Adaptive Control Strategies for Hydraulic Systems

Hydraulic systems underpin a vast array of modern machinery, from heavy-duty construction excavators to precision industrial robots and marine stabilisation platforms. Their intrinsic nonlinearity, frictional losses and time-varying load conditions pose significant challenges for conventional fixed-gain controllers. Adaptive control strategies seek to address these challenges by adjusting controller parameters in real time, compensating for uncertain dynamics and external disturbances. Contemporary approaches integrate robust control methods such as sliding mode or backstepping with online parameter estimation, often using neural network approximators to model unmeasured dynamics. Disturbance observers further enhance performance by estimating and cancelling unknown forces, while fault-tolerant schemes detect internal leakage or sensor failures and reconfigure control laws. These adaptive architectures deliver high-precision position and force tracking, improve energy efficiency through optimised metering, and bolster reliability in safety-critical applications. Their global significance extends across manufacturing, robotics, aerospace and offshore engineering, where resilient, self-tuning hydraulic actuation is vital for productivity, sustainability and operational safety.

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Adaptive Control Strategies for Hydraulic Systems publication trend

The graph below shows the total number of articles in adaptive control strategies for hydraulic systems across all publications each year (not limited to Nature Index journals).

Technical terms

Adaptive control: A control approach that adjusts its parameters online to accommodate system variations and uncertainties.

Sliding mode control: A robust technique enforcing system trajectories to a designed switching surface to reject disturbances.

Backstepping: A recursive design method for stabilising nonlinear plants by systematic coordinate transformations.

Disturbance observer: An algorithm estimating unknown external forces or model errors for compensation in the control loop.

Neural network approximator: A machine-learning model used online to capture unmodelled dynamics or nonlinearities.

References

1. Active Disturbance Rejection Control for Position Tracking of Electro-Hydraulic Servo Systems under Modeling Uncertainty and External Load. Actuators (2021).
2. Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer. IEEE Access (2021).
3. Active Fault Tolerant Control System Design for Hydraulic Manipulator With Internal Leakage Faults Based on Disturbance Observer and Online Adaptive Identification. IEEE Access (2021).