For the past several decades there have been many attempts to improve suspension performance due to its importance within vehicle dynamics. The suspension system main functions are to connect the chassis to the ground, and to isolate the chassis from the ground. To improve upon these two functions, large amounts of effort are focused on two elements that form the building blocks of the suspension system, stiffness and damping. With the advent of new technologies, such as variable dampers, and powerful microprocessors and sensors, suspension performance can be enhanced beyond the traditional capabilities of a passive suspension system. Recently, Yin et al. [1, 2] have developed a novel dual chamber pneumatic spring that can provide tunable stiffness characteristics, which is rare compared to the sea of tunable dampers. The purpose of this thesis is to develop a controller to take advantage of the novel pneumatic spring’s functionality with a tunable damper to improve vehicle dynamic performance.
Since the pneumatic spring is a slow-acting element (i.e. low bandwidth), the typical control logic for semi-active suspension systems are not practical for this framework. Most semi-active controllers assume the use of fast-acting (i.e. high bandwidth) variable dampers within the suspension design. In this case, a lookup table controller is used to manage the stiffness and damping properties for a wide range of operating conditions.
To determine the optimum stiffness and damping properties, optimization is employed. Four objective functions are used to quantify vehicle performance; ride comfort, rattle space (i.e. suspension deflection), handling (i.e. tire deflection), and undamped sprung mass natural frequency. The goal is to minimize the first three objectives, while maximizing the latter to avoid motion sickness starting from 1Hz and downward. However, these goals cannot be attained simultaneously, necessitating compromises between them. Using the optimization strength of genetic algorithms, a Pareto optima set can be generated to determine the compromises between objective functions that have been normalized. Using a trade-off study, the stiffness and damping properties can be selected from the Pareto optima set for suitability within an operating condition of the control logic.
When implementing the lookup table controller, a practical method is employed to recognize the road profile as there is no direct method to determine road profile. To determine the road profile for the lookup table controller, the unsprung mass RMS acceleration and suspension state are utilized. To alleviate the inherent flip-flopping drawback of lookup table controllers, a temporal deadband is employed to eliminate the flip-flopping of the lookup table controller.
Results from the semi-active suspension with tunable stiffness and damping show that vehicle performance, depending on road roughness and vehicle speed, can improve up to 18% over passive suspension systems. Since the controller does not constantly adjust the damping properties, cost and reliability may increase over traditional semi-active suspension systems. The flip-flopping drawback of lookup table controllers has been reduced through the use of a temporal deadband, however further enhancement is required to eliminate flip-flopping within the control logic. Looking forward, the novel semi-active suspension has great potential to improve vehicle dynamic performance especially for heavy vehicles that have large sprung mass variation, but to increase robustness the following should be considered: better road profile recognition, the elimination of flip-flopping between suspension states, and using state equations model of the pneumatic spring within the vehicle model for optimization and evaluation.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/7200 |
| Date | January 2012 |
| Creators | Wong, Adrian Louis Kuo-Tian |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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