pwc-archive/papers-with-abstracts huggingface dataset:
Leader-Follower Density Control of Spatial Dynamics in Large-Scale Multi-Agent Systems
Authors: Gian Carlo Maffettone, Alain Boldini, Maurizio Porfiri, Mario di Bernardo
Date: 2024-06-03 00:00:00

We address the problem of controlling the density of a large ensemble of follower agents by acting on a group of leader agents that interact with them. Using coupled partial integro-differential equations to describe leader and follower density dynamics, we establish feasibility conditions and develop two control architectures ensuring global stability. The first employs feed-forward control on the followers' and a feedback on the leaders' density. The second implements a dual feedback loop through a reference-governor that adapts the leaders' density based on both populations' measurements. Our methods, initially developed in a one-dimensional setting, are extended to multi-dimensional cases, and validated through numerical simulations for representative control applications, both for groups of infinite and finite size.
Wasserstein Distributionally Robust Control and State Estimation for Partially Observable Linear Systems
Authors: MinHyuk Jang, Astghik Hakobyan, Insoon Yang
Date: 2024-06-03 00:00:00
Tasks: Computational Efficiency, State Estimation

This paper presents a novel Wasserstein distributionally robust control and state estimation algorithm for partially observable linear stochastic systems, where the probability distributions of disturbances and measurement noises are unknown. Our method consists of the control and state estimation phases to handle distributional ambiguities of system disturbances and measurement noises, respectively. Leveraging tools from modern distributionally robust optimization, we consider an approximation of the control problem with an arbitrary nominal distribution and derive its closed-form optimal solution. We show that the separation principle holds, thereby allowing the state estimator to be designed separately. A novel distributionally robust Kalman filter is then proposed as an optimal solution to the state estimation problem with Gaussian nominal distributions. Our key contribution is the combination of distributionally robust control and state estimation into a unified algorithm. This is achieved by formulating a tractable semidefinite programming problem that iteratively determines the worst-case covariance matrices of all uncertainties, leading to a scalable and efficient algorithm. Our method is also shown to enjoy a guaranteed cost property as well as a probabilistic out-of-sample performance guarantee. The results of our numerical experiments demonstrate the performance and computational efficiency of the proposed method.
High-performance magnetostatic wave resonators through deep anisotropic etching of GGG substrates
Authors: Sudhanshu Tiwari, Anuj Ashok, Connor Devitt, Sunil A. Bhave, Renyuan Wang
Date: 2024-06-03 00:00:00

Microscale resonators are fundamental and necessary building blocks for modern radio communication filters for mobile devices. The resonator's Q factor ($Q$) determines the insertion loss while coupling ($K_t^2$) governs the fractional bandwidth. The product $k_t^2 \times Q$ is widely recognized as the definitive figure of merit for microresonators. Magnetostatic wave resonators based on Yttrium Iron Garnet (YIG) are a promising technology platform for future communication filters. They have shown considerably better performance in terms of $Q$ when compared to the commercially successful acoustic resonators in the $>$7 GHz range. However, the coupling coefficients of these resonators have been limited to $<$3 %, primarily due to the restricted design space imposed by microfabrication challenges related to the patterning of gadolinium gallium garnet (GGG), the substrate material used for growing single crystal YIG. This paper reports novel resonator designs enabled by breakthrough bulk micromachining technology for anisotropic etching of GGG, leading to coupling >8 % in the 6-20 GHz frequency range. We use the same technology platform to show resonant enhancement of effective coupling, reaching up to 23 \% at 10.5 GHz. The frequency of resonant coupling can be tuned by design during the fabrication process. The resonant coupling results in an unprecedented $k_t^2 \times Q$ figure of merit of 191 at 10.5 GHz and 222 at 14.7 GHz. The technology platform presented in this paper supports both tunable filter architecture and switched filter banks that are currently being used in consumer mobile devices.
An Origami-Inspired Endoscopic Capsule with Tactile Perception for Early Tissue Anomaly Detection
Authors: Yukun Ge, Rui Zong, Xiaoshuai Zhang, Thrishantha Nanayakkara
Date: 2024-06-03 00:00:00
Tasks: Anomaly Detection, Position

Video Capsule Endoscopy (VCE) is currently one of the most effective methods for detecting intestinal diseases. However, it is challenging to detect early-stage small nodules with this method because they lack obvious color or shape features. In this letter, we present a new origami capsule endoscope to detect early small intestinal nodules using tactile sensing. Four soft tactile sensors made out of piezoresistive material feed four channels of phase-shifted data that are processed using a particle filter. The particle filter uses an importance assignment template designed using experimental data from five known sizes of modules. Moreover, the proposed capsule can use shape changes to passively move forward or backward under peristalsis, enabling it to reach any position in the intestine for detection. Experimental results show that the proposed capsule can detect nodules of more than 3mm diameter with 100% accuracy.
Innovative Gain Reconfiguration for Active Fault-Tolerant Flight Control: Balance of Stability and Agility
Authors: Ege C. Altunkaya, Akin Catak, Emre Koyuncu, Ibrahim Ozkol
Date: 2024-06-03 00:00:00

In this study, a distinct reconfigurable fault-tolerant flight control strategy is addressed for mitigating one of the persistent safety-critical issue, i.e. loss of control triggered by actuator faults. The attainable acceleration set notion is taken a step further towards incremental attainable acceleration set through a slight modification that enables instantaneous controllability checks. The inner-loop gains are updated in case of a fault using incremental attainable acceleration set and a tuning function, which is in charge as a compensator of agility and robustness. Additionally, the outer-loop gains are also such reconfigured that holding the bandwidth ratio of the successive loops at a prudent level to ensure the closed-loop stability; for this reason, an analytical outer-loop gain update law is derived based on the inner-loop gains and bandwidth, actuator and command filter time constants. Subsequently, the proposed architecture is assessed under a severe fault scenario with a demanding maneuver mission. Noticeably, the proposed method fulfills the expectations of stability and agility sufficiently, and surpasses the fixed-gain approach.
Loss of Control Prevention of an Agile Aircraft: Dynamic Command Saturation Approach
Authors: Ege Ç. Altunkaya, Akın Çatak, Emre Koyuncu, İbrahim Özkol
Date: 2024-06-03 00:00:00

The prevention of the loss of control in agile aircraft during the extreme maneuvers is of concern due to the nonlinear aerodynamics and flight dynamics nature of the aircraft in this study. Within this context, the primary objective is to present an architectural framework and elucidate the methodology for its determination. This architecture enables agile maneuvering aircraft to execute more extreme maneuvers while avoiding departure from stable flight, surpassing maneuverability capabilities of conventional state limiters. Hence, the notion of an incremental attainable moment set is introduced for an instantaneous controllability investigation using demanded control moment coefficients derived in the high-level controller, which is the incremental nonlinear dynamic inversion. In the event of detecting a violation of controllability boundaries, Lyapunov-based dynamic command saturation is employed to limit pilot commands, preventing the aircraft from initiating departure from stable flight. As a result, abrupt and excessive pilot inputs are dynamically softened in-flight, and presumable departure tendencies are mitigated. Consequently, the superiority of the proposed method over conventional state limiters is proven through the flight simulations of agile and abrupt maneuvers, as well as Monte Carlo simulations that demonstrate the expansion of stable maneuverable volumes up to 55%.
Unified Control of Voltage, Frequency and Angle in Electrical Power Systems: A Passivity and Negative-Imaginary based Approach
Authors: Yijun Chen, Kanghong Shi, Ian R. Petersen, Elizabeth L. Ratnam
Date: 2024-06-03 00:00:00

This paper proposes a unified methodology for voltage regulation, frequency synchronization, and rotor angle control in power transmission systems considering a one-axis generator model with time-varying voltages. First, we formulate an output consensus problem with a passivity and negative-imaginary (NI) based control framework. We establish output consensus results for both networked passive systems and networked NI systems. Next, we apply the output consensus problem by controlling large-scale batteries co-located with synchronous generators -- using real-time voltage phasor measurements. By controlling the battery storage systems so as to dispatch real and reactive power, we enable simultaneous control of voltage, frequency, and power angle differences across a transmission network. Validation through numerical simulations on a four-area transmission network confirms the robustness of our unified control framework.
On the Stability of Networked Nonlinear Negative Imaginary Systems with Applications to Electrical Power Systems
Authors: Yijun Chen, Kanghong Shi, Ian R. Petersen, Elizabeth L. Ratnam
Date: 2024-06-03 00:00:00

In the transition to achieving net zero emissions, it has been suggested that a substantial expansion of electric power grids will be necessary to support emerging renewable energy zones. In this paper, we propose employing battery-based feedback control and nonlinear negative imaginary (NI) systems theory to reduce the need for such expansion. By formulating a novel Lur\'e-Postnikov-like Lyapunov function, stability results are presented for the feedback interconnection of two single nonlinear NI systems, while output feedback consensus results are established for the feedback interconnection of two networked nonlinear NI systems based on a network topology. This theoretical framework underpins our design of battery-based control in power transmission systems. We demonstrate that the power grid can be gradually transitioned into the proposed NI systems, one transmission line at a time.
Programmable Multi-input Buck-Boost Converter for Photovoltaics Arrays
Authors: Zhongting Tang, Yi Zhang, Pooya Davari
Date: 2024-06-03 00:00:00

This paper proposes a programmable multi-input buck-boost structure method, which can enhance the operation tolerance for the PV array under extremely harsh climatic conditions. The proposed structure based on a traditional two switches buck-boost converter can connect PV panels in parallel and cascade flexibly, and also enable the individual operation of each PV panel. The active switches can be programmed to change the connection structures as well as achieve the maximum power point track of PV panels simultaneously. The paper presents the programming method for an exemplified scalable structure converter for two PV panels. The simulation has been established in MATLAB/Simulink to validate the performance of the proposed converter in terms of multiplexing function, wide operating range of PV panels, and low switching stress.
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective
Authors: Sindre Benjamin Remman, Bjørn Andreas Kristiansen, Anastasios M. Lekkas
Date: 2024-06-03 00:00:00
Tasks: Deep Reinforcement Learning, Dimensionality Reduction, reinforcement-learning, Reinforcement Learning

In this work, we use optimal control to change the behavior of a deep reinforcement learning policy by optimizing directly in the policy's latent space. We hypothesize that distinct behavioral patterns, termed behavioral modes, can be identified within certain regions of a deep reinforcement learning policy's latent space, meaning that specific actions or strategies are preferred within these regions. We identify these behavioral modes using latent space dimension-reduction with \ac*{pacmap}. Using the actions generated by the optimal control procedure, we move the system from one behavioral mode to another. We subsequently utilize these actions as a filter for interpreting the neural network policy. The results show that this approach can impose desired behavioral modes in the policy, demonstrated by showing how a failed episode can be made successful and vice versa using the lunar lander reinforcement learning environment.
Cascade Network Stability of Synchronized Traffic Load Balancing with Heterogeneous Energy Efficiency Policies
Authors: Mengbang Zou, Weisi Guo
Date: 2024-06-03 00:00:00

Cascade stability of load balancing is critical for ensuring high efficiency service delivery and preventing undesirable handovers. In energy efficient networks that employ diverse sleep mode operations, handing over traffic to neighbouring cells' expanded coverage must be done with minimal side effects. Current research is largely concerned with designing distributed and centralized efficient load balancing policies that are locally stable. There is a major research gap in identifying large-scale cascade stability for networks with heterogeneous load balancing policies arising from diverse plug-and-play sleep mode policies in ORAN, which will cause heterogeneity in the network stability behaviour. Here, we investigate whether cells arbitrarily connected for load balancing and having an arbitrary number undergoing sleep mode can: (i) synchronize to a desirable load-balancing state, and (ii) maintain stability. For the first time, we establish the criterion for stability and prove its validity for any general load dynamics and random network topology. Whilst its general form allows all load balancing and sleep mode dynamics to be incorporated, we propose an ORAN architecture where the network service management and orchestration (SMO) must monitor new load balancing policies to ensure overall network cascade stability.
Safety-Critical Control of Euler-Lagrange Systems Subject to Multiple Obstacles and Velocity Constraints
Authors: Zhi Liu, Si Wu, Tengfei Liu, Zhong-Ping Jiang
Date: 2024-06-03 00:00:00

This paper studies the safety-critical control problem for Euler-Lagrange (EL) systems subject to multiple ball obstacles and velocity constraints in accordance with affordable velocity ranges. A key strategy is to exploit the underlying inner-outer-loop structure for the design of a new cascade controller for the class of EL systems. In particular, the outer-loop controller is developed based on quadratic programming (QP) to avoid ball obstacles and generate velocity reference signals fulfilling the velocity limitation. Taking full advantage of the conservation-of-energy property, a nonlinear velocity-tracking controller is designed to form the inner loop. One major difficulty is caused by the possible non-Lipschitz continuity of the standard QP algorithm when there are multiple constraints. To solve this problem, we propose a refined QP algorithm with the feasible set reshaped by an appropriately chosen positive basis such that the feasibility is retained while the resulting outer-loop controller is locally Lipschitz. It is proved that the constraint-satisfaction problem is solvable as long as the ball obstacles satisfy a mild distance condition. The proposed design is validated by numerical simulation and an experiment based on a $2$-link planar manipulator.
Constructive Safety Control
Authors: Si Wu, Tengfei Liu, Zhong-Ping Jiang
Date: 2024-06-03 00:00:00

This paper proposes a constructive approach to safety control of nonlinear cascade systems subject to multiple state constraints. New design ingredients include a unified characterization of safety and stability for systematic designs of safety controllers, and a novel technique of reshaping the feasible sets of quadratically constrained quadratic programming induced from safety control. The proposed method guarantees Lipschitz continuity of virtual control laws, enabling a stepwise constructive design. A refined nonlinear small-gain synthesis is employed to address the nonlinear uncertain interconnections between the resulting subsystems corresponding to different virtual control laws, and to guarantee the achievement of the safety control objective. When the safety constraints are removed, the proposed approach coincides with the standard constructive nonlinear control. The proposed safety-control algorithm is experimentally validated in a testbed involving a vertical takeoff and landing (VTOL) vehicle taking off in narrow spaces.
Sensing, Communication, and Control Co-design for Energy Efficient Satellite-UAV Networks
Authors: Tianhao. Liang, Huahao. Ding, Yuqi. Ping, Bin. Cao, Tingting. Zhang, Qinyu. Zhang
Date: 2024-06-03 00:00:00

Traditional terrestrial communication infrastructures often fail to collect the timely information from Internet of Thing (IoT) devices in remote areas. To address this challenge, we investigate a Satellite-unmanned aerial vehicles (UAV) integrated Non-terrestrial network (NTN), where the UAV is controlled by remote control center via UAV-to-Satellite connections. To maximize the energy efficiency (EE) of the UAV, we optimize the UAV trajectory, power allocation, and state sensing strategies, while guaranteing the control stability and communication reliability. This challenging problem is addressed using an efficient algorithm, incorporating a Deep Q-Network (DQN)-based trajectory determination, a closed form of power allocation, and one-dimensional searching for sensing. Numerical simulations are conducted to validate the effectiveness of our approach. The results showcase the data size of collection has a greater impact than transmission power, and reveal the relationship among sensing interval, communication maximum power and control performance. This study provides promising solutions and valuable insights for efficient data collection in remote IoT.
Constructing Dynamic Feedback Linearizable Discretizations
Authors: Ashutosh Jindal, Florentina Nicolau, David Martin Diego, Ravi Banavar
Date: 2024-06-03 00:00:00

Dynamic feedback linearization-based methods allow us to design control algorithms for a fairly large class of nonlinear systems in continuous time. However, this feature does not extend to their sampled counterparts, i.e., for a given dynamically feedback linearizable continuous time system, its numerical discretization may fail to be so. In this article, we present a way to construct discretization schemes (accurate up to first order) that result in schemes that are feedback linearizable. This result is an extension of our previous work, where we had considered only static feedback linearizable systems. The result presented here applies to a fairly general class of nonlinear systems, in particular, our analysis applies to both endogenous and exogenous types of feedback. While the results in this article are presented on a control affine form of nonlinear systems, they can be readily modified to general nonlinear systems.
Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach
Authors: Borui Zhang, Chaojie Li, Guo Chen, ZhaoYang Dong
Date: 2024-06-03 00:00:00
Tasks: AI Agent, Deep Reinforcement Learning, Hallucination, Language Modeling, Language Modelling, Large Language Model

To incentivize flexible resources such as Battery Energy Storage Systems (BESSs) to offer Frequency Control Ancillary Services (FCAS), Australia's National Electricity Market (NEM) has implemented changes in recent years towards shorter-term bidding rules and faster service requirements. However, firstly, existing bidding optimization methods often overlook or oversimplify the key aspects of FCAS market procedures, resulting in an inaccurate depiction of the market bidding process. Thus, the BESS bidding problem is modeled based on the actual bidding records and the latest market specifications and then formulated as a deep reinforcement learning (DRL) problem. Secondly, the erratic decisions of the DRL agent caused by imperfectly predicted market information increases the risk of profit loss. Hence, a Conditional Value at Risk (CVaR)-based DRL algorithm is developed to enhance the risk resilience of bidding strategies. Thirdly, well-trained DRL models still face performance decline in uncommon scenarios during online operations. Therefore, a Large Language Models (LLMs)-assisted artificial intelligence (AI)-agent interactive decision-making framework is proposed to improve the strategy timeliness, reliability and interpretability in uncertain new scenarios, where conditional hybrid decision and self-reflection mechanisms are designed to address LLMs' hallucination challenge. The experiment results demonstrate that our proposed framework has higher bidding profitability compared to the baseline methods by effectively mitigating the profit loss caused by various uncertainties.
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis
Authors: Yi Hu, Hyeonjin Kim, Kai Ye, Ning Lu
Date: 2024-06-02 00:00:00
Tasks: Few-Shot Learning, Prompt Engineering

This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, demonstrated through the restoration of missing data in power system load profiles. A two-stage fine-tuning strategy is proposed to adapt a pre-trained LLMs, i.e., GPT-3.5, for missing data restoration tasks. Through empirical evaluation, we demonstrate the effectiveness of the fine-tuned model in accurately restoring missing data, achieving comparable performance to state-of-the-art specifically designed models such as BERT-PIN. Key findings include the importance of prompt engineering and the optimal utilization of fine-tuning samples, highlighting the efficiency of few-shot learning in transferring knowledge from general user cases to specific target users. Furthermore, the proposed approach demonstrates notable cost-effectiveness and time efficiency compared to training models from scratch, making it a practical solution for scenarios with limited data availability and computing resources. This research has significant potential for application to other power system load profile analysis tasks. Consequently, it advances the use of LLMs in power system analytics, offering promising implications for enhancing the resilience and efficiency of power distribution systems.
Matrix-Valued Measures and Wishart Statistics for Target Tracking Applications
Authors: Robin Forsling, Simon J. Julier, Gustaf Hendeby
Date: 2024-06-02 00:00:00

Ensuring sufficiently accurate models is crucial in target tracking systems. If the assumed models deviate too much from the truth, the tracking performance might be severely degraded. While the models are usually defined using multivariate conditions, the measures used to validate them are most often scalar-valued. In this paper, we propose matrix-valued measures for both offline and online assessment of target tracking systems. Recent results from Wishart statistics, and approximations thereof, are adapted and it is shown how these can be incorporated to infer statistical properties for the eigenvalues of the proposed measures. In addition, we relate these results to the statistics of the baseline measures. Finally, the applicability of the proposed measures are demonstrated using two important problems in target tracking: (i) distributed track fusion design; and (ii) filter model mismatch detection.