【作者】 于树友；

【导师】 陈虹；

【作者基本信息】 吉林大学 ， 控制理论与控制工程， 2005， 硕士

【摘要】 本文以LMI 为优化工具,融合预测控制的思想,主要讨论了滚动优化的约束H∞性能控制方法在汽车悬架系统中的应用。主动悬架控制问题实际上是一个存在输出约束和控制约束的干扰抑制问题。传统的H∞控制方法一般没有考虑系统中存在的约束。约束系统的H∞控制,把系统约束转化为优化参数的约束,把H∞性能指标当作优化目标,解带约束的LMI 优化问题。当扰动满足一定的条件下,能够满足系统约束;但当大扰动作用于系统,却常常导致系统约束的违背。滚动优化的H∞控制利用预测控制的滚动优化思想,把当前时刻系统的状态当作系统的初态,在每个优化时刻都求解带约束的LMI 优化问题。一方面当大扰动作用于系统时,使系统的约束依然得到满足;另一方面充分协调了满足约束和提高性能的矛盾,在一定程度上避免了系统设计的保守性。本文第四章提出了滚动优化的约束H∞控制,第五章提出了滚动优化的约束广义H 2控制,以此为基础进行了主动悬架设计,并针对确定性路面输入进行了系统仿真。更多还原

【Abstract】 Suspension is the main part of the vehicle. it lies between body and wheels. Usually, the suspension is composed of the spring and damper elements. It connects flexibly body with axle, affords force between wheel and body, retards impulsion load from uneven road to body, and attenuates vehicle vibration arising from all kinds of shift load. Suspension suppresses vibration of the wheels so as to maintain firm and uninterrupted contact between the wheels and the road, improve the handling stability. Performance requirements for advanced vehicle suspensions include isolating passengers from vibration and shock arising from road roughness (ride comfort), suppressing the hop of the wheels so as to maintain firm, uninterrupted contact of wheels to road (good handling or good road handling) and keeping suspension strokes within an allowable maximum. These requirements are conflicting, for example, increasing ride comfort results in larger suspension stroke and smaller damping in the wheeling-hop mode. In order to ensure a firm uninterrupted contact of wheels to road, the dynamic tire load cannot exceed the static ones at any time. Since suspensions are placed between the chassis and wheel assembly, structural features of a vehicle impose a hard limit on the suspension stroke. An excessive suspension bottoming can lead to considerable deterioration of ride comfort and possible structural damage. Having a close insight into the control requirements for active suspensions, it is clear that requirements on good road holding, limiting suspension strokes and control inputs within bounds are in fact hard constraints in time domain. Hence, it is much nature to formulate the active suspension control problem as a disturbance attenuation problem with hard constraints. Here, we adopt H∞norm of the closed-loop system as the criterion of optimization. Traditional H∞control does not consider the constraints of system although the constraints of inputs and state exit in a common control system. Under the base of H∞control, H∞control of constrained system is formed. It solves the problem of constraints by transforming the constraints of the input or state into the constraints of optimized parameters, taking the performance of H∞as optimized objection and resolving the optimization problem of constrained system. The demerit of H∞control for constrained system is that the performance is fixed in the control process, which means the performance can’t be adjusted according to the state of controlled system. For example, in order to get over the larger disturbance, we have to use the control scheme with bad performance, because the control scheme with good performance is likely to break the constraints of system. When the large disturbance pasts, the performance will still keep bad, which is not what we hope. In order to improve the control performance and take full advantage of the state x (t ) , the moving horizon H∞is formed. Taking the moving horizon scheme, it takes x (t ) as initial state, and solves the LMI optimization problem with constraints at each sampling time. By dong this, the moving horizon scheme can automatically manage the trade-off between achieving high performance and satisfying constraints. We can see the performance of the moving horizon H∞control is better. It introduced in the text two kinds of methods that describe the constraints:更多还原