Abstract:In this paper, constrained parameter update laws for adaptive control are developed using barrier constraints. An interpretation of the parameter update law from a constrained optimization problem, in which a regularized Barrier saddle function is formulated to incorporate parameter constraints using inverse and logarithmic barrier functions from interior-point methods. The resulting constrained update law is integrated with an adaptive trajectory tracking controller, enabling online learning of the unknown system model parameters. Forward invariance of the parameter estimate is established and Lyapunov stability of the closed-loop system with the constrained parameter update law is derived. The effectiveness of the proposed constrained adaptive control law is demonstrated through simulations, which validate its ability to maintain parameter estimates within prescribed bounds while ensuring convergence to the true parameter values and achieving steady state tracking performance.
From: Ashwin Dani [view email]
[v1]
Mon, 28 Apr 2025 01:42:57 UTC (315 KB)
[v2]
Sat, 6 Dec 2025 01:37:05 UTC (415 KB)
[v3]
Mon, 26 Jan 2026 14:00:21 UTC (444 KB)
[v4]
Thu, 27 Aug 2026 20:18:39 UTC (670 KB)
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