# Robust adaptive control with dung beetle optimization algorithm and disturbance observer for load displacement tracking of shock absorber damper test bench

## TL;DR

A new control method using a dung beetle optimization algorithm and disturbance observer improves shock absorber test bench performance.

## Contribution

A novel robust adaptive control strategy combining dung beetle optimization and disturbance observer for improved displacement tracking.

## Key findings

- The proposed control method reduced maximum displacement tracking error by 54.8% compared to unoptimized control.
- Response speed improved by 36.3% compared to unoptimized control and 47.7% compared to traditional PID control.
- Simulation and experimental results confirmed enhanced tracking accuracy and system robustness.

## Abstract

Shock absorbers are indispensable components utilized for vibration mitigation in various fields, including construction, bridges, wind power, and pipelines. The shock absorber test bench serves as an essential apparatus for evaluating the dynamic and static characteristics of these absorbers. To address the significant tracking errors between actual and expected displacements during displacement loading on shock absorber damper test benches, a novel control strategy is proposed. This strategy incorporates a robust adaptive controller (RAC) enhanced by a dung beetle optimization (DBO) algorithm and a disturbance observer (DO). The dung beetle optimization algorithm is specifically designed to iteratively optimize the control parameters of the robust adaptive controller. Concurrently, a disturbance observer is implemented to accurately estimate external disturbances and perform feedforward compensation. A mathematical model of the electro-hydraulic servo control system of the test bench is established, and the stability of the proposed controller is rigorously verified using Lyapunov theory. To simulate and analyze the control method for the electro-hydraulic servo system of the test bench, a joint simulation model integrating Simulink and AEMSim is constructed. The performance of the proposed robust adaptive controller with DBO and DO is compared against an unoptimized robust adaptive controller and a traditional PID controller in terms of load displacement tracking. Simulation results demonstrate that the control method proposed in this study significantly outperforms other controllers in enhancing position tracking accuracy and improving system robustness. Furthermore, experimental verification was carried out on the proposed control strategy, compared with unoptimized robust adaptive control, the maximum displacement tracking error was reduced by 54.8%, and the response speed was improved by 36.3%, compared with traditional PID control, the minimum displacement tracking error was reduced by 67.4%, and the response speed was improved by 47.7%. The results confirmed the superior performance of the proposed control method.