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Event-triggered control for nonlinear leaf spring hydraulic actuator suspension system with valve predictive management

Journal Article


Abstract


  • A new control strategy with networked event-triggered force domain controller and valve opening predictive management local controller is proposed for nonlinear hysteretic leaf spring suspension system with asymmetric cylinder hydraulic actuator over actuated by multiple servo-valves. Takagi–Sugeno fuzzy approach is employed to describe this uncertainty system with experimentally evaluated nonlinear hysteretic leaf spring under consideration of asynchronous premises and transmission delays in a unified framework. For the derived nonlinear network control system, event-triggered domain controller is proposed to generate target force with linear matrix inequality approach based on Lyapunov asymptotically stability theory. The target force is tracked by hydraulic actuator with over actuated multiple servo-valves. These servo-valve openings are collaboratively regulated by predictive management local controller via solving constrained optimal nonlinear control problem. Finally, numerical simulation results are provided to verify the effectiveness and benefits of the proposed control strategy. Compared to passive and time-triggered sampled data control, the obtained results indicate that the sprung mass acceleration and suspension working space are greatly improved without sacrificing tire dynamic load. The valve opening predictive management for over actuated hydraulic actuator is successfully achieved, and simultaneously both the communication resource and actuator power consumption are significantly saved with adaptive event-triggered control strategy.

Publication Date


  • 2021

Citation


  • Ding, F., Li, Q., Jiang, C., Han, X., Liu, J., Du, H., & Lei, F. (2021). Event-triggered control for nonlinear leaf spring hydraulic actuator suspension system with valve predictive management. Information Sciences, 551, 184-204. doi:10.1016/j.ins.2020.11.036

Scopus Eid


  • 2-s2.0-85098463344

Web Of Science Accession Number


Start Page


  • 184

End Page


  • 204

Volume


  • 551

Abstract


  • A new control strategy with networked event-triggered force domain controller and valve opening predictive management local controller is proposed for nonlinear hysteretic leaf spring suspension system with asymmetric cylinder hydraulic actuator over actuated by multiple servo-valves. Takagi–Sugeno fuzzy approach is employed to describe this uncertainty system with experimentally evaluated nonlinear hysteretic leaf spring under consideration of asynchronous premises and transmission delays in a unified framework. For the derived nonlinear network control system, event-triggered domain controller is proposed to generate target force with linear matrix inequality approach based on Lyapunov asymptotically stability theory. The target force is tracked by hydraulic actuator with over actuated multiple servo-valves. These servo-valve openings are collaboratively regulated by predictive management local controller via solving constrained optimal nonlinear control problem. Finally, numerical simulation results are provided to verify the effectiveness and benefits of the proposed control strategy. Compared to passive and time-triggered sampled data control, the obtained results indicate that the sprung mass acceleration and suspension working space are greatly improved without sacrificing tire dynamic load. The valve opening predictive management for over actuated hydraulic actuator is successfully achieved, and simultaneously both the communication resource and actuator power consumption are significantly saved with adaptive event-triggered control strategy.

Publication Date


  • 2021

Citation


  • Ding, F., Li, Q., Jiang, C., Han, X., Liu, J., Du, H., & Lei, F. (2021). Event-triggered control for nonlinear leaf spring hydraulic actuator suspension system with valve predictive management. Information Sciences, 551, 184-204. doi:10.1016/j.ins.2020.11.036

Scopus Eid


  • 2-s2.0-85098463344

Web Of Science Accession Number


Start Page


  • 184

End Page


  • 204

Volume


  • 551