Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
020
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
013
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
020
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
013
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
020
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
013
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
020
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
013
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
020
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
013
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
020
Nano Banana
Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.
013
Nano Banana