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Adaptive robust force control for vehicle active suspensions.

机译:车辆主动悬架的自适应鲁棒力控制。

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The objective of this dissertation is to develop high-performance force controllers for vehicle active suspensions. Since the majority of active suspension controllers in the literature did not include actuator dynamics, few of them have been verified to perform well experimentally. To provide accurate force generation, Adaptive Robust Control techniques (ARC) are adopted in this dissertation because they combine the advantages of deterministic robust control techniques and adaptive control techniques while avoiding many of their drawbacks. We also introduced a few enhancements to further improve the practicality and performance of the ARC algorithm. We first design a force controller by using existing ARC techniques. Secondly, an output feedback ARC is developed to make the resulting force controller easier to implement. An online adaptation algorithm is then proposed to improve performance and robustness of the controller. Finally, a modular technique to improve the controller's identification process is developed.; The importance of these enhancements is as follows. The output feedback extension is important for reducing sensor cost. The online adaptation algorithm improves system performance during normal operations while helping to stabilize the system under unmodeled uncertainties. The Modular ARC technique, which is the most important theoretical contribution of this work, improves the performance of the overall control system by allowing the identification algorithms to be designed with an emphasis on identification accuracy rather than on output tracking. Generalized procedures for designing an ARC controller with online adaptation of its performance parameters and a Modular ARC controller for systems in the parametric strict-feedback form are also provided.; The performance of the proposed ARC force control algorithms is verified experimentally in the University of Michigan Vehicle Dynamics Laboratory. Experimental results from a quarter-car test rig confirm that these force controllers work satisfactorily, either stand-alone or when they are integrated with LQ or LQG outer-loop control laws.
机译:本文的目的是开发用于车辆主动悬架的高性能力控制器。由于文献中的大多数主动悬架控制器均未包含执行器动力学特性,因此很少有人通过实验证明其性能良好。为了提供精确的力生成,本文采用自适应鲁棒控制技术(ARC),因为它们结合了确定性鲁棒控制技术和自适应控制技术的优点,同时避免了许多缺点。我们还引入了一些增强功能,以进一步提高ARC算法的实用性和性能。我们首先使用现有的ARC技术设计一个力控制器。其次,开发了输出反馈ARC,以使最终的力控制器更易于实施。然后提出了一种在线自适应算法来提高控制器的性能和鲁棒性。最后,开发了一种改进控制器识别过程的模块化技术。这些增强的重要性如下。输出反馈扩展对于降低传感器成本很重要。在线自适应算法提高了正常运行期间的系统性能,同时有助于在未建模的不确定性下稳定系统。模块化ARC技术是这项工作最重要的理论贡献,它通过允许识别算法的设计重点在于识别精度而不是输出跟踪,从而提高了整个控制系统的性能。还提供了用于设计具有在线性能参数的ARC控制器和用于参数严格反馈形式的系统的模块化ARC控制器的通用过程。所提出的ARC力控制算法的性能已在密歇根大学车辆动力学实验室进行了实验验证。四分之一车试验台的实验结果证实,这些力控制器可以独立运行,也可以与LQ或LQG外环控制定律集成在一起,令人满意地工作。

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