Safe Nonlinear Model Predictive Control using a Learned Approximate Control-Invariant Set
Year: 2024
Type: conference-paper
Abstract: In recent years, advanced model-based and datadriven control methods are unlocking agile and dynamic motions of robotics systems. However, ensuring safety with these control methods remains a challenge. This extended abstract presents a novel Model Predictive Control (MPC) scheme that can guarantee safety using a conservative approximation of a safe set (a.k.a. control-invariant set). This method replaces the classic terminal constraint with a novel receding constraint, which leads to a higher number of completed tasks, while retaining safety guarantees. We evaluated our approach on a simulated robot manipulator, empirically demonstrating its superiority to state-of-the-art MPC schemes.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893163142
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