提案するBarrier Integral Control (BRIC)アルゴリズムは、未知の非線形動特性を有する高次MIMO非線形システムの大域的漸近安定化を保証する。BRICアルゴリズムは、レシプロカルバリア関数と誤差積分項の革新的な統合により、滑らかなフィードバック制御を実現し、事前に定義された収束率で状態を零に収束させる。
The proposed Barrier Integral Control (BRIC) algorithm guarantees global asymptotic stabilization of uncertain nonlinear MIMO systems to the origin, while ensuring prescribed transient performance, without requiring any information or approximation of the unknown system dynamics.
Nonlinear feedback systems can be analyzed for Lyapunov and asymptotic stability using a general notion of dissipativity with dynamic supply rates, which extends classical dissipativity with static supply rates.
The core message of this article is to propose a modular safety filter layer that ensures constraint satisfaction and various stability specifications, enabling the integration of learning-based controllers and humans into safety-critical systems.
The proposed attention Kalman filter (AtKF) incorporates a self-attention mechanism to better capture dependencies in state sequences, improving the accuracy and robustness of state estimation in nonlinear systems compared to traditional Kalman filtering approaches.
A method is proposed to efficiently synthesize Control Lyapunov-Value Functions (CLVFs) for high-dimensional nonlinear systems by decomposing the system into low-dimensional subsystems, reconstructing the full-dimensional CLVF, and providing sufficient conditions for when the reconstruction is exact.
Dynamic safety margins can be used to systematically design control barrier functions that guarantee safety and recursive feasibility for constrained nonlinear systems.