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IEEE Open Journal of Control Systems Publication Information
Year: 2024 | Volume: 3
Adaptive Control for Singularly Perturbed Systems
Publication Year: 2024,Page(s):1 - 13
Cited by: Papers (1)
Singularly perturbed systems are a class of mathematical systems that are not well approximated by their limits and can be used to model plants with multiple fast and slow states. Multiple-timescale systems are very common in engineering applications, but adaptive control can be sensitive to timescale effects. Recently a method called [K]control of Adaptive Multiple-timescale Systems (KAMS) has sh...Show More
On the Ratio of Reactive to Active Power in Wave Energy Converter Control
Hafiz Ahsan Said;Demián García-Violini;Nicolás Faedo;John V. Ringwood
Publication Year: 2024,Page(s):14 - 31
Cited by: Papers (1)
Optimal control of wave energy converters (WECs), while converting wave energy into a usable form, such as electricity, may inject (reactive) power into the system at various points in the wave cycle. Though somewhat counter-intuitive, this action usually results in improved overall energy conversion. However, recent experimental results show that, on occasion, reactive power peaks can be signific...Show More
A Computational Framework for Optimal Adaptive Function Allocation in a Human-Autonomy Teaming Scenario
Publication Year: 2024,Page(s):32 - 44
Cited by: Papers (1)
This article proposes a quantitative framework for optimally allocating task functions in human-autonomy teaming (HAT). HAT involves cooperation between humans and autonomous agents to achieve common goals. As humans and autonomous agents possess different capabilities, function allocation plays a crucial role in ensuring effective HAT. However, designing the best adaptive function allocation rema...Show More
Human Modeling and Passivity Analysis for Semi-Autonomous Multi-Robot Navigation in Three Dimensions
Takeshi Hatanaka;Takahiro Mochizuki;Takumi Sumino;José M. Maestre;Nikhil Chopra
Publication Year: 2024,Page(s):45 - 57
In this article, we study a one-human-multiple-robot interaction for human-enabled multi-robot navigation in three dimensions. We employ two fully distributed control architectures designed based on human passivity and human passivity shortage. The first half of this article focuses on human modeling and analysis for the passivity-based control architecture through human operation data on a 3-D hu...Show More
Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications
Yue Wang;Fangjian Li;Huanfei Zheng;Longsheng Jiang;Maziar Fooladi Mahani;Zhanrui Liao
Publication Year: 2024,Page(s):58 - 86
Cited by: Papers (3)
Trust model is a topic that first gained interest in organizational studies and then human factors in automation. Thanks to recent advances in human-robot interaction (HRI) and human-autonomy teaming, human trust in robots has gained growing interest among researchers and practitioners. This article focuses on a survey of computational models of human-robot trust and their applications in robotics...Show More
A Multiplex Approach Against Disturbance Propagation in Nonlinear Networks With Delays
Publication Year: 2024,Page(s):87 - 101
Cited by: Papers (1)
We consider both leaderless and leader-follower, possibly nonlinear, networks affected by time-varying communication delays. For such systems, we give a set of sufficient conditions that guarantee the convergence of the network towards some desired behaviour while simultaneously ensuring the rejection of polynomial disturbances and the non-amplification of other classes of disturbances across the ...Show More
Classification of Human Learning Stages via Kernel Distribution Embeddings
Madeleine Shuhn-Tsuan Yuh;Kendric Ray Ortiz;Kylie Sue Sommer-Kohrt;Meeko Oishi;Neera Jain
Publication Year: 2024,Page(s):102 - 117
Cited by: Papers (2)
Adaptive automation, automation which is responsive to the human's performance via the alteration of control laws or level of assistance, is an important tool for training humans to attain new skills when operating dynamical systems. When coupled with cognitive feedback, adaptive automation has the potential to further facilitate human training, but requires precise assessments of human progressio...Show More
A Computationally-Efficient Data-Driven Safe Optimal Algorithm Through Control Merging
Publication Year: 2024,Page(s):118 - 127
This article presents a proactive approach to resolving the conflict between safety and optimality for continuous-time (CT) safety-critical systems with unknown dynamics. The presented method guarantees safety and performance specifications by combining two controllers: a safe controller and an optimal controller. On the one hand, the safe controller is designed using only input and state data mea...Show More
Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret
Publication Year: 2024,Page(s):128 - 142
Cited by: Papers (2)
We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distributions of rewards associated with arms are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget. The regret of a policy is defined by the difference in the expected cumulative reward obtained using the policy and using an oracle that selects the arm with...Show More
An Efficient Solution to Optimal Motion Planning With Provable Safety and Convergence
PANAGIOTIS ROUSSEAS;Charalampos Bechlioulis;Kostas Kyriakopoulos
Publication Year: 2024,Page(s):143 - 157
Cited by: Papers (1)
An innovative solution to the optimal motion planning problem is presented in this work. A novel parametrized actor structure is proposed, which guarantees safe and convergent navigation by construction. Concurrently, an efficient scheme for optimizing a mixed state and energy cost function is formulated. The proposed method inherits the positive traits of continuous methods, while at the same tim...Show More
Learning Robust Output Control Barrier Functions From Safe Expert Demonstrations
Lars Lindemann;Alexander Robey;Lejun Jiang;Satyajeet Das;Stephen Tu;Nikolai Matni
Publication Year: 2024,Page(s):158 - 172
Cited by: Papers (4)
This paper addresses learning safe output feedback control laws from partial observations of expert demonstrations. We assume that a model of the system dynamics and a state estimator are available along with corresponding error bounds, e.g., estimated from data in practice. We first propose robust output control barrier functions (ROCBFs) as a means to guarantee safety, as defined through control...Show More
Novel Bounds for Incremental Hessian Estimation With Application to Zeroth-Order Federated Learning
Publication Year: 2024,Page(s):173 - 189
The Hessian matrix conveys important information about the curvature, spectrum and partial derivatives of a function, and is required in a variety of tasks. However, computing the exact Hessian is prohibitively expensive for high-dimensional input spaces, and is just impossible in zeroth-order optimization, where the objective function is a black-box of which only input-output pairs are known. In ...Show More
Regional PID Control of Switched Positive Systems With Multiple Equilibrium Points
Publication Year: 2024,Page(s):190 - 201
This paper investigates the regional control problem of switched positive systems with multiple equilibrium points. A proportional-integral-derivative controller is designed by combining the output, the error between the state and the equilibrium point, and the difference of output. A cone is introduced to design the final stable region. Two classes of copositive Lyapunov functions are constructed...Show More
Model-Free Change Point Detection for Mixing Processes
Publication Year: 2024,Page(s):202 - 213
Cited by: Papers (1)
This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially $\alpha$, $\beta$, and fast $\phi$-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length ($ {\mathtt {ARL}}$) and...Show More
Self-Excited Dynamics of Discrete-Time Lur'e Systems With Affinely Constrained, Piecewise-C$^{1}$ Feedback Nonlinearities
Publication Year: 2024,Page(s):214 - 224
Self-excited systems (SES) arise in numerous applications, such as fluid-structure interaction, combustion, and biochemical systems. In support of system identification and digital control of SES, this paper analyzes discrete-time Lur'e systems with affinely constrained, piecewise-C$^{1}$ feedback nonlinearities. In particular, a novel feature of the discrete-time Lur'e system considered in this p...Show More
$\mathcal {H}_{2}$- and $\mathcal {H}_\infty$-Optimal Model Predictive Controllers for Robust Legged Locomotion
Publication Year: 2024,Page(s):225 - 238
Cited by: Papers (2)
This paper formally develops robust optimal predictive control solutions that can accommodate disturbances and stabilize periodic legged locomotion. To this end, we build upon existing optimization-based control paradigms, particularly quadratic programming (QP)-based model predictive controllers (MPCs). We present conditions under which the closed-loop reduced-order systems (i.e., template models...Show More
Navigation Systems May Deteriorate Stability in Traffic Networks
Publication Year: 2024,Page(s):239 - 252
Cited by: Papers (1)
Advanced traffic navigation systems, which provide routing recommendations to drivers based on real-time congestion information, are nowadays widely adopted by roadway transportation users. Yet, the emerging effects on the traffic dynamics originating from the widespread adoption of these technologies have remained largely unexplored until now. In this paper, we propose a dynamic model where drive...Show More
Sorta Solving the OPF by Not Solving the OPF: DAE Control Theory and the Price of Realtime Regulation
Publication Year: 2024,Page(s):253 - 265
This paper presents a new approach to approximate the AC optimal power flow (ACOPF). By eliminating the need to solve the ACOPF every few minutes, the paper showcases how a realtime feedback controller can be utilized in lieu of ACOPF and its variants. By i) forming the grid dynamics as a system of differential-algebraic equations (DAE) that naturally encode the non-convex OPF power flow constrain...Show More
Concurrent Learning of Control Policy and Unknown Safety Specifications in Reinforcement Learning
Publication Year: 2024,Page(s):266 - 281
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents the crucial challenge of ensuring safety. Traditional safe RL approaches have predominantly focused on incorporating predefined safety constraints into the policy learning process. However, this reliance on predefined s...Show More
Risk-Aware Stochastic MPC for Chance-Constrained Linear Systems
Publication Year: 2024,Page(s):282 - 294
This paper presents a fully risk-aware model predictive control (MPC) framework for chance-constrained discrete-time linear control systems with process noise. Conditional value-at-risk (CVaR) as a popular coherent risk measure is incorporated in both the constraints and the cost function of the MPC framework. This allows the system to navigate the entire spectrum of risk assessments, from worst-c...Show More
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown. A two-stage approach is proposed, which initially involves estimating the distributional dependence on decision variables, and subsequently optimizing over the estimated distribution...Show More
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
Publication Year: 2024,Page(s):310 - 324
Recent literature has proposed approaches that learn control policies with high performance while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has become an effective tool for verifying safety and supervising the training of reinforcement learning-based control policies for complex, high-dimensional systems. Previously, HJ reachability was restricted to verifying...Show More
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