Outline
- Abstract
- Keywords
- References
رئوس مطالب
- چکیده
- کلیدواژه ها
- 1. مقدمه
- 2. طراحی FSMC مبتنی بر مشاهده گر
- 3. FSMC انطباقی غیر مستقیم مبتنی بر مشاهده گر با فیلترهای متغیر حالت
- 4. نمونه های شبیه سازی
- 5. نتیجه گیری ها
Abstract
This paper proposes an observer-based indirect adaptive fuzzy sliding mode controller with state variable filters for a certain class of unknown nonlinear dynamic systems in which not all the states are available for measurement. To design the proposed controller, we first construct the fuzzy models to describe the input/output behavior of the nonlinear dynamic system. Then, an observer is employed to estimate the tracking error vector. Based on the observer, a fuzzy sliding model controller is developed to achieve the tracking performance. Then, a filtered observation error vector is obtained by passing the observation error vector to a set of state variable filters. Finally, on the basis of the filtered observation error vector, the adaptive laws are proposed to adjust the free parameters of the fuzzy models. The stability of the overall control system is analyzed based on the Lyapunov method. Simulation results illustrate the design procedures and demonstrate the tracking performance of the proposed controller.
Keywords: Fuzzy control - Fuzzy sliding mode control - State variable filtersConclusions
In this paper, we have proposed a method for designing an observer-based indirect adaptive fuzzy sliding mode controller with state variable filters for the control of a certain class of unknown nonlinear dynamic systems, in which not all the states are available for measurement. We first construct the fuzzy models to describe the input/output behavior of the nonlinear dynamic system. Then, an observer is employed to estimate the tracking error vector. Based on the observer, a fuzzy sliding model controller is developed to achieve the tracking performance. By passing the observation error vector to a set of state variable filters, a filtered observation error vector is obtained. The free parameters of the fuzzy models can be adjusted by the adaptive laws, based on the filtered observation error vector and the Lyapunov synthesis method. With the proposed control strategy, the stability of the overall control system can be guaranteed. The simulation results show that the proposed control strategy can turn in a good tracking performance and is robust against the external noise.