透過您的圖書館登入
IP:3.145.46.18
  • 學位論文

未知非線性系統之混合適應小腦模型滑動模式控制器設計

HYBRID ADAPTIVE CMAC SLIDING MODE CONTROLLER DESIGN FOR UNKNOWN NONLINEAR SYSTEM

指導教授 : 呂虹慶
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


摘要 在本篇論文中,我們針對不確定非線性系統提出一個新的混合適應小腦模型滑動模式控制器(HACSMC),此控制器是利用小腦模型控制器(CMAC)所設計的直接和間接適應控制器來模擬滑動模式控制中的等效控制力,並且利用一權重因子將直接適應小腦模型控制器和間接適應小腦模型控制器的控制力作結合。在本篇論文中,我們提出兩種方法來設計滑動模式控制中的切換控制力:符號函數切換控制器以及CMAC切換控制器。在符號函數切換控制器中,我們使用一估測法則估測系統不確定性的上界,並結合符號函數來設計切換控制器。在CMAC切換控制器中,我們直接利用CMAC網路來近似切換控制器。此外,監督控制器的設計是依附在HACSMC中用來保證系統的狀態能維持在邊界層裡。假如適應小腦模型控制器能有良好的響應,則監督控制器不會動作;反之,監督控制器將啟動使系統狀態拉回至邊界層裡。控制系統的適應法則皆由李亞普諾夫(Lyapunov)定理推導得知,並且保證控制系統的穩定性。最後所提出的控制器將運用在倒單擺系統與蔡氏混沌電路中。經過模擬與比較,結果顯示,所提出的混合適應小腦模型滑動模式控制器不僅使控制系統具有優異的追蹤性能與強健性並且設計過程更有彈性。

並列摘要


ABSTRACT In this thesis, a new hybrid adaptive cerebeller model articulation controller (CMAC) sliding mode control system is developed for a class of unknown nonlinear systems. The hybrid adaptive CMAC sliding mode controller (HACSMC) uses the direct and indirect adaptive CMAC controllers to perform the equivalent control of sliding mode control (SMC). A weighting factor is adopted to sum together the control efforts from the direct and indirect adaptive CMAC controller. Two types of methods, sign function switching controller and CMAC switching controller are proposed to design the switching control law of SMC. In sign function switching controller, we use an estimation law to estimate the upper bound of uncertainty, and combine with sign function to design the switching control law of SMC. In CMAC switching controller, a CMAC network is employed to perform the switching control law of SMC. Furthermore, a supervisory controller is appended to the HACSMC to guarantee the states staying in the boundary layer. Therefore, if HACSMC can maintain the states within the boundary layer, supervisory controller will be idle. Otherwise, the supervisory controller starts working to pull the states back to the boundary layer. In addition, the adaptive laws of the control system are derived in the sense of Lyapunov theorem, so that the stability of the system can be guaranteed. Finally, the proposed control system is applied to inverted pendulum system and Chua’s chaotic circuit. The simulation results show that the HACSMC can not only make control system have good tracking performance and strong robustness but also have more flexibility during the design process.

參考文獻


[1] B. S. Chen, C. H. Lee, and Y. C. Chang, ” tracking design of uncertain nonlinear SISO systems: Adaptive fuzzy approach,” IEEE Trans. Fuzzy Syst., vol.4, pp. 32-43, Feb. 1996.
[2] G.. A. Rovithakis and M. A. Christodoulou, “Adaptive control of unknown plants using dynamical neural networks,” IEEE Trans. Syst., Man, Cybern., vol.24, pp. 400-412, Mar. 1994.
[3] G.. A. Rovithakis and M. A. Christodoulou, ”Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks,” IEEE Trans. Syst., Man, Cybern., vol.25, pp. 1578-1594, Dec. 1995.
[4] L. X. Wang, “Stable adaptive fuzzy control of nonlinear systems,” IEEE Trans. Fuzzy Syst., vol. 1, pp. 146-155, May 1993.
[6] J. T. Spooner and K. M. Passino, “Stable adaptive control using fuzzy systems and neural networks,” IEEE Trans. Fuzzy Syst., vol. 4, pp. 339-359, Aug. 1996.

延伸閱讀