Multi-turn Retrieval-Augmented Generation faces structural challenges that go beyond single-turn retrieval and fusion. Context-dependent queries, cross-turn evidence accumulation, and uncertain answerability jointly affect retrieval quality and generation reliability. We propose a structured control framework that formulates multi-turn RAG as a regulated reasoning process rather than a loosely coupled pipeline. The system first performs evidence and context structuring, extracting atomic facts strictly grounded in reference passages while reconstructing a self-contained query from dialogue history. It then conducts decision-conditioned generation, where explicit control signals regarding question intent, dialogue dependency, and answerability govern response feasibility, scope, and organization. By separating structural decision making from surface realization, the framework enforces consistent information flow across stages and reduces hallucination.Experiments on SemEval-2026 Task 8 show that our approach achieves strong faithfulness and stable overall performance, ranking 17/26 on Task B (generation, H=0.6333).