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A Roll Stabilization System for a Monohull Ship: Modeling, Identification, and Adaptive Control

A Roll Stabilization System for a Monohull Ship: Modeling, Identification, and Adaptive Control. 指導教授:曾慶耀 學生:陳柏均 學號: 10067006 日期: 2011.11.17. 大綱. 摘要 船舶介紹 系統方塊圖 Design of a Lead Controller Adaptive LQ Controller 結論. 摘要.

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A Roll Stabilization System for a Monohull Ship: Modeling, Identification, and Adaptive Control

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  1. A Roll Stabilization System for a Monohull Ship:Modeling, Identification, and Adaptive Control 指導教授:曾慶耀 學生:陳柏均 學號:10067006 日期:2011.11.17

  2. 大綱 • 摘要 • 船舶介紹 • 系統方塊圖 • Design of a Lead Controller • Adaptive LQ Controller • 結論

  3. 摘要 為了減少海浪所造成的橫搖,使用兩個輔助自動控 制的翅膀。這兩個翅膀在水中可改變傾角產生力矩,降 低船舶的橫搖。以船舶的實驗數據為基礎,建立動態模 型。針對橫搖影響設計兩種不同的補償器:第一個使用 典型頻域技術,第二個使用適應LQ(linear quadratic)補 償。

  4. 船舶介紹 長47公尺,排水量160噸,載客數512名 最大船速35節,手臂長2.5公尺

  5. 系統方塊圖

  6. Ship Dynamic(1) Ψ(s)是橫搖角度、O(s)是全部輸入力矩 Kns = 9.5 * 10^-7 rad/Nm Δ是船重、h是橫向穩心高度、Jg是船的慣性 Δ=151392Kg、Jg = 1.189 * 10^6kgm^2

  7. Ship Dynamic(2) 從船的設計資料推導出來ωn=1.25rad/s 在船速為零時,船的相對阻尼 =0.5 當順向船速增加,阻尼也會跟著增加 在順向船速為35節時,阻尼 =0.8

  8. Model of the Wing System(1) 單一翅膀在海中改變傾角會產生一個流力,如上式 Ap是在水中的表面積、Vn是船速 參數k與海的密度有關

  9. Model of the Wing System(2) 假設船在等速下移動,會遵循以下關係式: Cp = Kp * Φ Cp是對應的力矩、Φ是翅膀的角度 Kp=2.3356 * 10^5Nm/rad

  10. Electro-Hydraulic System Model 藉由兩個相同的電動液壓系統產生力矩使得手臂和 翅膀能夠移動。 電動液壓系統可用一階來近似,如下式: Φ(s)/Vh(s) = 0.33/s+1.57 Vh是輸入電壓、 Φ是翅膀角度

  11. Gyroscope 陀螺儀是做為橫搖角度的感測器 這元件產生的電壓正比於橫搖角度 方程式如下: Vroll = Kgyro * Ψroll Kgyro = 11.46 V/rad

  12. Model of the Waves(1) 海浪產生的干擾力矩被模擬成一個隨機訊號,它的頻譜 由下面方程式產生: Sxi(ω)=(αg^2/ω^5)*e^-β(g/vω)^4 g是重力,v是風速, α 、β是常數

  13. Model of the Waves(2)

  14. Identification From Experimental Data • 在平靜的海面進行測量 • 船速維持在35節 • 輸入方波到電動液壓系統 • 每50毫秒收集一次橫搖角度和翅膀角度的變化對船造成的影響 • 用LS來建模電動液壓系統和船的轉移函數

  15. Design of a Lead Controller • 建立一個比例領先控制器 • 補償器的主要規格設計範圍在0.2~3rad/s • 避免控制器的飽和 • 在感興趣的頻率範圍內減少干擾的影響

  16. Adaptive LQ Controller • 藉由改變增益值適應相對應的海況 • 使用類神經網路,在任何時間插入適當的增益值到控制器 • 均方誤差公式: tf =80s, Ψ是橫搖角度

  17. 在三種不同風速下,對於相同干擾訊號,分別測試開路、在三種不同風速下,對於相同干擾訊號,分別測試開路、 領先控制器和LQR的橫搖均方誤差。

  18. 結論 • 本論文介紹自動橫搖穩定系統的各個設計步驟 • 由於不同的海況和飽和度的限制,單線性控制器無法對所有情況做最佳化,使用適應控制器去調整增益值比領先控制器的效果還要好。

  19. References(1) [l] E. V. Lewis and J. P. Comstock, Principles ofNaval Architecture. New York: Society of Naval Architects and Marine Engineers, 1967. [2] J. van Amerongen, P. G. M. van der Klugt, and H. R. van Nauta Lemke, “Rudder roll stabilization for ships,” Automatica, vol. 26, no. 4, pp. [3] W. E. Cowley and T. H. Lambert, “The use of the rudder as a roll stabilizer,” in Proc. 3rd Ship Contr. Syst. Symp., Bath, U.K., 1972. [4] C. G. Kallstrom, “Control of yaw and roll by a ruddedfin stabilization system,” in Proc. 6th Ship Contr. Syst. Symp., Ottawa, Canada, 1981. [5] M. R. Katebi, D. K K Wong, and M J Grimble, “LQG autopilot and rudder roll stabilization control system design,” in Proc. 8th Ship Control Syst. Symp., The Hague, The Netherlands, 1987. [6] G. N. Roberts, “A note on the applicability of rudder roll stabilization for ships,” in Proc. 1993 Amer. Contr. Con$, San Francisco, CA. [7] W. G. Price and R. E. D. Bishop, Probabilistic Theory of Ship Dynamics. London: Chapman & Hall, 1974. [8] R. Bhattacharyya, Dynamics of Marine Vehicles. New York: Wiley, 1978.

  20. References(2) [9] A Tiano and E Volta, “Application of the identification technique to the ship system,” in Proc 7th IFAC Triennial World Congr., Helsiuki, Finland, 1978 [lo] C G Kallstrom and K J Astrom, “Expenences of system identification applied to ship steering," Automatica, vol 17, no 1, pp 187-198, 1981 [11]L. Ljung, System Ident$cation-Theov for the User Englewood Cliffs, NJ Prentice-Hall, 1987 [12] B D 0 Anderson and J B Moore, Optimal Control Linear Quadruhc Methods Englewood Cliffs, NJ Prentice-Hall, 1989 [13] K J Astrom and B Wittenmark, Adaptive Control Reading, MA: Addison- Wesley, 19 89 [14] D E Rumelhart, G E Hinton, and R J Williams, “Learning mtemal representation by error propagation,” in Parallel Dzstnbuted Processing.Explorations in the Microstructure of Cognition, D E Rumelhart and JL McClelland, Eds Cambridge, MA MIT Press, 1986, pp. 318-362 [15] G Cybenko, “Approximation by superpositlons of sigmoidal functions,”Mathematical Contr Syst, vol 2, pp 303-314, 1989 [16] L Fortuna, S Graziani, M Lo Presti, and G Mnscato, “Improving backpropagation learning using auxiliary neural networks,” Int J Contr ,vol 55, no 4, pp 793-807, 1992

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