Tendon-driven　continuum　robots　(TDCRs)　have　infinite　degrees　of　freedom　and　high　flexibility,　posing　challenges　for　accurate　modeling　and　autonomous　control,　especially　in　confined　environments.　This　paper　presents　a　model-less　optimal　visual　control　(MLOVC)　method　using　neurodynamic　optimization　to　enable　autonomous　target　tracking　of　TDCRs　in　confined　environments.　The　TDCR＇s　kinematics　are　estimated　online　from　sensory　data,　establishing　a　connection　between　the　actuator　input　and　visual　features.　An　optimal　visual　servoing　method　based　on　quadratic　programming　(QP)　is　developed　to　ensure　precise　target　tracking　without　violating　the　robot＇s　physical　constraints.　An　inverse-free　recurrent　neural　network　(RNN)-based　neurodynamic　optimization　method　is　designed　to　solve　the　complex　QP　problem.　Comparative　simulations　and　experiments　demonstrate　that　the　proposed　method　outperforms　existing　methods　in　target　tracking　accuracy　and　computational　efficiency.　The　RNN-based　controller　successfully　achieves　target　tracking　within　constraints　in　confined　environments.