공압 스프링을 사용한 능동 서스펜션에 대한 적응 제어 접근 방식의 설계
- This thesis studies some advanced controllers for the pneumatic active suspension with the vertical displacement constraint of sprung mass, parametric uncertainties, and actuator and sensor failures based on combinations of nonlinear and intelligent approaches. A quarter car model with a pneumatic spring is first fabricated on the basis of thermodynamics to describe the dynamic characteristics. To overcome the lumped unknown nonlinearities and enhance the requirement of modeling precision, the radial basis function neural networks (RBFNNs) and fuzzy logic systems (FLSs) are proposed to approximate unknown continuous functions caused by the uncertain body mass and other factors of pneumatic spring. Then, a neural/fuzzy state observer is constructed to estimate unmeasured states and compensate for the partial loss of effectiveness of sensor failure simultaneously. Besides, by utilizing the disturbance estimation and fuzzy approximation techniques, an adaptive fault-tolerant control (FTC) is designed to enhance the output performance of the vehicle suspension under the presence of actuator failures. Furthermore, an adaptive fault tolerant-based compensation observer is proposed to approximate unmeasured states and address the unknown sensor failure. To solve the explosion of complexity problem in the traditional backstepping designs, a proposed command filter control is applied by using the Levant differentiators which approach the derivative of the virtual control signals. Nussbaum gain technique is then incorporated into the controller to avoid the problem of the completely unknown control gain and control directions of a pneumatic actuator. In addition, to improve the tracking accuracy and get the ride comfort, this study is concerned with a prescribed performance function so that the vertical displacement of sprung mass is not violated the predefined boundary. Based on the command filtered backstepping control with the prescribed performance technique, the Lyapunov theorem is then applied to indicate the system stability analysis. Finally, comparative simulation examples and experiments for the pneumatic suspension are given to verify the effectiveness and reliability of the proposed controllers.
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- Active suspension systems; pneumatic suspension; adaptive control; prescribed performance function; neural networks; fuzzy logic systems; command filtered control
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