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Adaptive Prescribed Performance Asymptotic Tracking for High-Order Odd-Rational-Power Nonlinear Systems

Lv, Maolong (author), De Schutter, B.H.K. (author), Cao, Jinde (author), Baldi, S. (author)

Practical tracking results have been reported in the literature for high-order odd-rational-power nonlinear dynamics (a chain of integrators whose power is the ratio of odd integers). Asymptotic tracking remains an open problem for such dynamics. This note gives a positive answer to this problem in the framework of prescribed performance...

journal article 2023

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Mean Field Behavior of Collaborative Multiagent Foragers

Jarne Ornia, D. (author), Zufiria, Pedro J. (author), Mazo, M. (author)

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain...

journal article 2022

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Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Chen, N. (author), van Arem, B. (author), Wang, M. (author)

Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative...

journal article 2022

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Neural-adaptive constrained flight control for air ground recovery under terrain obstacles

Su, Zikang (author), Wang, X. (author), Wang, Honglun (author)

This article contrives a neural-adaptive constrained controller of the cable towed air-ground recovery system subject to terrain obstacles, unmeasurable cable tensions, trailing vortex, wind gust, and actuator saturation. In air-ground recovery system modeling, the towed vehicle's nominal 6 DOF affine nonlinear dynamics and the cable system's...

journal article 2022

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Learning Tracking Control for Cyber-Physical Systems

Wu, C. (author), Pan, W. (author), Sun, Guanghui (author), Liu, Jianxing (author), Wu, Ligang (author)

This paper investigates the problem of optimal tracking control for cyber-physical systems (CPS) when the cyber realm is attacked by denial-of-service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and...

journal article 2021

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Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

Zhang, Q. (author), Pan, W. (author), Reppa, V. (author)

This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed...

journal article 2021

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Distributed Time-Varying Optimization of Second-Order Multiagent Systems Under Limited Interaction Ranges

Hong, Huifen (author), Baldi, S. (author), Yu, Wenwu (author), Yu, Xinghuo (author)

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the...

journal article 2022

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Abstracting the Traffic of Nonlinear Event-Triggered Control Systems

Delimpaltadakis, Giannis (author), Mazo, M. (author)

Scheduling communication traffic in networks of event-triggered control (ETC) systems is challenging, as their sampling times are unknown, hindering application of ETC in networks. In previous work, finite-state abstractions were created, capturing the sampling behavior of linear time-invariant (LTI) ETC systems with quadratic triggering...

journal article 2023

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Actuating Eigenmanifolds of Conservative Mechanical Systems Via Bounded or Impulsive Control Actions

Della Santina, C. (author), Calzolari, Davide (author), Giordano, Alessandro Massimo (author), Albu-Schaffer, Alin (author)

Eigenmanifolds are two-dimensional submanifolds of the state space, which generalize linear eigenspaces to nonlinear mechanical systems. Initializing a robot on an Eigenmanifold (or driving it there by control) yields hyper-efficient and regular oscillatory behaviors, called modal oscillations. This letter investigates the possibility of...

journal article 2021

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Exciting efficient oscillations in nonlinear mechanical systems through Eigenmanifold stabilization

Della Santina, C. (author), Albu-Schaeffer, Alin (author)

Nonlinear modes are a well investigated concept in dynamical systems theory, extending the celebrated modal analysis of linear mechanical systems to nonlinear ones. This letter moves a first step in the direction of combining control theory and nonlinear modal analysis towards the implementation of hyper-efficient oscillatory behaviors in...

journal article 2021

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Promises of Deep Kernel Learning for Control Synthesis

Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)

Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...

journal article 2023

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Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

Zhu, H. (author), Martinez Claramunt, Francisco (author), Ferreira de Brito, B.F. (author), Alonso-Mora, J. (author)

This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by...

journal article 2021

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Probabilistic Risk Metric for Highway Driving Leveraging Multi-Modal Trajectory Predictions

Wang, X. (author), Alonso-Mora, J. (author), Wang, M. (author)

Road traffic safety has attracted increasing research attention, in particular in the current transition from human-driven vehicles to autonomous vehicles. Surrogate measures of safety are widely used to assess traffic safety but they typically ignore motion uncertainties and are inflexible in dealing with two-dimensional motion. Meanwhile,...

journal article 2022

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Unraveling Gap Selection Process during Discretionary Lane Changing by Vehicle Class

Sharma, Salil (author), van Lint, J.W.C. (author), Tavasszy, Lorant (author), Snelder, M. (author)

This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large...

journal article 2022

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A Pitch-Matched Low-Noise Analog Front-End With Accurate Continuous Time-Gain Compensation for High-Density Ultrasound Transducer Arrays

Guo, P. (author), Chang, Z.Y. (author), Noothout, E.C. (author), Vos, H.J. (author), Bosch, Johan G. (author), de Jong, N. (author), Verweij, M.D. (author), Pertijs, M.A.P. (author)

This article presents a compact analog front-end (AFE) circuit for ultrasound receivers with linear-in-dB continuous gain control for time-gain compensation (TGC). The AFE consists of two variable-gain stages, both of which employ a novel complementary current-steering network (CCSN) as the interpolator to realize continuously variable gain....

journal article 2022

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Contingency Games for Multi-Agent Interaction

Peters, L. (author), Bajcsy, Andrea (author), Chiu, Chih Yuan (author), Fridovich-Keil, David (author), Laine, Forrest (author), Ferranti, L. (author), Alonso-Mora, J. (author)

Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot's actions impact the...

journal article 2024

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What driving style makes pedestrians think a passing vehicle is driving automatically?

Bazilinskyy, P. (author), Sakuma, Tsuyoshi (author), de Winter, J.C.F. (author)

An important question in the development of automated vehicles (AVs) is which driving style AVs should adopt and how other road users perceive them. The current study aimed to determine which AV behaviours contribute to pedestrians' judgements as to whether the vehicle is driving manually or automatically as well as judgements of likeability....

journal article 2021

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Stabilizability of Linear Dynamical Systems Using Sparse Control Inputs

Sriram, Chandrasekhar (author), Joseph, G. (author), Murthy, Chandra R. (author)

The stabilizability of a linear dynamical system (LDS) refers to the existence of control inputs that drive the system state to zero. In this article, we analyze both the theoretical and algorithmic aspects of the stabilizability of an LDS using sparse control inputs with potentially time-varying supports. We show that an LDS is stabilizable...

journal article 2022

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Data-driven Abstractions for Verification of Linear Systems

Coppola, R. (author), Peruffo, A. (author), Mazo, M. (author)

We introduce a novel approach for the construction of symbolic abstractions - simpler, finite-state models - which mimic the behaviour of a system of interest, and are commonly utilized to verify complex logic specifications. Such abstractions require an exhaustive knowledge of the concrete model, which can be difficult to obtain in real...

journal article 2023

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Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Liu, Xinjie (author), Peters, L. (author), Alonso-Mora, J. (author)

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in...

journal article 2023