Adaptive Constraint-Lifting Control with Stability and Invariance Guarantees
Jhon Manuel Portella Delgado, Ankit Goel

TL;DR
This paper introduces an adaptive control method for nonlinear systems with uncertainties that guarantees stability, tracking, and safety without online optimization, using a novel constraint-lifting framework.
Contribution
It develops an adaptive constraint-lifting approach that transforms constrained control problems into unconstrained ones, enabling recursive synthesis with stability guarantees.
Findings
Successfully applied to a DC motor with uncertain parameters
Achieved accurate tracking and safety guarantees
Bounded all signals and ensured convergence despite uncertainties
Abstract
This paper develops an adaptive tracking controller for a class of nonlinear systems with parametric uncertainty subject to state constraints. The system is characterized by a strict-feedback structure with unknown parameters entering both the drift and input channels. The objective is to design a control law, without knowledge of the unknown parameters, that guarantees closed-loop stability, achieves desired tracking performance, and ensures forward invariance of a prescribed safe set. An adaptive constraint-lifting framework is developed that transforms the constrained control problem into an equivalent unconstrained representation, enabling recursive controller synthesis in lifted coordinates. The proposed design integrates parameter estimation with constraint enforcement without requiring online optimization. A Lyapunov-based stability analysis, combined with the…
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