335 lines
10 KiB
TOML
335 lines
10 KiB
TOML
# chassis-tune 超参数预设
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#
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# 一次整定的结果不只取决于被控对象与种子，还取决于"搜什么区间、用哪个场景、
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# 什么算好、什么算达标、搜多细"。这份文件把这些**实验条件**固化下来：
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# 评审一次、长期复用、任何机器上都能复现同一组默认行为。
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#
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# 用法：
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# chassis-tune presets # 列出本文件全部预设
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# chassis-tune sweep --preset config/tune.toml # 用 default_preset
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# chassis-tune sweep --preset config/tune.toml --preset-name bench_loaded
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# chassis-tune sweep --preset-name bench_loaded # 缺省路径即本文件
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# chassis-tune sweep --preset config/tune.toml --samples 128 # CLI 覆盖预设的单项
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#
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# 优先级：内置默认 < 本文件的预设 < 显式命令行参数（逐字段覆盖）。
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# 本文件里没写的项一律沿用内置默认，因此预设只需写"与默认不同"的部分——
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# 但为了让评审者一眼看全工况，下面每个预设都写全了关键项。
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#
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# **安全边界**：current_limit / direction / gear_ratio / 电调 ID / 串口设备名
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# 不属于超参数，本文件**不接受**这些键（写了会直接报错）。它们只来自
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# chassis.toml 或显式 CLI——否则预设文件就成了绕过限流的口子。
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#
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# schema：见 crates/chassis-tune/src/preset.rs（version = 1，未知键报错）。
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version = 1
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default_preset = "sim_bench_model"
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# ---------------------------------------------------------------- 离线：台架量级模型
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# 默认预设。评审、CI 冒烟、改完代码回归都用它：不碰硬件，结果可复现。
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[preset.sim_bench_model]
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description = "离线 · 台架量级模型（静/库仑摩擦 + 2 拍延迟）：全量搜索，评审与回归的基准"
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[preset.sim_bench_model.space]
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# kp 覆盖手调基线 2 与仿真最优 8 两侧；对数刻度保证小增益也被采样到。
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kp = { min = 0.5, max = 30.0, scale = "log" }
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# ki 允许精确取 0（纯比例在某些工况已经够用）。
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ki = { min = 0.1, max = 100.0, scale = "log0" }
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# kd 半概率取 0：速度环加微分对量化噪声敏感，先让搜索自己决定要不要。
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kd = { min = 0.01, max = 3.0, scale = "log0" }
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# 前馈上限 2.5 覆盖带载所需的 ≈1.77（raw/rpm）。
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feedforward = { min = 0.01, max = 2.5, scale = "log0" }
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[preset.sim_bench_model.scenario]
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profile = "ramp-and-hold"
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target_rpm = 1500.0
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ramp_ms = 200.0
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hold_ms = 1800.0
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dt_us = 1000.0
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[preset.sim_bench_model.cost]
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# 跟得上、不过冲最重要；电流只作次要惩罚（省电流不是目的，避免烧电调才是）。
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settle = 1.0
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overshoot = 1.0
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iae = 0.5
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effort = 0.1
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[preset.sim_bench_model.acceptance]
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max_overshoot_pct = 20.0
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max_ss_error_pct = 5.0
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[preset.sim_bench_model.search]
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seed = 20260101
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samples = 512
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top_k = 8
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solver = "nelder-mead"
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max_iters = 200
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particles = 24
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max_evals = 4000
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threads = 0
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[preset.sim_bench_model.sim]
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plant = "gm3508-bench"
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# 域随机化：单点模型调出来的增益对增益/时间常数/延迟高度敏感，
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# 因此默认在 8 个成员的集成上做鲁棒优化，而不是对着一条理想曲线最优化。
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ensemble = 8
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robust_weight = 0.5
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gain_pct = 0.2
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tau_pct = 0.3
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delay_ticks_max = 3
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friction_pct = 0.5
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[preset.sim_bench_model.bench]
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# 离线预设不用台架，但安全包络仍写全，方便临时切 --backend bench 预演。
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max_rpm = 3000.0
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max_temp_c = 70
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trial_timeout_ms = 6000
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between_trials_ms = 500
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startup_timeout_ms = 5000
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continue_on_abort = false
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validate_runs = 3
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# ---------------------------------------------------------------- 离线：理想模型
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# 无摩擦、无延迟的一阶模型：跑得快，适合改完控制逻辑后的秒级回归，
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# **不要**拿它的结果直接上车（模型里没有摩擦与延迟，最优解会偏激进）。
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[preset.sim_ideal]
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description = "离线 · 理想一阶模型（无摩擦无延迟）：秒级回归用，结果不可直接上车"
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[preset.sim_ideal.space]
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kp = { min = 0.5, max = 30.0, scale = "log" }
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ki = { min = 0.1, max = 100.0, scale = "log0" }
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kd = { min = 0.01, max = 3.0, scale = "log0" }
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feedforward = { min = 0.01, max = 2.5, scale = "log0" }
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[preset.sim_ideal.scenario]
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profile = "ramp-and-hold"
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target_rpm = 1500.0
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ramp_ms = 200.0
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hold_ms = 1800.0
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dt_us = 1000.0
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[preset.sim_ideal.cost]
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settle = 1.0
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overshoot = 1.0
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iae = 0.5
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effort = 0.1
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[preset.sim_ideal.acceptance]
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max_overshoot_pct = 20.0
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max_ss_error_pct = 5.0
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[preset.sim_ideal.search]
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seed = 20260101
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samples = 128
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top_k = 4
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solver = "nelder-mead"
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max_iters = 100
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particles = 16
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max_evals = 1000
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threads = 0
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[preset.sim_ideal.sim]
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plant = "gm3508"
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# 理想模型本身就是确定的，单成员即可。
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ensemble = 1
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robust_weight = 0.0
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gain_pct = 0.0
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tau_pct = 0.0
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delay_ticks_max = 0
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friction_pct = 0.0
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[preset.sim_ideal.bench]
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max_rpm = 3000.0
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max_temp_c = 70
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trial_timeout_ms = 6000
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between_trials_ms = 500
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startup_timeout_ms = 5000
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continue_on_abort = false
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validate_runs = 3
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# ---------------------------------------------------------------- 台架：空载
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# 轮子架空（无地面载荷）。摩擦小、静差靠积分压制，前馈**必须为 0**：
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# 空载加前馈会把轮子直接推到最高转速。目标转速也压低，便于观察与急停。
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[preset.bench_noload]
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description = "真机台架 · 空载（轮子架空）：小样本局部搜索，禁用前馈，目标 800 rpm"
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# 真机后端：仍然需要 --i-know-the-wheel-spins 才会真的驱动电机。
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backend = "bench"
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[preset.bench_noload.space]
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# 空载好解在手调基线（kp≈2, ki≈10）附近，区间收紧以提高样本密度。
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kp = { min = 0.2, max = 12.0, scale = "log" }
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ki = { min = 0.1, max = 50.0, scale = "log0" }
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kd = { min = 0.01, max = 1.0, scale = "log0" }
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# 固定为 0：单点区间 + 线性刻度 = 搜索时该轴恒定。
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feedforward = { min = 0.0, max = 0.0, scale = "linear" }
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[preset.bench_noload.scenario]
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profile = "ramp-and-hold"
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target_rpm = 800.0
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ramp_ms = 300.0
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hold_ms = 1700.0
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dt_us = 1000.0
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[preset.bench_noload.cost]
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settle = 1.0
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overshoot = 1.0
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iae = 0.5
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effort = 0.1
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[preset.bench_noload.acceptance]
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# 空载量化噪声相对目标更大，放宽到 25%，避免"为了 0.5% 的稳态误差过冲"。
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max_overshoot_pct = 25.0
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max_ss_error_pct = 8.0
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[preset.bench_noload.search]
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seed = 20260101
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# 只有一条总线，每次试验 2s，24 次 ≈ 1 分钟：够做局部精修，不够铺满空间。
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samples = 24
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top_k = 3
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solver = "nelder-mead"
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max_iters = 40
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particles = 12
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max_evals = 40
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threads = 1
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[preset.bench_noload.sim]
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# 真机后端不用这些，但保留"最接近台架的模型"，方便先离线预演再上车。
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plant = "gm3508-bench"
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ensemble = 8
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robust_weight = 0.5
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gain_pct = 0.2
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tau_pct = 0.3
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delay_ticks_max = 3
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friction_pct = 0.5
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[preset.bench_noload.bench]
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# 目标是 800，过速保护留足裕量但远低于电调上限。
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max_rpm = 2000.0
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max_temp_c = 60
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trial_timeout_ms = 6000
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between_trials_ms = 500
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startup_timeout_ms = 5000
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continue_on_abort = false
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# 同一组增益跑 3 次看重复性；离散度大说明参数贴着噪声，不该上车。
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validate_runs = 3
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# ---------------------------------------------------------------- 台架：带载
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# 整车落地 / 配重（有地面摩擦与惯量）。静差明显，前馈开始有意义（覆盖 ≈1.77）。
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[preset.bench_loaded]
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description = "真机台架 · 带载（整车落地/配重）：前馈区间上调，目标 1200 rpm，超调收紧"
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backend = "bench"
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[preset.bench_loaded.space]
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kp = { min = 0.5, max = 30.0, scale = "log" }
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ki = { min = 0.5, max = 100.0, scale = "log0" }
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kd = { min = 0.01, max = 3.0, scale = "log0" }
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# 带载需要前馈抵消摩擦：下限不为 0，让搜索必须考虑它。
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feedforward = { min = 0.5, max = 2.5, scale = "log" }
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[preset.bench_loaded.scenario]
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profile = "ramp-and-hold"
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target_rpm = 1200.0
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ramp_ms = 300.0
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hold_ms = 2200.0
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dt_us = 1000.0
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[preset.bench_loaded.cost]
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settle = 1.0
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overshoot = 1.0
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iae = 0.5
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effort = 0.1
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[preset.bench_loaded.acceptance]
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# 带载下超调意味着实际位移过冲，收紧到 15%。
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max_overshoot_pct = 15.0
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max_ss_error_pct = 5.0
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[preset.bench_loaded.search]
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seed = 20260101
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samples = 32
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top_k = 4
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solver = "nelder-mead"
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max_iters = 60
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particles = 16
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max_evals = 80
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threads = 1
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[preset.bench_loaded.sim]
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plant = "gm3508-bench"
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ensemble = 8
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robust_weight = 0.5
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gain_pct = 0.2
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tau_pct = 0.3
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delay_ticks_max = 3
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friction_pct = 0.5
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[preset.bench_loaded.bench]
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max_rpm = 2500.0
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max_temp_c = 70
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trial_timeout_ms = 8000
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between_trials_ms = 800
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startup_timeout_ms = 5000
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continue_on_abort = false
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validate_runs = 3
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# ---------------------------------------------------------------- 实战验收：双向
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# 上车前的最后一道关：升速 → 保持 → 降速 → 保持（回零），验的是双向跟随与
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# 回零能力，比单向斜坡更接近实战。样本很少（围绕现有增益做局部复算），
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# 因为此时**不该**再大改增益，只想确认现有一组在两个方向上都不出问题。
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[preset.field_updown]
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description = "真机实战验收 · 升降速双向（回零）：小样本复验，超调与稳态误差收紧"
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backend = "bench"
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[preset.field_updown.space]
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kp = { min = 0.5, max = 30.0, scale = "log" }
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ki = { min = 0.5, max = 100.0, scale = "log0" }
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kd = { min = 0.01, max = 3.0, scale = "log0" }
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feedforward = { min = 0.5, max = 2.5, scale = "log" }
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[preset.field_updown.scenario]
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profile = "up-down"
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target_rpm = 1200.0
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ramp_ms = 400.0
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hold_ms = 800.0
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dt_us = 1000.0
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[preset.field_updown.cost]
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# 双向场景里稳态误差（尤其回零）比调节时间更重要。
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settle = 1.0
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overshoot = 1.0
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iae = 1.0
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effort = 0.1
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[preset.field_updown.acceptance]
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max_overshoot_pct = 12.0
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max_ss_error_pct = 3.0
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[preset.field_updown.search]
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seed = 20260101
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samples = 16
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top_k = 2
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solver = "nelder-mead"
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max_iters = 30
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particles = 12
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max_evals = 40
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threads = 1
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[preset.field_updown.sim]
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plant = "gm3508-bench"
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ensemble = 8
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robust_weight = 0.5
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gain_pct = 0.2
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tau_pct = 0.3
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delay_ticks_max = 3
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friction_pct = 0.5
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[preset.field_updown.bench]
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max_rpm = 2500.0
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max_temp_c = 70
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# 双向场景总时长翻倍，超时也翻倍。
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trial_timeout_ms = 10000
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between_trials_ms = 1000
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startup_timeout_ms = 5000
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continue_on_abort = false
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# 验收阶段多跑几次，重复性不过关就不签字。
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validate_runs = 5
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