Prescribed-Time Safety Filter for a 7-DOF Robot Manipulator: Experiment and Design

Bertino, Alexander; Krstić, Miroslav

doi:10.1109/tcst.2023.3243403

Cited by 4 publications

(1 citation statement)

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“…Despite the rigorous proof of safety, vanilla CBF lacks the consideration of temporal constraints. In settings where the system has to be within a safe region after a pre-specified time, the prescribed-time safety (PTSf) controller is devised in [6], [7] based on a time-varying CBF design. Compared to other CBF frameworks with time requirements such as finite-time [8], [9] and fixed-time safety controller [10], [11], PTSf controller benefits in its simple design and independence of the initial system states.…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes

Yang

Sosnowski

Liu

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.

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