CLADTECH-LOGOCLADDING TECHNOLOGY SHANXI CO., LTD
CLADDING TECHNOLOGY SHANXI CO., LTD
CLADDING · BIMETAL PRODUCT · BIMETAL PRESSURE VESSEL TECHNICAL STUDY

Pressure-Speed Composite Control Strategy for Hydraulic Systems

Literature Overview and Control Theory Foundation

This study addresses a fundamental challenge in hydraulic system design: achieving simultaneous control of both pressure and velocity in systems where these two variables are inherently coupled through the dynamics of fluid compressibility, valve flow characteristics, and actuator inertia. The author proposes a composite control strategy that decouples the pressure and velocity control loops while maintaining system stability under varying load conditions. This is particularly relevant for applications involving precision motion control, such as machining centers, press machines, and hydraulic pumps operating under variable demand conditions.

Control Architecture and Signal Processing

The composite control strategy employs a hierarchical architecture with the following components:

Control Layer Function Implementation Method
Outer Loop Pressure regulation Proportional-integral (PI) controller with load feedforward
Inner Loop Velocity regulation Sliding mode control or adaptive PID
Coupling Compensation Decouple pressure-velocity interaction Feedforward compensation based on system model
Disturbance Rejection Handle external load fluctuations Kalman filter or observer-based estimation

The pressure loop operates at a slower bandwidth (typically 5–20 Hz) to regulate the system supply pressure in response to load demand. The velocity loop operates at a higher bandwidth (50–200 Hz) to achieve precise motion tracking. The key innovation described in the literature is the coupling compensation term, which estimates the interaction between pressure transients and velocity deviations using a dynamic model of the hydraulic circuit.

Performance Verification and Stability Analysis

The study validates the control strategy through both simulation and experimental testing. Key performance metrics include:

Stability analysis is performed using both time-domain simulation and frequency-domain methods (Bode plot analysis, Nyquist criterion). The composite control strategy maintains adequate gain and phase margins across the operating envelope, confirming that the decoupling approach does not introduce instability at the cost of improved performance.

Engineering Implications and Practical Considerations

For engineers implementing this control strategy in real hydraulic systems, several practical considerations arise. The sensor requirements are demanding: pressure transducers must have response times below 5 ms, and velocity sensors (or position encoders with differentiation) must provide accurate signals at frequencies up to 200 Hz. The control algorithm requires computational resources, making it suitable for systems equipped with programmable logic controllers (PLCs) or dedicated digital signal processors (DSPs) rather than purely analog control hardware.

The study also notes that the performance of the composite control strategy depends on the accuracy of the system model used for coupling compensation. In practice, model parameters such as fluid bulk modulus, valve flow coefficients, and actuator friction characteristics may vary with temperature, contamination level, and wear. Adaptive identification techniques or periodic parameter updating are recommended to maintain control performance over the system lifetime.

Summary

This literature presents a well-conceived and thoroughly validated composite control strategy for hydraulic systems requiring simultaneous pressure and velocity regulation. The hierarchical control architecture with explicit coupling compensation represents a significant advancement over conventional single-loop or cascade control approaches. Engineers working with precision hydraulic systems should consider this strategy for applications where both pressure stability and motion accuracy are critical performance requirements, while paying careful attention to sensor quality, computational resources, and model parameter maintenance.