pwc-archive/papers-with-abstracts huggingface dataset:
Loop Shaping of Hybrid Motion Control with Contact Transition
Authors: Michael Ruderman
Date: 2024-11-29 00:00:00

A standard motion control with feedback of the output displacement cannot handle unforeseen contact with environment without penetrating into the soft, i.e. viscoelastic, materials or even damaging the fragile materials. Robotics and mechatronics with tactile and haptic capabilities, and in particular medical robotics for example, place special demands on the advanced motion control systems that should enable the safe and harmless contact transitions. This paper shows how the basic principles of loop shaping can be easily used to handle sufficiently stiff motion control in such a way that it is extended by sensor-free dynamic reconfiguration upon contact with the environment. A thereupon based hybrid control scheme is proposed. A remarkable feature of the developed approach is that no measurement of the contact force is required and the input signal and the measured output displacement are the only quantities used for design and operation. Experiments on 1-DOF actuator are shown, where the moving tool comes into contact with grapes that are soft and simultaneously penetrable.
Voice Communication Analysis in Esports
Authors: Aymeric Vinot, Nicolas Perez
Date: 2024-11-21 00:00:00

In most team-based esports, voice communications are prominent in the team efficiency and synergy. In fact it has been observed that not only the skill aspect of the team but also the team effective voice communication comes into play when trying to have good performance in official matches. With the recent emergence of LLM (Large Language Models) tools regarding NLP (Natural Language Processing) (Vaswani et. al.), we decided to try applying them in order to have a better understanding on how to improve the effectiveness of the voice communications. In this paper the study has been made through the prism of League of Legends esport. However the main concepts and ideas can be easily applicable in any other team related esports.
Towards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and its Applications
Authors: Nirmal Joshua Kapu, Raghav Karan
Date: 2024-11-20 00:00:00
Tasks: Emotion Recognition, Speaker Identification, Speech Enhancement, speech-recognition, Speech Recognition

This article surveys convolution-based models including convolutional neural networks (CNNs), Conformers, ResNets, and CRNNs-as speech signal processing models and provide their statistical backgrounds and speech recognition, speaker identification, emotion recognition, and speech enhancement applications. Through comparative training cost assessment, model size, accuracy and speed assessment, we compare the strengths and weaknesses of each model, identify potential errors and propose avenues for further research, emphasizing the central role it plays in advancing applications of speech technologies.
Secure Filtering against Spatio-Temporal False Data Attacks under Asynchronous Sampling
Authors: Zishuo Li, Anh Tung Nguyen, André M. H. Teixeira, Yilin Mo, Karl H. Johansson
Date: 2024-11-29 00:00:00
Tasks: State Estimation

This paper addresses the secure state estimation problem for continuous linear time-invariant systems with non-periodic and asynchronous sampled measurements, where the sensors need to transmit not only measurements but also sampling time-stamps to the fusion center. This measurement and communication setup is well-suited for operating large-scale control systems and, at the same time, introduces new vulnerabilities that can be exploited by adversaries through (i) manipulation of measurements, (ii) manipulation of time-stamps, (iii) elimination of measurements, (iv) generation of completely new false measurements, or a combination of these attacks. To mitigate these attacks, we propose a decentralized estimation algorithm in which each sensor maintains its local state estimate asynchronously based on its measurements. The local states are synchronized through time prediction and fused after time-stamp alignment. In the absence of attacks, state estimates are proven to recover the optimal Kalman estimates by solving a weighted least square problem. In the presence of attacks, solving this weighted least square problem with the aid of $\ell_1$ regularization provides secure state estimates with uniformly bounded error under an observability redundancy assumption. The effectiveness of the proposed algorithm is demonstrated using a benchmark example of the IEEE 14-bus system.
Lyapunov based dynamic controller designs for reach-and-avoid problems
Authors: Lukas Lanza, Philipp Braun
Date: 2024-11-29 00:00:00

Safe obstacle avoidance and target set stabilization for nonlinear systems using reactive feedback control is under consideration. Based only on local information and by considering virtual dynamics, a safe path is generated online. The control law for the virtual dynamics is combined with a feedback controller for the dynamics of interest, where Lyapunov arguments and forward invariance are used to ensure that the state of the system remains in a vicinity of the path. To allow for discrete decisions in the avoidance controller design, the closed-loop dynamics are formulated using the hybrid systems framework. The results are illustrated by a numerical example for unicycle dynamics.
Fixed-Relative-Switched Threshold Strategies for Consensus Tracking Control of Nonlinear Multiagent Systems
Authors: ZiMing Wang, Yun Gao, Apostolos I. Rikos, Ning Pang, Yiding Ji
Date: 2024-11-29 00:00:00

This paper investigates event-triggered consensus tracking in nonlinear semi-strict-feedback multi-agent systems involving one leader and multiple followers. We first employ radial basis function neural networks and backstepping techniques to approximate the unknown nonlinear dynamics, facilitating the design of dual observers to measure the unknown states and disturbances. Then three adaptive event-triggered control schemes are proposed: fixed-threshold, relative-threshold, and switched-threshold configurations, each featuring specialized controller architectures and triggering mechanisms. Through Lyapunov stability analysis, we establish that the follower agents can asymptotically track the reference trajectory of the leader, meanwhile all error signals remain uniform bounded. Our proposed control strategies effectively prevent Zeno behaviors through stringent exclusion criteria. Finally, an illustrative example is presented, demonstrating the competitive performance of our control framework in achieving consensus tracking and optimizing triggering efficiency.
Mixed-Integer Linear Programming Model for Collision Avoidance Planning in Commercial Aircraft Formations
Authors: Songqiying Yang, Ania Adil, Eric Feron
Date: 2024-11-29 00:00:00
Tasks: Collision Avoidance

With advancements in technology, commercial aircraft formation flying is becoming increasingly feasible as an efficient and environmentally friendly flight method. However, gaps remain in practical implementation, particularly in collision avoidance for aircraft formations. Existing avoidance algorithms mainly focus on single aircraft or UAV swarms, lacking comprehensive studies on the complex interactions within commercial aircraft formations. To address this, this paper proposes an optimization model designed to generate safe and effective collision avoidance solutions for commercial aircraft formations. This model demonstrates avoidance paths for formations facing intruders and offers insights for developing formation flight strategies. This study explores response strategies for commercial aircraft formations encountering intruders, considering the difficulty of pilot maneuvers. The findings provide theoretical support for the practical implementation of commercial formation flying and may advance the adoption of this technology.
Prying Pedestrian Surveillance-Evasion: Minumum-Time Evasion from an Agile Pursuer
Authors: Philipp Braun, Timothy L. Molloy, Iman Shames
Date: 2024-11-28 00:00:00

A new surveillance-evasion differential game is posed and solved in which an agile pursuer (the prying pedestrian) seeks to remain within a given surveillance range of a less agile evader that aims to escape. In contrast to previous surveillance-evasion games, the pursuer is agile in the sense of being able to instantaneously change the direction of its velocity vector, whilst the evader is constrained to have a finite maximum turn rate. Both the game of kind concerned with conditions under which the evader can escape, and the game of degree concerned with the evader seeking to minimize the escape time whilst the pursuer seeks to maximize it, are considered. The game-of-degree solution is surprisingly complex compared to solutions to analogous pursuit-evasion games with an agile pursuer since it exhibits dependence on the ratio of the pursuer's speed to the evader's speed. It is, however, surprisingly simple compared to solutions to classic surveillance-evasion games with a turn-limited pursuer.
Fast Switching in Mixed-Integer Model Predictive Control
Authors: Artemi Makarow, Christian Kirches
Date: 2024-11-28 00:00:00
Tasks: Model Predictive Control

We derive stability results for finite control set and mixed-integer model predictive control with a downstream oversampling phase. The presentation rests upon the inherent robustness of model predictive control with stabilizing terminal conditions and techniques for solving mixed-integer optimal control problems by continuous optimization. Partial outer convexification and binary relaxation transform mixed-integer problems into common optimal control problems. We deduce nominal asymptotic stability for the resulting relaxed system formulation and implement sum-up rounding to restore efficiently integer feasibility on an oversampling time grid. If fast control switching is technically possible and inexpensive, we can approximate the relaxed system behavior in the state space arbitrarily close. We integrate input perturbed model predictive control with practical asymptotic stability. Numerical experiments illustrate practical relevance of fast control switching.
Economic Dispatch and Power Flow Analysis for Microgrids
Authors: Saskia A. Putri, Xiaoyu Ge, Javad Khazaei
Date: 2024-11-28 00:00:00

This study investigates the economic dispatch and optimal power flow (OPF) for microgrids, focusing on two configurations: a single-bus islanded microgrid and a three-bus grid-tied microgrid. The methodologies integrate renewable energy sources (solar PV and wind turbines), battery energy storage systems (BESS), and conventional generators (CHP, diesel, and natural gas), which are connected to the grid to ensure cost-efficient and reliable operation. The economic dispatch analysis evaluates the allocation of generation resources over daily and weekly horizons, highlighting the extensive utilization of renewable energy and the strategic use of BESS to balance system dynamics. The OPF analysis examines the distribution of active and reactive power across buses while ensuring voltage stability and compliance with operational constraints. Results show that the microgrid consistently satisfies load demand with minimal reliance on costly external grid power. Renewable energy sources are maximized for cost reduction, while BESS is employed strategically to address renewable intermittency. For the grid-tied microgrid, optimal power dispatch prioritizes cheaper sources, with Bus 1 contributing the largest share due to its favorable cost profile. Voltage variations remain within acceptable boundaries but indicate potential stability challenges under dynamic load changes, suggesting the need for secondary voltage control. These findings demonstrate the effectiveness of the proposed methodologies in achieving sustainable, cost-effective, and stable microgrid operations.
L4acados: Learning-based models for acados, applied to Gaussian process-based predictive control
Authors: Amon Lahr, Joshua Näf, Kim P. Wabersich, Jonathan Frey, Pascal Siehl, Andrea Carron, Moritz Diehl, Melanie N. Zeilinger
Date: 2024-11-28 00:00:00
Tasks: Computational Efficiency, Gaussian Processes, Model Predictive Control

Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, enabling state-of-the-art implementations of learning-based models for MPC is complicated by the challenge of interfacing machine learning frameworks with real-time optimal control software. This work aims at filling this gap by incorporating external sensitivities in sequential quadratic programming solvers for nonlinear optimal control. To this end, we provide L4acados, a general framework for incorporating Python-based residual models in the real-time optimal control software acados. By computing external sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting parallelization of sensitivity computations when preparing the quadratic subproblems. We demonstrate significant speed-ups and superior scaling properties of L4acados compared to available software using a neural-network-based control example. Last, we provide an efficient and modular real-time implementation of Gaussian process-based MPC using L4acados, which is applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver.
Calculation of time-optimal motion primitives for systems exhibiting oscillatory internal dynamics
Authors: Thomas Auer, Frank Woittennek
Date: 2024-11-28 00:00:00
Tasks: Motion Planning, Trajectory Planning, valid

An algorithm for planning near time-optimal trajectories for systems with an oscillatory internal dynamics has been developed in previous work. It is based on assembling a complete trajectory from motion primitives called jerk segments, which are the time-optimal solution to an optimization problem. To achieve the shortest overall transition time, it is advantageous to recompute these segments for different acceleration levels within the motion planning procedure. This publication presents a numerical calculation method enabling fast and reliable calculation. This is achieved by explicitly evaluating the optimality conditions that arise for the problem, and further by reducing the evaluation of these conditions to a line-search problem on a bounded interval. This reduction guarantees, that a valid solution if found after a fixed number of computational steps, making the calculation time constant and predictable. Furthermore, the algorithm does not rely on optimisation algorithms, which allowed its implementation on a laboratory system for measurements with the purpose of validating the approach.
On trajectory design from motion primitives for near time-optimal transitions for systems with oscillating internal dynamics
Authors: Thomas Auer, Frank Woittennek
Date: 2024-11-28 00:00:00
Tasks: Motion Planning

An efficient approach to compute near time-optimal trajectories for linear kinematic systems with oscillatory internal dynamics is presented. Thereby, kinematic constraints with respect to velocity, acceleration and jerk are taken into account. The trajectories are composed of several motion primitives, the most crucial of which is termed jerk segment. Within this contribution, the focus is put on the composition of the overall trajectories, assuming the required motion primitives to be readily available. Since the scheme considered is not time-optimal, even decreasing particular constraints can reduce the overall transition time, which is analysed in detail. This observation implies that replanning of the underlying jerk segments is required as an integral part of the motion planning scheme, further insight into which has been analysed in a complementary contribution. Although the proposed scheme is not time-optimal, it allows for significantly shorter transition times than established methods, such as zero-vibration shaping, while requiring significantly lower computational power than a fully time-optimal scheme.
Conformal Prediction for Distribution-free Optimal Control of Linear Stochastic Systems
Authors: Eleftherios E. Vlahakis, Lars Lindemann, Pantelis Sopasakis, Dimos V. Dimarogonas
Date: 2024-11-28 00:00:00
Tasks: Conformal Prediction

We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the $S$-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size.
Modeling and Designing Non-Pharmaceutical Interventions in Epidemics: A Submodular Approach
Authors: Shiyu Cheng, Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark, Radha Poovendran
Date: 2024-11-28 00:00:00

This paper considers the problem of designing non-pharmaceutical intervention (NPI) strategies, such as masking and social distancing, to slow the spread of a viral epidemic. We formulate the problem of jointly minimizing the infection probabilities of a population and the cost of NPIs based on a Susceptible-Infected-Susceptible (SIS) propagation model. To mitigate the complexity of the problem, we consider a steady-state approximation based on the quasi-stationary (endemic) distribution of the epidemic, and prove that the problem of selecting a minimum-cost strategy to satisfy a given bound on the quasi-stationary infection probabilities can be cast as a submodular optimization problem, which can be solved in polynomial time using the greedy algorithm. We carry out experiments to examine effects of implementing our NPI strategy on propagation and control of epidemics on a Watts-Strogatz small-world graph network. We find the NPI strategy reduces the steady state of infection probabilities of members of the population below a desired threshold value.
Using dynamic extensions for the backstepping control of hyperbolic systems
Authors: Nicole Gehring, Joachim Deutscher, Abdurrahman Irscheid
Date: 2024-11-28 00:00:00

This paper systematically introduces dynamic extensions for the boundary control of general heterodirectional hyperbolic PDE systems. These extensions, which are well known in the finite-dimensional setting, constitute the dynamics of state feedback controllers. They make it possible to achieve design goals beyond what can be accomplished by a static state feedback. The design of dynamic state feedback controllers is divided into first introducing an appropriate dynamic extension and then determining a static feedback of the extended state, which includes the system and controller state, to meet some design objective. In the paper, the dynamic extensions are chosen such that all transport velocities are homogenized on the unit spatial interval. Based on the dynamically extended system, a backstepping transformation allows to easily find a static state feedback that assigns a general dynamics to the closed-loop system, with arbitrary in-domain couplings. This new design flexibility is also used to determine a feedback that achieves complete input-output decoupling in the closed loop with ensured internal stability. It is shown that the modularity of this dynamic feedback design allows for a straightforward transfer of all results to hyperbolic PDE-ODE systems. An example demonstrates the new input-output decoupling approach by dynamic extension.
Occam's Razor in Residential PV-Battery Systems: Theoretical Interpretation, Practical Implications, and Possible Improvements
Authors: Mostafa Farrokhabadi
Date: 2024-11-28 00:00:00

This paper presents a theoretical interpretation and explores possible improvements of a widely adopted rule-based control for residential solar photovoltaics (PV) paired with battery storage systems (BSS). The method is referred to as Occam's control in this paper, given its simplicity and as a tribute to the 14th-century William of Ockham. Using the self-consumption-maximization application, it is proven that Occam's control is a special case of a larger category of optimization methods called online convex learning. Thus, for the first time, a theoretical upper bound is derived for this control method. Furthermore, based on the theoretical insight, an alternative algorithm is devised on the same complexity level that outperforms Occam's. Practical data is used to evaluate the performance of these learning methods as compared to the classical rolling-horizon linear/quadratic programming. Findings support online learning methods for residential applications given their low complexity and small computation, communication, and data footprint. Consequences include improved economics for residential PV-BSS systems and mitigation of distribution systems' operational challenges associated with high PV penetration.
Self-Adaptive Active Damping Method for Stability Enhancement of Systems With Black-Box Inverters Considering Operating Points
Authors: Yang Li, Xiangyang Wu, Zhikang Shuai, Junbin Fang, Lili He, Yi Lei, Z. John Shen
Date: 2024-11-28 00:00:00

Due to the black-box nature of inverters and the wide variation range of operating points, it is challenging to on-line predict and adaptively enhance the stability of inverter-based systems. To solve this problem, this paper provides a feasible self-adaptive active damping method to eliminate potential small-signal instability of systems with black-box inverters under multiple operating points. First, the framework that includes grid impedance estimation, inverters' admittance identification, and self-adaptive strategy is presented. Second, a widely-applicable and engineering-friendly method for inductive-resistive grid impedance estimation is studied, in which a frequency-integral-based dq-axis aligning method is presented to avoid the inaccuracy resulting from the disturbance theta. Then, to make the system have a sufficient stable margin under different operating points, a self-adaptive active damper (SAD) as well as its control strategy with lag compensator modification is proposed, in which the SAD's damping compensation mechanism for the system's stability enhancement is investigated and revealed. Finally, the mapping between system's parameter variations and SAD's parameters is established based on the artificial neural network (ANN) technique, serving as a computationally light model surrogate that is favorable for on-line parameter-tuning for SAD to compensate the system's damping according to operating points. The effectiveness of the proposed method is verified by simulations in PSACD/EMTDC and experiments in RT-Lab platforms.