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Active Chassis Control Engineering with help from ITN/PD at DaimlerChrysler, has been developing a hardware-in-the-loop (HIL) system, for the evaluation and optimization of interaction among the various chassis control systems, and between the chassis control system and the vehicle. This HIL system consists of a transfer case, a hydraulic load box, two differential load boxes, an electronic control unit (ECU), a virtual 14-DOF (Degree of Freedom) vehicle model, and a computer system with a high-speed operating platform. The HIL system simultaneously demonstrates the advantages of both hardware testing and computer modeling. Different from conventional bench tester, the system offers the capability to implement dynamic ECU tests with feedback requirements for the sophisticated control algorithms, CAN diagnostics, fault injection, automatic-operating scenarios duplication and validation.

An articulated vehicle suspension comprising a parallel combination of passive energy restoring and dissipative elements and a feedback controlled force generator is analyzed using H2 control synthesis. The active suspension schemes based on limited-state measurements are formulated to minimize a performance measure comprising ride quality, cargo safety, suspension and tire dynamic deflections, and power requirements. The ride quality and the dynamic wheel load performance characteristics of these suspension schemes are compared to those of a vehicle with an ideal active suspension and an “optimum” passive suspension to demonstrate the performance potentials of the proposed limited-measurement-based suspension schemes.

A covariance analysis technique is proposed to derive the optimal suspension parameters of an articulated freight vehicle. A performance criteria comprising vehicle ride response, suspension deflections and tire deflections related to dynamic wheel loads, is formulated for the 9 degrees-of-freedom (DOF) in-plane model of the vehicle. The range of suspension parameters to achieve four different design requirements is identified and a parametric study is performed to make initial parameter selection using the covariance analysis. The optimal suspension parameters are then identified from the results of the study. The study concludes that the proposed technique can yield the optimal solution in a convenient and highly efficient manner.