第七章 結論與未來的研究方向
7.2 未來的研究方向
最後由於此次模擬時連續轉離散的方法為近似算法，所以本文的鑑別 還是有可改善的空間，若能克服電腦硬體上的運算速度，相信演算法效果 更能收斂至更佳的解。未來方向則是外加干擾值，來做系統分析，並增加 整體系統強健性。
參考文獻
[1] A. Ichikawa etal , Control Hand Book, Ohmu Publisher, Tokyo in Japanese, 1993.
[2] B. C. Kuo, "Automatic Control System", 7th ed., Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1995.
[3] B. J. Glaria, R. R. Rojas, B. M. Salgado, "Unstructured uncertainty and Graphic robust control system design", Proceedings of the 31 IEEE Conference on Decision and Control, vol.2, pp. 1575-1577, 1992.
[4] C. Phillips and H. T. Nagle, "Digital Control System Analysis and Design", 3rd ed., Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1995.
[5] C. T. Chen, "Linear System Theory and Design", 3rd ed., Oxford University Press, New York, 1999.
[6] E. H. Mandani and S. Assilian, "Application of fuzzy algorithms for control Of simple dynamic plant", Proc. Inst. Elec. Eng. Vol. 121, pp. 1585-1588, 1974.
[7] G.Y. Park and P.H. Seong, “Towards increasing the learning speed of gradient descent method in fuzzy system”, Fuzzy Sets and Systems, vol.77, pp.229-313,1996.
[8] H. Nakanishi, B. I. Turksen and M. Sugeno, "A review and comparison of Six reasoning methods", Fuzzy Sets and Systems, Vol. 57, pp.
257-294,1993.
[9] H. Chapellat, M. A. Dahleh, S. P. Bhattacharyya, "Robust stability under structured and unstructured perturbations", IEEE Transactions on Automatic Control, Vol. 35, pp. 1100-1108, 1990.
[10] J. M. Martin, "State-space measures for stability robustness", IEEE Trans.
Automatic Control, Ac-32, No. 6, pp. 509-512, 1991.
[11] J. Kennedy and R. Eberhart, Particle swarm optimization, IEEE International Conference of Neural Networks, 1995, pp.1942-1948.
[12] K.M. Chow and A.B. Rad, “System identification via a virtual higher-resolution fuzzy model”,Intell. Automat. Soft-Comput. ,vol.6 (4),pp.
243–259 ,2000.
[13] K.M. Chew and A.B. Rad, “On-line fuzzy identification using genetic algorithms”, Fuzzy Sets and Systems ,vol.132, pp.147-171,2002.
[14] K. Tanaka and H. O. Wang, Fuzzy Control Systems Analysis and Design:
A Linear Matrix Inequality Approach, New York: Wiley,2001.
[15] L. A. Zadeh, “Fuzzy sets”, Inform. Control, vol. 8, pp. 338–352, 1965.
[16] L.A. Zadeh, “Fuzzy sets and systems”, in: Proc. Sysmp. Systems Theory, Polytech. Inst. Brooklyn, pp. 29-37, 1965.
[17] L.-X. Wang and J.M. Mendel, “Back-propagation fuzzy system as non-linear dynamic system identifiers”, IEEE Internat.Conf. on Fuzzy Systems, San Diego,CA, pp. 1409–1412 , 1992.
[18] Li-Xin Wang, “Design and analysis of fuzzy identifiers of nonlinear dynamic systems”, IEEE Trans. Automat. Control , vol.40 , pp11-23 , 1995.
[19] L. A. Zadeh, ”Fuzzy Sets,” Inform. And control, Vol. 8, pp. 338-353, 1965 [20] L. A. Zadeh, ”Fuzzy Algorithms,” Inform. Control, Vol. 82, pp. 94-102,
1968.
[21] L. X. Wang, "A Course in Fuzzy Systems and Control", Prentice Hall, Inc.,New Jersey, 1997.
[22] M. Sugeno and K. Tanaka, “Successive identification of a fuzzy model and its applications to prediction of a complex system,” Fuzzy Sets Syst., vol. 42, pp.315–334, 1991.
[23] M. Sugeno and G. T. Kang, "Structure identification of fuzzy model",Fuzzy Sets and Systems, Vol. 28, pp. 15-33, 1988.
[24] M. Sugeno and K. Tanaka, "Successive identification of fuzzy model and Its applicatons to prediction of complex system", Fuzzy Sets and Systems, Vol. 42,pp. 315-344, 1991.
[25] R. Rovatti and R. Guerrieri, “Fuzzy sets of rules for system identification”, IEEE Trans. Fuzzy Systems ,vol.4,pp.89-102,May 1996.
[26] R. H. Cannon, Dynamics of Physical Systems, McGraw-Hill, New York,1967.
[27] S.G. Cao and N.W. Rees, “Identification of dynamic fuzzy models”, Fuzzy Sets and Systems,vol.74,pp.307-320,1995.
[28] S. G. Cao and N. W. Rees, "Identification of dynamic fuzzy models", Fuzzy Sets and Systems, Vol. 74, pp. 307-320, 1995.
[29] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transactions on Systems, Man, and Cybernetics,Vol. SMC-15, No. 1, pp. 116-132,1985.
[30] W.F. Xie and A.B. Rad, “Fuzzy on-line identification of SISO nonlinear system ”,Fuzzy Sets and Systems, vol.107,pp. 323-334,1999.
[31] 呂宗訓，"預測型與專家型模糊控制器之設計"，國立中山大學機械工 程
研究所碩士論文，中華民國八十一年。
[32] 李賢良，"模糊控制應用於平移振盪台車系統"，義守大學電子工程研究
所碩士論文，中華民國九十四年。
[33] 洪惠陽，"未確定系統的特徵值分析與強健控制器設計"，國立中山大學
機械工程研究所博士論文，中華民國八十二年。
[34] 孫宗瀛，楊英魁，"Fuzzy 控制：理論、實作與應用"，全華科技圖書出
版，中華民國八十四年。
[35] 陳明志，"模糊鑑別法於模糊控制器之設計與應用"，逢甲大學自動控制
研究所碩士論文，中華民國八十四年。
[36] 黃智樑，"微量群聚法之最佳化LQ 控制器設計"，國立高雄海洋科技大
學輪機工程研究所碩士論文，中華民國九十五年。
[37] 楊英魁，"Fuzzy 控制"，全華科技圖書出版，中華民國八十一年。
趙清風，使用MATLAB-控制之系統識別，全華圖書，民90 年。
[39] 賴彥翔， "粒子群尋優演算法於滑車倒單擺之系統鑑別"，義守大學電 子工程技術研討會， 2010。
[40] 賴彥翔， "穩定模糊控制器的設計應用在倒車行駛控制"，航空機械工
程學術研討會， 2009。
附錄程式(一)
%%%%car pendulum S-Function %%%%
function [sys,x0,str,ts] = carp(t,x,u,flag) switch flag,
%%%%%%%%%%%%%%%%%%
% Initialization %
%%%%%%%%%%%%%%%%%%
case 0
[sys,x0,str,ts] = mdlInitializeSizes;
%%%%%%%%%%%%%%%
% Derivatives %
%%%%%%%%%%%%%%%
case 1,
sys = mdlDerivatives(t,x,u);
%%%%%%%%%%%
% Outputs %
%%%%%%%%%%%
case 3
sys = mdlOutputs(t,x,u);
%%%%%%%%%%%%%
% Terminate %
%%%%%%%%%%%%%
case { 2, 4, 9 } % Unused flags sys = [];
otherwise
error(['Unhandled flag = ',num2str(flag)]);
end
function [sys, x0,str,ts] = mdlInitializeSizes
sizes = simsizes;
sizes.NumContStates = 2;
sizes.NumDiscStates = 0;
sizes.NumOutputs = 2;
sizes.NumInputs = 1;
sizes.DirFeedthrough = 0;
sizes.NumSampleTimes = 1;
sys = simsizes(sizes);
x0 = [pi/6 0];
str = [];
ts = [0 0]; % continuous sample time: [period, offset]
% end mdlInitializeSizes
function sys = mdlDerivatives(t,x,u)
g=9.8;M=8;m=2;l=0.5;
a=1/(m+M);
c1=g*sin(x(1))-a*m*l*(x(2)^2)*sin(2*x(1))/2-a*cos(x(1))*u;
c2=4*l/3-a*m*l*cos(x(1))^2;
sys = [x(2) ;c1/c2];
function sys = mdlOutputs(t,x,u) sys(1) =x(1);
sys(2) =x(2);
% end mdlOutputs
附錄程式(二)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Identification for Car-Pendulum %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function esum =carpend(pop);
load aa
u=inaa(2,:);
load bb
y1=outbb(2,:);
y2=outbb(3,:);
x1(1)=0;
x2(1)=0;
popsize=size(pop,1);
esum = zeros(size(pop,1),1);
for i = 1:popsize p = pop(i,:);
leng=size(u,2);
for t=1:(leng-1) U=u(t);
T=0.005;
u1=trimf(y1,[-90 -90 0]);
Ac1=[0 1;p(1) 0];
Bc1=[0 ; p(2)];
Ad1=Ac1*T+eye(2)+(((Ac1)^2)*T^2)/2+(((Ac1)^3)*T^3)/6+(((Ac1)^4)*
T^4)/24+(((Ac1)^5)*T^5)/120;
Bd1=(eye(2)*T+Ac1*(T*T/2)+(((Ac1)^2)*T*T*T)/6+
(((Ac1)^3)*T^4)/24+(((Ac1)^4)*T^5)/120)*Bc1;
x11d=Ad1(1,:)*[x1(t);x2(t)]+Bd1(1,:)*U;
x12d=Ad1(2,:)*[x1(t);x2(t)]+Bd1(2,:)*U;
u3=trimf(y1, [-90 0 90]);
Ac2=[0 1;p(3) 0];
Bc2=[0 ; p(4)];
Ad2=Ac2*T+eye(2)+(((Ac2)^2)*T^2)/2+(((Ac2)^3)*T^3)/6+(((Ac2)^4)*
T^4)/24+(((Ac2)^5)*T^5)/120;
Bd2=(eye(2)*T+Ac2*(T*T/2)+(((Ac2)^2)*T*T*T)/6+
(((Ac2)^3)*T^4)/24+(((Ac2)^4)*T^5)/120)*Bc2;
x21d=Ad2(1,:)*[x1(t);x2(t)]+Bd2(1,:)*U;
x22d=Ad2(2,:)*[x1(t);x2(t)]+Bd2(2,:)*U;
x1(t+1)=(u1*x11d+u2*x11d+u3*x21d)/(u1+u2+u3);
x2(t+1)=(u1*x12d+u2*x12d+u3*x22d)/(u1+u2+u3);
g1(t)=y1(t)-x1(t);
g2(t)=y2(t)-x2(t);
end end
esum(i)=g1*g1'+g2*g2' end