Ultimate Boundedness and Output Convergence of Prioritized Output Tracking Control Under Nonsmooth and Imperfect Feedback Linearization
Sang-ik An, Dongheui Lee, Gyunghoon Park
IF 7 (2025)
IEEE Transactions on Automatic Control
The concept of priority has been introduced to the robotic systems in 1980s as an effort to overcome problems caused by singularity and it has attracted considerable attention from both the robotics and control societies. However, none of the previous works have successfully addressed two fundamental degenerate properties of singularity: nonsmoothness and imperfect inversion. This technical note proposes a prioritized output tracking control method that guarantees the ultimate boundedness and the convergence of higher priority outputs when the input–output feedback linearization becomes nonsmooth and imperfect by singularity. For that purpose, we first prioritize the input–output feedback linearization and discuss how two degenerate properties can occur when the system becomes singular. Then, we introduce the differential inclusion to deal with the nonsmooth internal dynamics and establish a condition using the upper Dini derivative for asymptotically stable zero dynamics. Also, we propose a strictly positive realness-based feedback gain design and find a condition related to an M-matrix in order to handle nonlinearities that appear in the imperfect feedback linearization. Finally, we combine these two results and show the ultimate boundedness and the output convergence. In addition, we provide a motivational example with a planar four-link manipulator to illustrate the effectiveness and limitations of the proposed method.
A Sliding Mode Approach to Asymptotic Recovery of Nominal Performance for Uncertain Linear Systems
Hyuntae Kim, Hyungbo Shim, Nam Hoon Jo, Mohammad Ataei, Gyunghoon Park
IF 7.2 (2025)
IEEE Transactions on Industrial Electronics
This article presents a new structure of a sliding mode disturbance observer (SM-DOB) for handling model uncertainty and unmodeled disturbances. The proposed SM-DOB is composed of two key components. First, a Levant’s differentiator is used to estimate the high-order time derivatives of the output. To satisfy the conditions required for the effective operation of Levant’s differentiator, we employ an integral SM technique so that on the sliding surface, the real plant’s dynamics are substituted with the nominal dynamics. With this structure, the proposed SM-DOB can achieve robust disturbance rejection and recover nominal performance in finite time. The stability and the finite time convergence of the proposed SM-DOB are rigorously proved by the Lyapunov stability method. Finally, to show the superiority and verify the validity of the proposed SM-DOB, we conduct both numerical simulations and laboratory experiments with brushless dc (BLDC) motor drives. It is observed that the proposed SM-DOB guarantees satisfactory performance in rejecting both input disturbance and plant uncertainty while remaining robust in the presence of noisy measurement.
□ 적응적이고 설명가능한 협력 자율주행을 위한 실시간 차량동역학 학습 및 공유 기술 개발□ 결과물- 실시간 차량동역학 협력 학습 및 공유 기술 및 시작품(SW,HW)- 차량동역학 학습 활용 협력 자율주행 기술(적응제어, 신뢰도 분석, 미래예측을 위한 실시간 시뮬레이션 기술)
자율주행
동역학학습
설명가능한 인공지능
시스템 식별
2
2021년 6월-2023년 2월
|42,984,000원
불확실한 환경에서 지능형 로봇의 안전성을 보장하는 강인 제어 이론 연구
본 과제는 불확실성이 존재하는 환경에서 지능형 로봇의 작업 수행 중 안전성을 강인하게 보장하는 제어 알고리즘을 연구하는 과제임. 다관절 로봇의 동역학식을 기반으로 일반적인 해법을 마련하고, 물리 엔진 기반 모의실험과 실제 로봇 플랫폼 실험을 통해 안전성 보장 가능성을 검증하는 것이 목표임.
핵심 연구 내용은 모델 불확실성과 예상치 못한 외력에 강인한 input-to-state safety 기반 제어 이론 개발, 부정확한 장애물 위치에 대응하는 reaction force 추정 및 admittance 기반 동적 안전 영역 생성 기법 연구임. 기대 효과는 지능형 로봇 안전성의 이론적 검증, 범용 제어 알고리즘 설계 방법 제시, 휴머노이드·모바일 매니퓰레이터 등 다양한 플랫폼 적용 가능성 확보, 학문후속세대 양성임.