Dynamic response force control of electrohydraulic servo actuator of active suspension based on intelligent optimization algorithm
Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strate...
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| Vydáno v: | PloS one Ročník 20; číslo 6; s. e0323066 |
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10.06.2025
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| Abstract | Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7 % and 70.3 % , respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body’s vertical acceleration and pitch angle acceleration are reduced by 51.7 % and 13.1 % , respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers. |
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| AbstractList | Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7% and 70.3%, respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body’s vertical acceleration and pitch angle acceleration are reduced by 51.7% and 13.1%, respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers. Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7[Formula: see text] and 70.3[Formula: see text], respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body's vertical acceleration and pitch angle acceleration are reduced by 51.7[Formula: see text] and 13.1[Formula: see text], respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers.Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7[Formula: see text] and 70.3[Formula: see text], respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body's vertical acceleration and pitch angle acceleration are reduced by 51.7[Formula: see text] and 13.1[Formula: see text], respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers. Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7[Formula: see text] and 70.3[Formula: see text], respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body's vertical acceleration and pitch angle acceleration are reduced by 51.7[Formula: see text] and 13.1[Formula: see text], respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers. |
| Audience | Academic |
| Author | Wang, Mengchao Wang, Shenghuai Liu, Renjun Guo, Qinghe Wang, Hongxia Chen, Yurong |
| AuthorAffiliation | 1 School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, Hubei, China 2 Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan, Hubei, China Anurag University, INDIA 3 School of Art and Design, Hubei University of Automotive Technology, Shiyan, Hubei, China |
| AuthorAffiliation_xml | – name: Anurag University, INDIA – name: 2 Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan, Hubei, China – name: 1 School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, Hubei, China – name: 3 School of Art and Design, Hubei University of Automotive Technology, Shiyan, Hubei, China |
| Author_xml | – sequence: 1 givenname: Qinghe surname: Guo fullname: Guo, Qinghe – sequence: 2 givenname: Mengchao surname: Wang fullname: Wang, Mengchao – sequence: 3 givenname: Renjun orcidid: 0009-0005-7346-7861 surname: Liu fullname: Liu, Renjun – sequence: 4 givenname: Yurong surname: Chen fullname: Chen, Yurong – sequence: 5 givenname: Shenghuai surname: Wang fullname: Wang, Shenghuai – sequence: 6 givenname: Hongxia surname: Wang fullname: Wang, Hongxia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40493556$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Accuracy Actuators Algorithms Animals Biology and Life Sciences Computer and Information Sciences Computer Simulation Controllers Design Dynamic response Earth Sciences Engineering and Technology Exploitation Genetic algorithms Humans Hydraulic actuators Magnetic levitation systems Mathematical optimization Mechanical properties Models, Theoretical Nonlinear systems Optimization Optimization algorithms Parameter uncertainty Passenger comfort Physical Sciences Pitch (inclination) Proportional integral derivative Research and Analysis Methods Root-mean-square errors Servocontrol Systems stability Whales & whaling |
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| Title | Dynamic response force control of electrohydraulic servo actuator of active suspension based on intelligent optimization algorithm |
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