pwc-archive/papers-with-abstracts huggingface dataset:
Unified incremental nonlinear controller for the transition control of a hybrid dual-axis tilting rotor quad-plane
Authors: Alessandro Mancinelli, Bart D. W. Remes, Guido C. H. E. de Croon, Ewoud J. J. Smeur
Date: 2023-11-15 00:00:00

Overactuated Tilt Rotor Unmanned Aerial Vehicles are renowned for exceptional wind resistance and a broad operational range, which poses complex control challenges due to non-affine dynamics. Traditional solutions employ multi-state switched logic controllers for transitions. Our study introduces a novel unified incremental nonlinear controller for overactuated dual-axis tilting rotor quad-planes, seamlessly managing pitch, roll, and physical actuator commands. The control allocation problem is addressed using a SQP iterative optimization algorithm, well-suited for nonlinear actuator effectiveness in thrust vectoring vehicles. The controller design integrates desired roll and pitch angle inputs. These desired attitude angles are autonomously managed by the controller and then conveyed to the vehicle during slow airspeed phases, when the vehicle maintains its 6 DOF. We incorporate an AoA protection logic to prevent wing stall and a yaw rate reference model for coordinated turns. Flight tests confirm the controller's effectiveness in transitioning from hovering to forward flight, achieving desired vertical and lateral accelerations, and reverting to hovering.
A Low Rank Approach to Minimize Sensor-to-Actuator Communication in Finite Horizon Output Feedback
Authors: Antoine Aspeel, Jakob Nylof, Jing Shuang Li, Necmiye Ozay
Date: 2023-11-15 00:00:00

Many modern controllers are composed of different components that communicate in real-time over some network with limited resources. In this work, we are interested in designing a controller that can be implemented with a minimum number of sensor-to-actuator messages, while satisfying safety constraints over a finite horizon. For finite horizon problems, a linear time-varying controller with memory can be represented as a block-lower-triangular matrix. We show that the rank of this matrix exactly captures the minimum number of messages needed to be sent from the sensors to actuators to implement such a controller. Moreover, we introduce a novel matrix factorization called causal factorization that gives the required implementation. Finally, we show that the rank of the controller is the same as the rank of the Youla parameter, enabling the Youla parametrization (or analogous parametrizations) to be used to design the controller, which reduces the overall design problem into a rank minimization one over a convex set. Finally, convex relaxations for rank are used to demonstrate that our approach leads to 20-50% less messages on a simulation than a benchmark method.
Thermally-Resilient Soft Gripper for On-Orbit Operations
Authors: Fernando Ruiz, Begona Arrue, Anibal Ollero
Date: 2023-11-15 00:00:00

Research in soft manipulators has significantly enhanced object grasping capabilities, thanks to their adaptability to various shapes and sizes. Applying this technology to on-orbit servicing, especially during the capture and containment stages of active space debris removal missions, might offer a secure, adaptable, and cost-effective solution compared to the trend of increasing the degrees of freedom and complexity of the manipulator (e.g. ClearSpace, Astroscale). This work aims to conduct an experimental proof of concept, for which challenges such as radiation, vacuum, and microgravity are significant, but the predominant issue is ensuring effective operation in the extreme temperature swings, where flexible materials may exhibit cryogenic crystallization or drastic shifts in their elasticity. This work addresses this challenge through an initial stage of analytical modeling of the thermal dynamics inside the manipulator in orbit; which is then used for the development of a first experimental prototype tested with liquid nitrogen and heat guns. The multi-layered design for Low Earth Orbit (LEO) leverages the properties of TPU at low infill rates for lightweight inherent flexibility, silicone rubber ensuring structural integrity, PTFE (Teflon) for unparalleled thermal stability, and aerogel for insulation. The tendon-actuated servo-driven gripper is tested in the laboratory by varying the shape and size of objects during the grasping. The results, based on servomotor force metrics to assess the flexible manipulator's adaptability and object capture efficiency across temperature changes, affirm the concept's viability. Forces increase up to 220$\%$ in cryogenic conditions and decrease by no more than 50$\%$ at high temperatures.
Design and Implementation of a Hybrid Wireless Power and Communication System for Medical Implants
Authors: A. Khaleghi, A. Hasanvand, I. Balasingham
Date: 2023-11-15 00:00:00

Data collection and analysis from multiple implant nodes in humans can provide targeted medicine and treatment strategies that can prevent many chronic diseases. This data can be collected for a long time and processed using artificial intelligence (AI) techniques in a medical network for early detection and prevention of diseases. Additionally, machine learning (ML) algorithms can be applied for the analysis of big data for health monitoring of the population. Wireless powering, sensing, and communication are essential parts of future wireless implants that aim to achieve the aforementioned goals. In this paper, we present the technical development of a wireless implant that is powered by radio frequency (RF) at 401 MHz, with the sensor data being communicated to an on-body reader. The implant communication is based on two simultaneous wireless links: RF backscatter for implant-to-on-body communication and a galvanic link for intra-body implant-to-implant connectivity. It is demonstrated that RF powering, using the proposed compact antennas, can provide an efficient and integrable system for powering up to an 8 cm depth inside body tissues. Furthermore, the same antennas are utilized for backscatter and galvanic communication.
A Novel Octal Annular Ring-Shaped Planar Monopole Antenna For WiFi And Unlicensed Ultra Wideband Frequency Range Applications
Authors: Jai Mangal
Date: 2023-11-15 00:00:00

Our paper presents the design of a unique annular ring-shaped planar monopole antenna with octal geometry intended for a broad spectrum of frequency applications. Utilizing FR4 epoxy for the substrate and copper material for the top patch, the antenna measures 39 mm $\times$ 30 mm $\times$ 1.6 mm. It exhibits resonance at 6.8 GHz, with a return loss of -49.01 dB. The antenna demonstrates a broad frequency range from 2.1 GHz - 13.1 GHz, resulting in an overall -10 dB bandwidth of 11 GHz. At the resonating frequency of 6.8 GHz, the antenna accomplished a total gain of 3.01 dBi and a peak gain of 5.87 dBi at 12.4 GHz. Additionally, it attains a high radiation efficiency of 95.26\%. The annular patch of the antenna helps distribute the current uniformly at the boundaries of the patch. This helps achieve better current distribution and wider -10 dB bandwidth. This antenna is a versatile replacement for multiple antennas catering to various frequency bands.
An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels
Authors: Meng-Qian Alexander Wu, Tzu-Hsien Sang, Luisa Schuhmacher, Ming-Jie Guo, Khodr Hammoud, Sofie Pollin
Date: 2023-11-15 00:00:00
Tasks: Deep Reinforcement Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems with concurrent Channel State Information (CSI) represented in the training data set. Consequently, solutions for RIS-assisted wireless communication systems under time-varying environments are relatively unexplored. However, communication problems should be considered with realistic assumptions; for instance, in scenarios where the channel is time-varying, the policy obtained by reinforcement learning should be applicable for situations where CSI is not well represented in the training data set. In this paper, we apply Meta-Reinforcement Learning (MRL) to the joint optimization problem of active beamforming at the Base Station (BS) and phase shift at the RIS, motivated by MRL's ability to extend the DRL concept of solving one Markov Decision Problem (MDP) to multiple MDPs. We provide simulation results to compare the average sum rate of the proposed approach with those of selected forerunners in the literature. Our approach improves the sum rate by more than 60% under time-varying CSI assumption while maintaining the advantages of typical DRL-based solutions. Our study's results emphasize the possibility of utilizing MRL-based designs in RIS-assisted wireless communication systems while considering realistic environment assumptions.
Wireless Communications in Cavity: A Reconfigurable Boundary Modulation based Approach
Authors: Xuehui Dong, Xiang Ren, Bokai Lai, Rujing Xiong, Tiebin Mi, Robert Caiming Qiu
Date: 2023-11-15 00:00:00
Tasks: Position

This paper explores the potential wireless communication applications of Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation environments. Unlike in free space, we utilize the sensitivity to boundaries of the enclosed electromagnetic (EM) field and the equivalent perturbation of RISs. For the first time, we introduce the framework of reconfigurable boundary modulation in the cavities . We have proposed a robust boundary modulation scheme that exploits the continuity of object motion and the mutation of the codebook switch, which achieves pulse position modulation (PPM) by RIS-generated equivalent pulses for wireless communication in cavities. This approach achieves around 2 Mbps bit rate in the prototype and demonstrates strong resistance to channel's frequency selectivity resulting in an extremely low bit error rate (BER).
Stochastic Geometry-based Trajectory Design for Multi-Purpose UAVs: Package and Data Delivery
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-11-15 00:00:00

With the advancements achieved in drones' flexibility, low cost, and high efficiency, they obtain huge application opportunities in various industries, such as aerial delivery and future communication networks. However, the increasing transportation needs and expansion of network capacity demands for UAVs will cause aerial traffic conflicts in the future. To address this issue, in this paper, we explore the idea of multi-purpose UAVs, which act as aerial wireless communication data relays and means of aerial transportation simultaneously to deliver data and packages at the same time. While UAVs deliver the packages from warehouses to residential areas, we design their trajectories which enable them to collect data from multiple Internet of Things (IoT) clusters and forward the collected data to terrestrial base stations (TBSs). To select the serving nearby IoT clusters, UAVs rank them based on their priorities and distances. From the perspectives of data and package delivery, respectively, we propose two algorithms that design the optimal UAVs trajectory to maximize the transmitted data or minimize the round trip time. Specifically, we use tools from stochastic geometry to model the locations of IoT clusters and TBSs. Given the nature of random locations, the proposed algorithm applies to general cases. Our numerical results show that multi-purpose UAVs are practical and have great potential to enhance the energy/time-efficiency of future networks.
K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control for Tractor-Trailer Trajectory Tracking with Unknown Parameters
Authors: Zehao Wang, Han Zhang, Jingchuan Wang
Date: 2023-11-15 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.
System-Wide Emergency Policy for Transitioning from Main to Secondary Fuel
Authors: Laurent Pagnier, Criston Hyett, Robert Ferrando, Igal Goldshtein, Jean Alisse, Lilah Saban, Michael Chertkov
Date: 2023-11-15 00:00:00

Faced with the complexities of managing natural gas-dependent power system amid the surge of renewable integration and load unpredictability, this study explores strategies for navigating emergency transitions to costlier secondary fuels. Our aim is to develop decision-support tools for operators during such exigencies. We approach the problem through a Markov Decision Process (MDP) framework, accounting for multiple uncertainties. These include the potential for dual-fuel generator failures and operator response during high-pressure situations. Additionally, we consider the finite reserves of primary fuel, governed by gas-flow partial differential equations (PDEs) and constrained by nodal pressure. Other factors include the variability in power forecasts due to renewable generation and the economic impact of compulsory load shedding. For tractability, we address the MDP in a simplified context, replacing it by Markov Processes evaluated against a selection of policies and scenarios for comparison. Our study considers two models for the natural gas system: an over-simplified model tracking linepack and a more nuanced model that accounts for gas flow network heterogeneity. The efficacy of our methods is demonstrated using a realistic model replicating Israel's power-gas infrastructure.
Modelling of the Electric Vehicle Charging Infrastructure as Cyber Physical Power Systems: A Review on Components, Standards, Vulnerabilities and Attacks
Authors: Sagar Babu Mitikiri, K. Victor Sam Moses Babu, Divyanshi Dwivedi, Vedantham Lakshmi Srinivas, Pratyush Chakraborty, Pradeep Kumar Yemula, Mayukha Pal
Date: 2023-11-15 00:00:00

The increasing number of electric vehicles (EVs) has led to the growing need to establish EV charging infrastructures (EVCIs) with fast charging capabilities to reduce congestion at the EV charging stations (EVCS) and also provide alternative solutions for EV owners without residential charging facilities. The EV charging stations are broadly classified based on i) where the charging equipment is located - on-board and off-board charging stations, and ii) the type of current and power levels - AC and DC charging stations. The DC charging stations are further classified into fast and extreme fast charging stations. This article focuses mainly on several components that model the EVCI as a cyberphysical system (CPS).
A Hybrid Approach using ARIMA, Kalman Filter and LSTM for Accurate Wind Speed Forecasting
Authors: Manas Ranjan Mohapatra, Rahul Radhakrishnan, Raj Mani Shukla
Date: 2023-11-14 00:00:00

Present energy demand and modernization are leading to greater fossil fuel consumption, which has increased environmental pollution and led to climate change. Hence to decrease dependency on conventional energy sources, renewable energy sources are considered. Wind energy is a long-term renewable energy resource but its intermittent nature makes it difficult in harnessing it. Since wind speed prediction is vital there are different methodologies for wind speed estimation available in the literature. In this work, a new hybrid model is proposed by combining auto-regressive integrated moving average (ARIMA), Kalman filter and long short-term memory (LSTM) for estimating wind speed which works more accurately than the existing methods proposed in the literature. From simulations, it is observed that the proposed method works with better accuracy when compared to the existing methods.
Density Steering of Gaussian Mixture Models for Discrete-Time Linear Systems
Authors: Isin M. Balci, Efstathios Bakolas
Date: 2023-11-14 00:00:00

In this paper, we study the finite-horizon optimal density steering problem for discrete-time stochastic linear dynamical systems. Specifically, we focus on steering probability densities represented as Gaussian mixture models which are known to give good approximations for general smooth probability density functions. We then revisit the covariance steering problem for Gaussian distributions and derive its optimal control policy. Subsequently, we propose a randomized policy to enhance the numerical tractability of the problem and demonstrate that under this policy the state distribution remains a Gaussian mixture. By leveraging these results, we reduce the Gaussian mixture steering problem to a linear program. We also discuss the problem of steering general distributions using Gaussian mixture approximations. Finally, we present results of non-trivial numerical experiments and demonstrate that our approach can be applied to general distribution steering problems.
A Robust, Efficient Predictive Safety Filter
Authors: Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar
Date: 2023-11-14 00:00:00
Tasks: Novel Concepts

In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation. The proposed safety filter extends upon existing work to reject disturbances for discrete-time, time-varying nonlinear systems with time-varying constraints. The safety filter is based on novel concepts of robust, discrete-time barrier functions and can be used to filter any control law. Here, we use the safety filter in conjunction with Differentiable Predictive Control (DPC) as a promising offline learning-based policy optimization method. The approach is demonstrated on a two-tank system, building, and single-integrator example.
State Estimation of Continuous-time Dynamical Systems with Uncertain Inputs with Bounded Variation: Entropy, Bit Rates, and Relation with Switched Systems
Authors: Hussein Sibai, Sayan Mitra
Date: 2020-11-20 00:00:00
Tasks: State Estimation

We extend the notion of estimation entropy of autonomous dynamical systems proposed by Liberzon and Mitra [1] to nonlinear dynamical systems with uncertain inputs with bounded variation. We call this new notion the {$\epsilon$}-estimation entropy of the system and show that it lower bounds the bit rate needed for state estimation. {$\epsilon$}-estimation entropy represents the exponential rate of the increase of the minimal number of functions that are adequate for {$\epsilon$}- approximating any trajectory of the system. We show that alternative entropy definitions using spanning or separating trajectories bound ours from both sides. On the other hand, we show that other commonly used definitions of entropy, for example the ones in [1], diverge to infinity. Thus, they are potentially not suitable for systems with uncertain inputs. We derive an upper bound on {$\epsilon$}-estimation entropy and estimation bit rates, and evaluate it for two examples. We present a state estimation algorithm that constructs a function that approximates a given trajectory up to an {$\epsilon$} error, given time-sampled and quantized measurements of state and input. We investigate the relation between {$\epsilon$}-estimation entropy and a previous notion for switched nonlinear systems and derive a new upper bound for the latter, showing the generality of our results on systems with uncertain inputs.
Rate-induced tipping in complex high-dimensional ecological networks
Authors: Shirin Panahi, Younghae Do, Alan Hastings, Ying-Cheng Lai
Date: 2023-11-15 00:00:00

In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we discover a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.
Theory of Infectious Diseases with Testing and Testing-less Covid-19 Endemic
Authors: Bo Deng, Chayu Yang
Date: 2023-11-15 00:00:00

What is the long term dynamics of the Covid-19 pandemic? How will it end? Here we constructed an infectious disease model with testing and analyzed the existence and stability of its endemic states. For a large parameter set, including those relevant to the SARS-CoV-2 virus, we demonstrated the existence of one endemic equilibrium without testing and one endemic equilibrium with testing and proved their local and global stabilities for some cases. Our results suggest that the pandemic is to end with a testing-less endemic state through a novel and surprising mechanism called stochastic trapping.
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Authors: Yixuan Sun, Elizabeth Cucuzzella, Steven Brus, Sri Hari Krishna Narayanan, Balu Nadiga, Luke Van Roekel, Jan Hückelheim, Sandeep Madireddy
Date: 2023-11-10 00:00:00
Tasks: Sensitivity

Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.