Cascaded MRAC-Feedforward Composite Control Strategy for Negative Pressure Stabilization of an Electric-Driven Seeding Fan

Bus-voltage fluctuations of unstabilized PTO-driven generators, together with pneumatic load variations, cause negative-pressure instability in electric-driven air-suction seeding fans, degrading seeding quality. This paper proposes a cascaded composite control strategy combining model reference adaptive control (MRAC) with voltage feedforward. Based on time-scale separation, the inner loop uses a PI controller with voltage feedforward for millisecond-level compensation of bus-voltage disturbances, while the outer loop employs output-feedback MRAC against time-varying chamber dynamics. The strategy was compared with PID, fuzzy PID, and cascaded MRAC without feedforward (CMRAC-NFF) through nonlinear simulations, bench tests, and cotton field trials. In nonlinear simulations, a 72-to-65 V voltage step induced a maximum negative-pressure deviation of −0.48 kPa with a 1.08 s recovery time; under combined voltage and seed-suction load disturbances in the simulation environment, the error standard deviation was 0.07 kPa. In bench tests, the mean absolute steady-state deviation remained within 0.12–0.16 kPa across the 7.0–10.0 kPa setpoints. In field tests, the proportion of time outside the ±0.3 kPa tolerance was reduced to 3.84%, with a qualified seeding rate of 96.24%. With feedforward rejecting supply disturbances and MRAC absorbing model uncertainty, the scheme sustained agronomic-grade pressure stability on a 12-row cotton seeder across tested 7.0–10.0 kPa setpoints.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2026-10-22
DOI
https://doi.org/10.3390/agriculture16212302
Primary Topic
Agricultural Engineering and Mechanization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cascaded MRAC-Feedforward Composite Control Strategy for Negative Pressure Stabilization of an Electric-Driven Seeding Fan

Caiyun Lu, Panpan Yuan, Qingchun Feng, Baiwei Wang et al.
Agriculture
Agricultural Engineering and Mechanization
article

Cascaded MRAC-Feedforward Composite Control Strategy for Negative Pressure Stabilization of an Electric-Driven Seeding Fan

Caiyun Lu, Panpan Yuan, Qingchun Feng, Baiwei Wang, Yuze He, Jincheng Chen, Chao Ji
article en

Abstract

Bus-voltage fluctuations of unstabilized PTO-driven generators, together with pneumatic load variations, cause negative-pressure instability in electric-driven air-suction seeding fans, degrading seeding quality. This paper proposes a cascaded composite control strategy combining model reference adaptive control (MRAC) with voltage feedforward. Based on time-scale separation, the inner loop uses a PI controller with voltage feedforward for millisecond-level compensation of bus-voltage disturbances, while the outer loop employs output-feedback MRAC against time-varying chamber dynamics. The strategy was compared with PID, fuzzy PID, and cascaded MRAC without feedforward (CMRAC-NFF) through nonlinear simulations, bench tests, and cotton field trials. In nonlinear simulations, a 72-to-65 V voltage step induced a maximum negative-pressure deviation of −0.48 kPa with a 1.08 s recovery time; under combined voltage and seed-suction load disturbances in the simulation environment, the error standard deviation was 0.07 kPa. In bench tests, the mean absolute steady-state deviation remained within 0.12–0.16 kPa across the 7.0–10.0 kPa setpoints. In field tests, the proportion of time outside the ±0.3 kPa tolerance was reduced to 3.84%, with a qualified seeding rate of 96.24%. With feedforward rejecting supply disturbances and MRAC absorbing model uncertainty, the scheme sustained agronomic-grade pressure stability on a 12-row cotton seeder across tested 7.0–10.0 kPa setpoints.

AgricultureVol. 16(21)
Xinjiang Agricultural University (CN), Xinjiang Academy of Agricultural and Reclamation Science (CN), Beijing Academy of Agricultural and Forestry Sciences (CN), Ministry of Agriculture and Rural Affairs (CN), China Agricultural University (CN)
Openalex Percentile: Top 22%
Agricultural Engineering and Mechanization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon