louis
a03735387c
按 plan.md 新增三个交付物，全部默认关闭以保持与纯 PID 的 A/B 兼容： - valve_model.py：阀特性模型 Q_ss = A_eff(x)·P1_abs·F(r)，含 ISO 6358 椭圆 F(r)、行程/面积互逆插值、单调性校验、JSON 存取。 - identify_valve.py：从 open_loop.py 扫点 CSV 拟合 A_eff 表并输出 JSON。 - 控制器集成：config/flow_control/main/data_logger 新增阀前压读取 （channel 2）、前馈打底 + PI 修残差、按 P1 增益调度，并记录/绘制前馈项。 新增配置 FEEDFORWARD_ENABLED / GAIN_SCHEDULE_ENABLED 默认 False， VALVE_MODEL_PATH 默认空，未加载模型时行为与原先纯 PID 完全一致。
285 lines
9.0 KiB
Python
285 lines
9.0 KiB
Python
"""从开环扫点 CSV 拟合 A_eff(x) 表，输出 valve_model.json。
|
||
|
||
用法：
|
||
python identify_valve.py --csv open_loop_data/open_loop_xxx.csv \\
|
||
--out valve_model.json [--tail 20] [--plot]
|
||
|
||
流程：
|
||
1. 按 step_index 分组，每组取末尾 --tail 个采样点平均，得到稳态 (x, Q, P1, P2)；
|
||
2. 对每个稳态点反解 A_eff = Q / (P1_abs * F(r))；
|
||
3. 按行程升序构表，做单调性检查；
|
||
4. 用 ValveModel 存成 JSON，并打印摘要（可选画诊断图）。
|
||
"""
|
||
|
||
import argparse
|
||
import csv
|
||
import math
|
||
from pathlib import Path
|
||
|
||
import config
|
||
from valve_model import CRITICAL_RATIO, ValveModel, abs_pressure, f_ratio
|
||
|
||
DEFAULT_TAIL = 20
|
||
|
||
|
||
def _to_float(text):
|
||
"""把 CSV 单元格解析成有限浮点数；空/非法/非有限返回 None。"""
|
||
if text is None:
|
||
return None
|
||
text = str(text).strip()
|
||
if text == "":
|
||
return None
|
||
try:
|
||
value = float(text)
|
||
except (TypeError, ValueError):
|
||
return None
|
||
return value if math.isfinite(value) else None
|
||
|
||
|
||
def _average(values):
|
||
"""返回非 None 数值的平均；没有有效值时返回 None。"""
|
||
valid = [value for value in values if value is not None]
|
||
return None if not valid else sum(valid) / len(valid)
|
||
|
||
|
||
def load_steady_points(csv_path, tail=DEFAULT_TAIL):
|
||
"""提取每个 step_index 的稳态均值，返回按 step 升序的 (x, q, p1, p2) 列表。"""
|
||
rows_by_step = {}
|
||
with open(csv_path, "r", newline="", encoding="utf-8-sig") as file:
|
||
for row in csv.DictReader(file):
|
||
step_text = (row.get("step_index") or "").strip()
|
||
if step_text == "":
|
||
continue
|
||
try:
|
||
step = int(float(step_text))
|
||
except (TypeError, ValueError):
|
||
continue
|
||
rows_by_step.setdefault(step, []).append(row)
|
||
|
||
points = []
|
||
for step in sorted(rows_by_step):
|
||
rows = rows_by_step[step]
|
||
rows.sort(key=lambda r: _to_float(r.get("time_s")) or 0.0)
|
||
tail_rows = rows[-tail:] if tail > 0 else rows
|
||
|
||
x = _average(_to_float(r.get("motor_position")) for r in tail_rows)
|
||
q = _average(_to_float(r.get("flow_after_slm")) for r in tail_rows)
|
||
p1 = _average(_to_float(r.get("pressure_before_kpa")) for r in tail_rows)
|
||
p2 = _average(_to_float(r.get("pressure_after_kpa")) for r in tail_rows)
|
||
|
||
if None in (x, q, p1, p2):
|
||
continue
|
||
points.append((x, q, p1, p2))
|
||
return points
|
||
|
||
|
||
def compute_area_table(steady_points):
|
||
"""把稳态点换算成 (x, A_eff) 单调表，并返回丢弃点与阻塞/亚声速统计。
|
||
|
||
返回 ``(table, dropped, stats)``。``table`` 为按 x 升序的 (x, A_eff) 列表，
|
||
只保留最长的单调连续段；``dropped`` 为因非单调被丢弃的点。
|
||
"""
|
||
area_points = []
|
||
choked = subsonic = 0
|
||
for x, q, p1, p2 in steady_points:
|
||
p1_abs = abs_pressure(p1)
|
||
p2_abs = abs_pressure(p2)
|
||
if p1_abs <= 0.0:
|
||
continue
|
||
r = p2_abs / p1_abs
|
||
f = f_ratio(r)
|
||
denom = p1_abs * f
|
||
if denom <= 0.0 or q < 0.0:
|
||
continue
|
||
if r <= CRITICAL_RATIO:
|
||
choked += 1
|
||
else:
|
||
subsonic += 1
|
||
area_points.append((x, q / denom))
|
||
|
||
area_points.sort(key=lambda item: item[0])
|
||
ys = [a for _, a in area_points]
|
||
|
||
if _is_monotonic(ys):
|
||
table = area_points
|
||
dropped = []
|
||
else:
|
||
start, end = _longest_monotonic_run(ys)
|
||
table = area_points[start:end + 1]
|
||
dropped = area_points[:start] + area_points[end + 1:]
|
||
|
||
stats = {"choked": choked, "subsonic": subsonic, "total": choked + subsonic}
|
||
return table, dropped, stats
|
||
|
||
|
||
def _is_monotonic(ys):
|
||
"""序列是否单调（允许相等，但不允许中途反向）。"""
|
||
direction = 0
|
||
for i in range(1, len(ys)):
|
||
delta = ys[i] - ys[i - 1]
|
||
if delta == 0:
|
||
continue
|
||
sign = 1 if delta > 0 else -1
|
||
if direction == 0:
|
||
direction = sign
|
||
elif direction != sign:
|
||
return False
|
||
return True
|
||
|
||
|
||
def _longest_monotonic_run(ys):
|
||
"""返回最长单调连续段的闭区间下标 ``(start, end)``。"""
|
||
best_start = best_end = 0
|
||
for start in range(len(ys)):
|
||
direction = 0
|
||
end = start
|
||
for j in range(start + 1, len(ys)):
|
||
delta = ys[j] - ys[j - 1]
|
||
if delta == 0:
|
||
end = j
|
||
continue
|
||
sign = 1 if delta > 0 else -1
|
||
if direction == 0:
|
||
direction = sign
|
||
end = j
|
||
elif direction == sign:
|
||
end = j
|
||
else:
|
||
break
|
||
if end - start > best_end - best_start:
|
||
best_start, best_end = start, end
|
||
return best_start, best_end
|
||
|
||
|
||
def identify(csv_path, tail=DEFAULT_TAIL, out_path=None):
|
||
"""端到端辨识；返回 ``(model, steady_points, table, dropped, stats)``。"""
|
||
steady = load_steady_points(csv_path, tail)
|
||
table, dropped, stats = compute_area_table(steady)
|
||
if len(table) < 2:
|
||
raise ValueError(
|
||
f"有效稳态点不足（{len(table)} 个），无法构表。请确认 CSV 的 "
|
||
"pressure_before_kpa / pressure_after_kpa / flow_after_slm 列读数合理。"
|
||
)
|
||
model = ValveModel(
|
||
table,
|
||
config.MOTOR_OPEN_POSITION,
|
||
config.MOTOR_CLOSED_POSITION,
|
||
)
|
||
if out_path:
|
||
model.save(out_path)
|
||
return model, steady, table, dropped, stats
|
||
|
||
|
||
def create_diagnostic_plot(steady_points, table, dropped, image_path):
|
||
"""画 x vs A_eff（散点+插值线）与 x vs Q_ss（散点）两张子图。"""
|
||
import matplotlib
|
||
|
||
matplotlib.use("Agg")
|
||
import matplotlib.pyplot as plt
|
||
|
||
xs = [x for x, _ in table]
|
||
areas = [a for _, a in table]
|
||
|
||
figure, axes = plt.subplots(2, 1, figsize=(10, 8), constrained_layout=True)
|
||
figure.suptitle("Identified valve characteristic")
|
||
|
||
axes[0].scatter(xs, areas, color="#1565C0", label="measured A_eff")
|
||
axes[0].plot(xs, areas, color="#1565C0", linewidth=1.2)
|
||
if dropped:
|
||
axes[0].scatter(
|
||
[x for x, _ in dropped],
|
||
[a for _, a in dropped],
|
||
color="#D84315",
|
||
marker="x",
|
||
label="dropped (non-monotonic)",
|
||
)
|
||
axes[0].set_ylabel("A_eff (slm/kPa)")
|
||
axes[0].set_title("Effective area vs stroke")
|
||
axes[0].legend(loc="best")
|
||
axes[0].grid(True, alpha=0.3, linestyle="--")
|
||
|
||
qxs = [x for x, _, _, _ in steady_points]
|
||
qs = [q for _, q, _, _ in steady_points]
|
||
axes[1].scatter(qxs, qs, color="#2E7D32", label="measured Q_ss")
|
||
axes[1].set_ylabel("Flow (SLM)")
|
||
axes[1].set_xlabel("Motor stroke x")
|
||
axes[1].set_title("Steady flow vs stroke")
|
||
axes[1].legend(loc="best")
|
||
axes[1].grid(True, alpha=0.3, linestyle="--")
|
||
|
||
figure.savefig(image_path, dpi=config.PLOT_DPI, bbox_inches="tight")
|
||
plt.close(figure)
|
||
|
||
|
||
def parse_args(argv=None):
|
||
parser = argparse.ArgumentParser(
|
||
description="从开环扫点 CSV 拟合 A_eff(x) 阀特性表"
|
||
)
|
||
parser.add_argument("--csv", required=True, help="开环扫点 CSV 路径")
|
||
parser.add_argument("--out", default="valve_model.json", help="输出 JSON 路径")
|
||
parser.add_argument(
|
||
"--tail", type=int, default=DEFAULT_TAIL,
|
||
help=f"每组末尾用于平均的采样点数（默认 {DEFAULT_TAIL}）",
|
||
)
|
||
parser.add_argument("--plot", action="store_true", help="生成诊断图 PNG")
|
||
args = parser.parse_args(argv)
|
||
if args.tail < 1:
|
||
parser.error("--tail 必须 >= 1")
|
||
return args
|
||
|
||
|
||
def main(argv=None):
|
||
args = parse_args(argv)
|
||
csv_path = Path(args.csv)
|
||
if not csv_path.exists():
|
||
print(f"CSV 不存在：{csv_path}")
|
||
return 2
|
||
|
||
try:
|
||
model, steady, table, dropped, stats = identify(
|
||
csv_path, tail=args.tail, out_path=args.out
|
||
)
|
||
except (ValueError, OSError) as exc:
|
||
print(f"辨识失败：{exc}")
|
||
return 2
|
||
|
||
print(
|
||
f"读取稳态点 {len(steady)} 个；阻塞 {stats['choked']} 个，"
|
||
f"亚声速 {stats['subsonic']} 个，合计 {stats['total']} 个。"
|
||
)
|
||
print(
|
||
f"有效行程范围 {table[0][0]:.1f} ~ {table[-1][0]:.1f}"
|
||
f"（{len(table)} 点）。"
|
||
)
|
||
|
||
if dropped:
|
||
print(
|
||
"警告：A_eff(x) 非单调，以下点被丢弃"
|
||
"（建议缩小扫点范围到单调段重扫）。"
|
||
)
|
||
print("行程 x -> A_eff：")
|
||
for x, a in table:
|
||
print(f" x={x:8.3f} A_eff={a:.6f}")
|
||
if dropped:
|
||
print("被丢弃的点：")
|
||
for x, a in dropped:
|
||
print(f" x={x:8.3f} A_eff={a:.6f} [DROPPED]")
|
||
|
||
print(f"已保存阀特性模型：{args.out}")
|
||
|
||
if args.plot:
|
||
image_path = Path(args.out).with_suffix(".png")
|
||
try:
|
||
create_diagnostic_plot(steady, table, dropped, image_path)
|
||
print(f"诊断图已保存：{image_path}")
|
||
except Exception as exc:
|
||
print(f"生成诊断图失败（不影响 JSON）：{exc}")
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import sys
|
||
|
||
sys.exit(main())
|