Journal of Engineering and Applied Science· 2026Q2
Nonlinear characteristic analysis and MPC-based position control of a valve-controlled electro-hydraulic cylinder
- 0citations
- Q2SCImago
- 2026year
Short summary
A Model Predictive Control (MPC) strategy significantly reduces overshoot and steady-state error in valve-controlled electro-hydraulic cylinders compared to PID, achieving 0.04 mm overshoot and 0.10-0.20 mm steady-state error for 10-20 mm steps.
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Key points
- An integrated electro-hydraulic position-servo model was established, incorporating valve dead zone, hysteresis, oil compressibility, and LuGre friction.
- A discrete MPC controller, using a linear transfer-function prediction model, was implemented and compared against a PID controller.
- MPC reduced step-response overshoot from 0.22-0.25 mm to 0.04 mm and steady-state error from 0.25-0.35 mm to 0.10-0.20 mm for 10-20 mm commands.
- MPC improved sinusoidal tracking by reducing amplitude attenuation (e.g., from 0.55 mm to 0.15 mm at 6.28 rad/s) and phase lag (e.g., from 22.3 degrees to 10.1 degrees).
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract This paper investigates how nonlinear hydraulic elasticity and LuGre-type friction affect the position tracking of a valve-controlled electro-hydraulic cylinder. An integrated electro-hydraulic position-servo model is established, including the power amplifier, servo valve, hydraulic actuator, displacement sensor, and feedback loop. Valve dead zone and hysteresis are treated as input-side nonlinearities, whereas the analysis focuses on cylinder-side oil compressibility and friction. The effects of bulk modulus, the nominal air-content parameter, Coulomb friction, viscous damping, and bristle stiffness are examined through MATLAB/Simulink-AMESIM co-simulation. A discrete MPC controller is then implemented in the MATLAB/Simulink MPC Toolbox using a transfer-function prediction model derived from the reduced-order electro-hydraulic position model. The internal prediction model is linear; hydraulic elasticity and LuGre friction remain in the nonlinear plant model and are not implemented as separate real-time feedforward compensation channels. The controller uses a sampling time of 0.02 s, a prediction horizon of 20 steps, a control horizon of 5 steps, and an output-related tuning weight of 0.06. Hardware experiments show that, compared with the tested proportional-integral-derivative (PID) controller, MPC reduces step-response overshoot from 0.22 mm to 0.04 mm for a 10 mm command and from 0.25 mm to 0.04 mm for a 20 mm command; the corresponding steady-state errors decrease from 0.35 mm to 0.20 mm and from 0.25 mm to 0.10 mm. For sinusoidal tracking at 3.14 rad/s and 6.28 rad/s, MPC reduces amplitude attenuation from 0.30 mm to 0.10 mm and from 0.55 mm to 0.15 mm, and phase lag from 18.0 degrees to 6.5 degrees and from 22.3 degrees to 10.1 degrees, respectively. The results demonstrate improved transient positioning and periodic tracking relative to PID under the investigated nonlinear plant conditions.
The authors' abstract, as published at the source. Journal of Engineering and Applied Science, 2026 · DOI ↗
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Field: Mechanical Engineering
Mechanical EngineeringEngineering