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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2014.10
- 수록면
- 174 - 178 (5page)
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초록· 키워드
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In this paper, we present adaptive iterative learning control (AILC) schemes for batch-varying reference trajectories. In the general ILC, reference trajectory must be identical for all batches, but reference trajectories can be changed in dynamic systems such as robotics and chemical processes according to cycles or batches. The plant-model mismatch error must vanish to make outputs converge to different references in each batch. For this reason, Markov parameters of the system dynamics are identified at the end of each iteration in an iterative learning manner. ILC schemes for batch-varying reference trajectories are proposed in two forms, which are inverse of model-based ILC (I-ILC) and quadratic-criterion based ILC (Q-ILC). These control schemes are studied for discrete linear time-invariant (LTI) system. A numerical example is provided to demonstrate the performance of the proposed algorithm.
목차
Abstract
1. INTRODUCTION
2. PRELIMINARY
3. ADAPTIVE ITERATIVE LEARNING CONTROL FOR BATCH-VARYING REFERENCE TRAJECTORIES
4. NUMERICAL ILLUSTRATION AND RESULTS
5. CONCLUDING REMARKS
REFERENCES
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