotor
Authors: Liwen Xu, Bailing Tian, Cong Wang, Junjie Lu, Dandan Wang, Zhiyu Li, Qun Zong
Date: 2024-08-27 00:00:00
Tasks: Model Predictive Control

In this paper, a fixed-time disturbance observerbased model predictive control algorithm is proposed for trajectory tracking of quadrotor in the presence of disturbances. First, a novel multivariable fixed-time disturbance observer is proposed to estimate the lumped disturbances. The bi-limit homogeneity and Lyapunov techniques are employed to ensure the convergence of estimation error within a fixed convergence time, independent of the initial estimation error. Then, an observerbased model predictive control strategy is formulated to achieve robust trajectory tracking of quadrotor, attenuating the lumped disturbances and model uncertainties. Finally, simulations and real-world experiments are provided to illustrate the effectiveness of the proposed method.
Towards Safe Autonomous Intersection Management: Temporal Logic-based Safety Filters for Vehicle Coordination
Authors: Kaj Munhoz Arfvidsson, Frank J. Jiang, Karl H. Johansson, Jonas Mårtensson
Date: 2024-08-27 00:00:00
Tasks: Management

In this paper, we introduce a temporal logic-based safety filter for Autonomous Intersection Management (AIM), an emerging infrastructure technology for connected vehicles to coordinate traffic flow through intersections. Despite substantial work on AIM systems, the balance between intersection safety and efficiency persists as a significant challenge. Building on recent developments in formal methods that now have become computationally feasible for AIM applications, we introduce an approach that starts with a temporal logic specification for the intersection and then uses reachability analysis to compute safe time-state corridors for the connected vehicles that pass through the intersection. By analyzing these corridors, in contrast to single trajectories, we can make explicit design decisions regarding safety-efficiency trade-offs while taking each vehicle's decision uncertainty into account. Additionally, we compute safe driving limits to ensure that vehicles remain within their designated safe corridors. Combining these elements, we develop a service that provides safety filters for AIM coordination of connected vehicles. We evaluate the practical feasibility of our safety framework using a simulated 4-way intersection, showing that our approach performs in real-time for multiple scenarios.
Model Predictive Control for T-S Fuzzy Markovian Jump Systems Using Dynamic Prediction Optimization
Authors: Bin Zhang
Date: 2024-08-27 00:00:00
Tasks: Model Predictive Control

In this paper, the model predictive control (MPC) problem is investigated for the constrained discrete-time Takagi-Sugeno fuzzy Markovian jump systems (FMJSs) under imperfect premise matching rules. To strike a balance between initial feasible region, control performance, and online computation burden, a set of mode-dependent state feedback fuzzy controllers within the frame of dynamic prediction optimizing (DPO)-MPC is delicately designed with the perturbation variables produced by the predictive dynamics. The DPO-MPC controllers are implemented via two stages: at the first stage, terminal constraints sets companied with feedback gain are obtained by solving a ``min-max'' problem; at the second stage, and a set of perturbations is designed felicitously to enlarge the feasible region. Here, dynamic feedback gains are designed for off-line using matrix factorization technique, while the dynamic controller state is determined for online over a moving horizon to gradually guide the system state from the initial feasible region to the terminal constraint set. Sufficient conditions are provided to rigorously ensure the recursive feasibility of the proposed DPO-MPC scheme and the mean-square stability of the underlying FMJS. Finally, the efficacy of the proposed methods is demonstrated through a robot arm system example.
Learning-Based Adaptive Dynamic Routing with Stability Guarantee for a Single-Origin-Single-Destination Network
Authors: Yidan Wu, Feixiang Shu, Jianan Zhang, Li Jin
Date: 2024-08-27 00:00:00
Tasks: Computational Efficiency

We consider learning-based adaptive dynamic routing for a single-origin-single-destination queuing network with stability guarantees. Specifically, we study a class of generalized shortest path policies that can be parameterized by only two constants via a piecewise-linear function. Using the Foster-Lyapunov stability theory, we develop a criterion on the parameters to ensure mean boundedness of the traffic state. Then, we develop a policy iteration algorithm that learns the parameters from realized sample paths. Importantly, the piecewise-linear function is both integrated into the Lyapunov function for stability analysis and used as a proxy of the value function for policy iteration; hence, stability is inherently ensured for the learned policy. Finally, we demonstrate via a numerical example that the proposed algorithm learns a near-optimal routing policy with an acceptable optimality gap but significantly higher computational efficiency compared with a standard neural network-based algorithm.
Constructive Nonlinear Control of Underactuated Systems via Zero Dynamics Policies
Authors: William Compton, Ivan Dario Jimenez Rodriguez, Noel Csomay-Shanklin, Yisong Yue, Aaron D. Ames
Date: 2024-08-27 00:00:00

Stabilizing underactuated systems is an inherently challenging control task due to fundamental limitations on how the control input affects the unactuated dynamics. Decomposing the system into actuated (output) and unactuated (zero) coordinates provides useful insight as to how input enters the system dynamics. In this work, we leverage the structure of this decomposition to formalize the idea of Zero Dynamics Policies (ZDPs) -- a mapping from the unactuated coordinates to desired actuated coordinates. Specifically, we show that a ZDP exists in a neighborhood of the origin, and prove that combining output stabilization with a ZDP results in stability of the full system state. We detail a constructive method of obtaining ZDPs in a neighborhood of the origin, and propose a learning-based approach which leverages optimal control to obtain ZDPs with much larger regions of attraction. We demonstrate that such a paradigm can be used to stabilize the canonical underactuated system of the cartpole, and showcase an improvement over the nominal performance of LQR.
Energy Management for Prepaid Customers: A Linear Optimization Approach
Authors: Maitreyee Marathe, Line A. Roald
Date: 2024-08-27 00:00:00
Tasks: energy management, Management

With increasing energy prices, low income households are known to forego or minimize the use of electricity to save on energy costs. If a household is on a prepaid electricity program, it can be automatically and immediately disconnected from service if there is no balance in its prepaid account. Such households need to actively ration the amount of energy they use by deciding which appliances to use and for how long. We present a tool that helps households extend the availability of their critical appliances by limiting the use of discretionary ones, and prevent disconnections. The proposed method is based on a linear optimization problem that only uses average power demand as an input and can be solved to optimality using a simple greedy approach. We compare the model with two mixed-integer linear programming models that require more detailed demand forecasts and optimization solvers for implementation. In a numerical case study based on real household data, we assess the performance of the different models under different accuracy and granularity of demand forecasts. Our results show that our proposed linear model is much simpler to implement, while providing similar performance under realistic circumstances.
Stable dynamic pricing scheme independent of lane-choice models for high-occupancy-toll lanes
Authors: Wen-Long Jin, Xuting Wang, Yingyan Lou
Date: 2024-08-26 00:00:00

A stable dynamic pricing scheme is essential to guarantee the desired performance of high-occupancy-toll (HOT) lanes, where single-occupancy vehicles (SOVs) can pay a price to use the HOT lanes. But existing methods apply to either only one type of lane-choice models with unknown parameters or different types of lane-choice models but with known parameters. In this study we present a new dynamic pricing scheme that is stable and applies to different types of lane-choice models with unknown parameters. There are two operational objectives for operating HOT lanes: (i) to maintain the free-flow condition to guarantee the travel time reliability; and (ii) to maximize the HOT lanes' throughput to minimize the system's total delay. The traffic dynamics on both HOT and general purpose (GP) lanes are described by point queue models, where the queueing times are determined by the demands and capacities. We consider three types of lane-choice models: the multinomial logit model when SOVs share the same value of time, the vehicle-based user equilibrium model when SOVs' values of time are heterogeneous and follow a distribution, and a general lane-choice model. We demonstrate that the second objective is approximately equivalent to the social welfare optimization principle for the logit model. Observing that the dynamic price and the excess queueing time on the GP lanes are linearly correlated in all the lane-choice models, we propose a feedback control method to determine the dynamic prices based on two integral controllers. We further present a method to estimate the parameters of a lane-choice model once its type is known. Analytically we prove that the equilibrium state of the closed-loop system with constant demand patterns is ideal, since the two objectives are achieved in it, and that it is asymptotically stable. With numerical examples we verify the effectiveness of the solution method.