CyberSec Research

We propose a framework that enables autonomous vehicles (AVs) to proactively shape the intentions and behaviors of interacting human drivers. The framework employs a leader-follower game model with an adaptive role mechanism to predict human interaction intentions and behaviors. It then utilizes a branch model predictive control (MPC) algorithm to plan the AV trajectory, persuading the human to adopt the desired intention. The proposed framework is demonstrated in an intersection scenario. Simulation results illustrate the effectiveness of the framework for generating persuasive AV trajectories despite uncertainties.

This paper develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios. A novel hierarchical game-based decision framework is developed for the ego vehicle. This framework features an interaction graph, which characterizes the interaction relationships between the ego and its surrounding traffic agents (including AVs, human driven vehicles, pedestrians, and bicycles, and others), and enables the ego to smartly select a limited number of agents as its game players. Compared to the standard multi-player games, where all surrounding agents are considered as game players, the hierarchical game significantly reduces the computational complexity. In addition, compared to pairwise games, the most popular approach in the literature, the hierarchical game promises more efficient decisions for the ego (in terms of less unnecessary waiting and yielding). To further reduce the computational cost, we then propose an improved hierarchical game, which decomposes the hierarchical game into a set of sub-games. Decision safety and efficiency are analyzed in both hierarchical games. Comprehensive simulation studies are conducted to verify the effectiveness of the proposed frameworks, with an intersection-crossing scenario as a case study.

We demonstrate the first SCL-band long-haul transmission using G.654.E-compliant fibre, achieving 100.8 Tb/s (GMI) over 1552 km, despite its 1520 nm cutoff wavelength. Due to the fibre's ultra-low loss and low nonlinearity, the achievable-information-rate with lumped amplification is comparable to that of G.652.D-compliant fibre links with distributed-Raman-amplification.

With the large-scale integration of electric vehicles (EVs) in the distribution grid, the unpredictable nature of EV charging introduces considerable uncertainties to the grid's real-time operations. This can exacerbate load fluctuations, compromise power quality, and pose risks to the grid's stability and security. However, due to their dual role as controllable loads and energy storage devices, EVs have the potential to mitigate these fluctuations, balance the variability of renewable energy sources, and provide ancillary services that support grid stability. By leveraging the bidirectional flow of information and energy in smart grids, the adverse effects of EV charging can be minimized and even converted into beneficial outcomes through effective real-time management strategies. This paper explores the negative impacts of EV charging on the distribution system's real-time operations and outlines methods to transform these challenges into positive contributions. Additionally, it provides an in-depth analysis of the real-time management system for EV charging, focusing on state estimation and management strategies.

Stray flux tubes around cylindrical poles are commonly modelled starting from the results for planar flux tubes using the circumference of the cylinder as depth. While this is a tried and tested approach, we here discuss analytical expressions using the actual axisymmetric geometry of a fraction of a hollow torus and compare their results to those of the accepted approach.

Efficient greenhouse management is essential for sustainable food production in response to a growing global population. However, maintaining optimal indoor climates requires significant energy and resources, making advanced control systems critical for economic viability and environmental sustainability. Traditional greenhouse models are often complex and imprecise, limiting the effectiveness of conventional control strategies. To address these challenges, this study investigates data-driven predictive control methods using Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) neural networks. Our experiments showed that GRU-based predictive control reduced temperature and humidity violations by up to 5\% and required 40\% less computation time than the LSTM approach, all while maintaining equivalent economic performance and crop yield. These findings demonstrate that GRU-based predictive control offers a more efficient and practical solution for real-time greenhouse climate regulation in precision agriculture.

This letter proposes a deep neural network (DNN)-based neuro-adaptive sliding mode control (SMC) strategy for leader-follower tracking in multi-agent systems with higher-order, heterogeneous, nonlinear, and unknown dynamics under external disturbances. The DNN is used to compensate the unknown nonlinear dynamics with higher accuracy than shallow neural networks (NNs) and SMC ensures robust tracking. This framework employs restricted potential functions within a set-theoretic paradigm to ensure system trajectories remain bounded within a compact set, improving robustness against approximation errors and external disturbances. The control scheme is grounded in non-smooth Lyapunov stability theory, with update laws derived for both inner and outer layer network weights of DNN. A numerical example is simulated that showcases the proposed controller's effectiveness, adaptability, and robustness.

One of the fundamental challenges for non-Cartesian MRI is the need of designing time-optimal and hardware-compatible gradient waveforms for the provided $k$-space trajectory. Currently dominant methods either work only for certain trajectories or require significant computation time. In this paper, we aim to develop a fast general method that is able to generate time-optimal gradient waveforms for arbitrary non-Cartesian trajectories satisfying both slew rate and gradient constraints. In the proposed method, the gradient waveform is projected into a space defined by the gradients along the spatial directions, termed as $g$-space. In the constructed $g$-space, the problem of finding the next gradient vector given the current gradient vector under desired slew rate limit and with desired direction is simplified to finding the intersection between a line and a circle. To handle trajectories with increasing curvature, a Forward and Backward Sweep (FBS) strategy is introduced, which ensures the existence of the solution to the above mentioned geometry problem for arbitrary trajectories. Furthermore, trajectory reparameterization is proposed to ensure trajectory fidelity. We compare the proposed method with the previous optimal-control method in simulations and validate its feasibility for real MR acquisitions in phantom and human knee for a wide range of non-Cartesian trajectories. The proposed method enables accurate and fast gradient waveform design, achieving significant reduction in computation time and slew rate overshoot compared to the previous method. The source code will be publicly accessible upon publication of this study.

This paper proposes an algorithm to efficiently solve multistage stochastic programs with block separable recourse where each recourse problem is a multistage stochastic program with stage-wise independent uncertainty. The algorithm first decomposes the full problem into a reduced master problem and subproblems using Adaptive Benders decomposition. The subproblems are then solved by an enhanced SDDP. The enhancement includes (1) valid bounds at each iteration, (2) a path exploration rule, (3) cut sharing among subproblems, and (4) guaranteed {\delta}-optimal convergence. The cuts for the subproblems are then shared by calling adaptive oracles. The key contribution of the paper is the first algorithm for solving this class of problems. The algorithm is demonstrated on a power system investment planning problem with multi-timescale uncertainty. The case study results show that (1) the proposed algorithm can efficiently solve this type of problem, (2) deterministic wind modelling underestimate the objective function, and (3) stochastic modelling of wind leads to different investment decisions.

We provide experimental validation, in a pair of vehicles, of a recently introduced predictor-based cooperative adaptive cruise control (CACC) design, developed for achieving delay compensation in heterogeneous vehicular platoons subject to long actuation delays that may be distinct for each individual vehicle. We provide the explicit formulae of the control design that is implemented, accounting for the effect of zero-order hold and sampled measurements; as well as we obtain vehicle and string stability conditions numerically, via derivation of the transfer functions relating the speeds of pairs of consecutive vehicles. We also present consistent simulation results for a platoon with a larger number of vehicles, under digital implementation of the controller. Both the simulation and experimental results confirm the effectiveness of the predictor-based CACC design in guaranteeing individual vehicle stability, string stability, and tracking, despite long/distinct actuation delays.

Modeling and evaluation of automated vehicles (AVs) in mixed-autonomy traffic is essential prior to their safe and efficient deployment. This is especially important at urban junctions where complex multi-agent interactions occur. Current approaches for modeling vehicular maneuvers and interactions at urban junctions have limitations in formulating non-cooperative interactions and vehicle dynamics within a unified mathematical framework. Previous studies either assume predefined paths or rely on cooperation and central controllability, limiting their realism and applicability in mixed-autonomy traffic. This paper addresses these limitations by proposing a modeling framework for trajectory planning and decentralized vehicular control at urban junctions. The framework employs a bi-level structure where the upper level generates kinematically feasible reference trajectories using an efficient graph search algorithm with a custom heuristic function, while the lower level employs a predictive controller for trajectory tracking and optimization. Unlike existing approaches, our framework does not require central controllability or knowledge sharing among vehicles. The vehicle kinematics are explicitly incorporated at both levels, and acceleration and steering angle are used as control variables. This intuitive formulation facilitates analysis of traffic efficiency, environmental impacts, and motion comfort. The framework's decentralized structure accommodates operational and stochastic elements, such as vehicles' detection range, perception uncertainties, and reaction delay, making the model suitable for safety analysis. Numerical and simulation experiments across diverse scenarios demonstrate the framework's capability in modeling accurate and realistic vehicular maneuvers and interactions at various urban junctions, including unsignalized intersections and roundabouts.

In recent years, mutual information optimal control has been proposed as an extension of maximum entropy optimal control. Both approaches introduce regularization terms to render the policy stochastic, and it is important to theoretically clarify the relationship between the temperature parameter (i.e., the coefficient of the regularization term) and the stochasticity of the policy. Unlike in maximum entropy optimal control, this relationship remains unexplored in mutual information optimal control. In this paper, we investigate this relationship for a mutual information optimal control problem (MIOCP) of discrete-time linear systems. After extending the result of a previous study of the MIOCP, we establish the existence of an optimal policy of the MIOCP, and then derive the respective conditions on the temperature parameter under which the optimal policy becomes stochastic and deterministic. Furthermore, we also derive the respective conditions on the temperature parameter under which the policy obtained by an alternating optimization algorithm becomes stochastic and deterministic. The validity of the theoretical results is demonstrated through numerical experiments.

Any agents we can possibly build are subject to capacity constraints, as memory and compute resources are inherently finite. However, comparatively little attention has been dedicated to understanding how agents with limited capacity should allocate their resources for optimal performance. The goal of this paper is to shed some light on this question by studying a simple yet relevant continual learning problem: the capacity-constrained linear-quadratic-Gaussian (LQG) sequential prediction problem. We derive a solution to this problem under appropriate technical conditions. Moreover, for problems that can be decomposed into a set of sub-problems, we also demonstrate how to optimally allocate capacity across these sub-problems in the steady state. We view the results of this paper as a first step in the systematic theoretical study of learning under capacity constraints.

We address the problem of steering the phase distribution of oscillators all receiving the same control input to a given target distribution. In a large population limit, the distribution of oscillators can be described by a probability density. Then, our problem can be seen as that of ensemble control with a constraint on the steady-state density. In particular, we consider the case where oscillators are subject to stochastic noise, for which the theoretical understanding is still lacking. First, we characterize the reachability of the phase distribution under periodic feedforward control via the Fourier coefficients of the target density and the phase sensitivity function of oscillators. This enables us to design a periodic input that makes the stationary distribution of oscillators closest to the target by solving a convex optimization problem. Next, we devise an ensemble control method combining periodic and feedback control, where the feedback component is designed to accelerate the convergence of the distribution of oscillators. We exhibit some convergence results for the proposed method, including a result that holds even under measurement errors in the phase distribution. The effectiveness of the proposed method is demonstrated by a numerical example.

Sequence modeling is crucial for AI to understand temporal data and detect complex time-dependent patterns. While recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformers have advanced in capturing long-range dependencies, they struggle with achieving high accuracy with very long sequences due to limited memory retention (fixed context window). State-Space Models (SSMs) leverage exponentially decaying memory enabling lengthy context window and so they process very long data sequences more efficiently than recurrent and Transformer-based models. Unlike traditional neural models like CNNs and RNNs, SSM-based models require solving differential equations through continuous integration, making training and inference both compute- and memory-intensive on conventional CPUs and GPUs. In this paper we introduce a specialized hardware accelerator, EpochCore, for accelerating SSMs. EpochCore is based on systolic arrays (SAs) and is designed to enhance the energy efficiency and throughput of inference of SSM-based models for long-range sequence tasks. Within the SA, we propose a versatile processing element (PE) called LIMA-PE to perform traditional and specialized MAC operations to support traditional DNNs and SSMs. To complement the EpochCore microarchitecture, we propose a novel dataflow, ProDF, which enables highly efficient execution of SSM-based models. By leveraging the LIMA-PE microarchitecture and ProDF, EpochCore achieves on average 250x gains in performance and 45x improvement in energy efficiency, at the expense of 2x increase in area cost over traditional SA-based accelerators, and around ~2,000x improvement in latency/inference on LRA datasets compared to GPU kernel operations.

Advances in wearable robotics challenge the traditional definition of human motor systems, as wearable robots redefine body structure, movement capability, and perception of their own bodies. We measured gait performance and perceived body images via Selected Coefficient of Perceived Motion, SCoMo, after each training session. Based on human motor learning theory extended to wearer-robot systems, we hypothesized that learning the perceived body image when walking with a robotic leg co-evolves with the actual gait improvement and becomes more certain and more accurate to the actual motion. Our result confirmed that motor learning improved both physical and perceived gait pattern towards normal, indicating that via practice the wearers incorporated the robotic leg into their sensorimotor systems to enable wearer-robot movement coordination. However, a persistent discrepancy between perceived and actual motion remained, likely due to the absence of direct sensation and control of the prosthesis from wearers. Additionally, the perceptual overestimation at the later training sessions might limit further motor improvement. These findings suggest that enhancing the human sense of wearable robots and frequent calibrating perception of body image are essential for effective training with lower limb wearable robots and for developing more embodied assistive technologies.

The state of charge of battery systems is an important metric typically estimated by observation models, represented by open-circuit voltage graphs. These observation models are often nonlinear in the state of charge, resulting in varying observability from a state estimation perspective. In this paper, we employ a stochastic optimal control (also known as dual control) approach to simultaneously satisfy the control objective in the state of charge of battery systems and improve estimation accuracy. This is achieved implicitly by prioritizing trajectories that pass through high-observability regions of the state space, thereby improving the quality of future measurements. We apply our algorithm to a numerical simulation of a multi-battery system and show a statistical improvement in both the control objective and the state estimation error.

This paper introduces a novel stabilization control strategy for linear time-invariant systems affected by known time-varying measurement delays and matched unknown nonlinear disturbances, which may encompass actuator faults. It is considered that part of the state vector is not available for real-time measurement. To address this, the proposed approach combines an open-loop predictor with a state observer designed using the Super-Twisting Algorithm, aiming to compensate for the delays and estimate the unmeasured state components. Specifically, the nonlinear observer-based framework enables the reconstruction of unmodeled fault signals without assuming that they originate from a known exogenous system, offering robustness against parametric uncertainties. Meanwhile, the predictor forwards the delayed output in time. Subsequently, a sliding mode control law is formulated to enforce an ideal sliding mode and ensure global stabilization, even under a broader class of perturbations, unmodeled disturbances, parametric uncertainties, and delays, owing to the integration of the Super-Twisting observer. Numerical simulations illustrate the efficiency of the proposed approach.