Collaborative optimization design framework for hierarchical filter barrier control suspension system with projection adaptive tracking hydraulic actuator

Coupling characteristics of integrated mechanical-hydraulic-control systems for active hydro-suspension with uncertain and time-varying parameters make it difficult to achieve system-level optimal performances if only through physical or control system design. A novel collaborative design framework is proposed to optimize selected variables with objectives of structural lightweight, controllable suspension performances, and energy consumption. To improve ride/handling performances of active hydro-suspension under limited chatter space and allowable tire dynamic load, nonlinear filter barrier-Lyapunov-function-based backstepping upper controller is designed to generate target force under uncertain body weight, and projection-based adaptive backstepping sliding mode bottom controller is presented for valve current adjustment to drive asymmetric actuator precisely track required target force under time-varying fluid parameters. Based on designed hierarchical controller, physical/control collaborative design problem for system-level optimization is formulated by tailored optimal objective functions/constraints, independent and coupling design variables. The solution efficiency is improved through reduced calls of physical/control systems using response extreme difference sensitivity analysis, updated initial sets, and dynamic search interval for subsequent optimization. Finally, numerical simulation is presented to verify the effectiveness and benefits of the proposed collaborative optimization hierarchical control design method with eliminated conflicts between ride comfort and suspension deformation, improved control performances, better robustness, and lighter structure.

Automated summary

This study proposes a collaborative design framework for optimizing integrated mechanical-hydraulic-control systems for active hydro-suspension with uncertain and time-varying parameters. By designing upper and bottom controllers, optimal performance is achieved under changing conditions. Numerical simulations confirm the effectiveness and benefits of this approach.