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Related Experiment Video

Updated: Jan 28, 2026

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Parameters identification and trajectory control for a hydraulic system.

Hao Feng1, Chenbo Yin1, Wei Ma1

  • 1United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China; Institute of Automobile and Construction Machinery, Nanjing Tech University, Nanjing 211816, China.

ISA Transactions
|March 5, 2019
PubMed
Summary
This summary is machine-generated.

An improved ant colony optimization algorithm (IACO) enhances PID controller accuracy for hydraulic systems. This method significantly boosts trajectory precision in robotic excavator leveling operations by 28%.

Keywords:
Ant colony optimization algorithmPIDParameters identificationRecursive least square methodRobotic excavatorTrajectory control

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Hydraulic systems require precise control for optimal performance.
  • Existing Proportional-Integral-Derivative (PID) controllers often face challenges in achieving high tracking accuracy.
  • Ant Colony Optimization (ACO) offers a potential solution but requires enhancements for complex systems.

Purpose of the Study:

  • To develop an Improved Ant Colony Optimization (IACO) algorithm for optimizing PID controller parameters in hydraulic systems.
  • To enhance the tracking accuracy and operational performance of electro-hydraulic proportional control systems.
  • To validate the proposed IACO-PID controller through co-simulation and experimental studies on a robotic excavator.

Main Methods:

  • Mathematical modeling and analysis of electro-hydraulic proportional control systems.
  • Recursive Least Square (RLS) identification for system parameter estimation.
  • Development of an enhanced ACO algorithm (IACO) with improved convergence and reduced premature convergence.
  • MATLAB/Simulink and AMESim co-simulation for modeling and performance evaluation.
  • Experimental validation on a 23-ton robotic excavator performing leveling operations.

Main Results:

  • The IACO algorithm demonstrated improved convergence speed and avoided premature convergence compared to standard ACO.
  • IACO-tuned PID controllers showed enhanced settling time and rise time in simulations.
  • Co-simulation results indicated superior performance of IACO-PID over standard ACO-PID and Ziegler-Nichols PID.
  • Experimental results confirmed a 28% improvement in trajectory accuracy for leveling operations using IACO-PID.

Conclusions:

  • The proposed IACO algorithm effectively optimizes PID controllers for hydraulic systems, leading to significant improvements in tracking accuracy.
  • IACO provides a robust and efficient method for parameter identification and controller tuning in complex electro-hydraulic systems.
  • The enhanced controller shows practical applicability and superior performance in real-world robotic applications like excavator leveling.