陳炳昌 PING-CHANG , CHEN [email protected] skype:cpc1751

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控制系統. 陳炳昌 PING-CHANG , CHEN [email protected] skype:cpc1751. 1. CHAPTER. 導 論. 本章目的. 1. 控制系統是什麼。 2. 控制系統的重要性為何。 3. 控制系統的基本組件是什麼。 4. 控制系統的一些應用實例。 5. 為何大多數的控制系統需要回授。 6. 控制系統的型式。. 控制系統於現代文明之應用. 電力監控、工具機控制、太空技術與軍事系統、電腦控制、運輸系統、動力系統、機器人、微機電系統 、奈米生物科技與其它控制應用. 1. CHAPTER. - PowerPoint PPT Presentation

Transcript of 陳炳昌 PING-CHANG , CHEN [email protected] skype:cpc1751

  • [email protected] skype:cpc1751*

  • 1. 2. 3. 4. 5. 6.

  • 1. 2. 3. 1-1 (inputs) (actuating signals) u (outputs) (controlled variables) y

  • MATLAB Simulink (multivariable systems)

  • ()

  • () PID

  • 1-11 - (1-1) r y e b G H

  • ( 1-11 GH 1 + GH 1

  • 1-13

  • 1) (analog data) (discrete-data) 2) (modulated) (unmodulated) : :1) (linear) (nonlinear)2) (time-varying) (time-invariant): (position-control system) (velocity-control system)

  • r(t) e(t) () 1-16

  • 1. 2. 3. MATLAB z

  • 1. 2. s f (t) f (t) (2-3) s = + j (one-sided Laplace transform) (causal system) (physically realizable system) t = 0 t = 0 t = 0 t 0

  • f (t) f (t) f (t)

  • PS:c F(s) MATLAB (TFtool) 1. k 2.

  • 3. (2-13) f (t) (2-14) f (i) (0) f (t) t i t = 0 4. (2-15) n (2-16)

  • 5. (2-17) us (t T) T 6. (2-18) 7. (2-19) sF(s) j s sF(s) sF(s)

  • Findfind(2-22) 8. 9. t < 0 f1(t) = 0f2(t) = 0 (2-24) t (convolution)

  • P(s) Q(s) s G(s) s1 s2 sn (i = 12n)

  • Ks1

  • G(s) s = si r ploes(i 12n r ) G(s) (n r) Ks1Ks2Ks(n r) (n r) A1A r

  • jG(s) G(s) damping ratio) 1g (t) = ?

  • (2-55)

  • 1. s2. Y(s)3. 4. y(t) y(0) = 1

  • y (t) (2-68) y(t) dy(t)/dt

  • SISO-- n (2-86) a0a1an1 b0b1bm u(t) y(t)

  • 1. 2. 4. 5. s z z ( n > m) (strictly proper) n = m (proper) m > n (improper) S domaIn

  • (a) (b) 1. - 2. K

  • 1. 2. 3. (a) (b) (c) (d) a)

  • r(t)R(s) () y(t)Y(s) () b(t)B(s) u(t)U(s) = e(t)E(s) H(s) = 1H(s) G(s)H(s) L(s) = G(s) M(s) Y(s)/R(s) = M(s)

  • - N

  • N (3-21) SFG

  • Ex. y2 = a12y1 y1 y2 a12 y1 y2 a12 SFG

  • 1. 2. 3. 4. 5. yk yj yj yk 6. yk yk yj yj akj akj yk SFG () ()

  • y2 (path) (forward path) y1 y3 y1 y4 y1 y5 (d) (loop)

  • (d) (d) (d)

  • (path gain) (forward-path gain) (loop gain) y2 y4 y3 y2 a24a43a32 SFG SFG (nontouching) (d) SFG y2 y3 y2 y4 y4 (d)

  • SFG Mason rule SFG N K yin yout yin = yout = M = yin yout N = yin youtMk = yin yout k

  • Lmr = r (1 r K) m (m = ijk) = 1 () + ( ) () + k = k SFG 3. Y(s)/R(s)

    1. R(s) Y(s)

  • (d) (3-34) 2. (3-35) 3. 1 = 1 (3-31) SFGmason rule y1 y5

  • 1. 2. SFG 3. 4. M1 M3 1 = 3 = 1 5. M2 y3 y4 y4 y4 y4

  • 6. y1 y5 y2 5. SFGmason-

  • SFG y7/y2 yin SFG yout y2 yout /y2 y2 y7 1. - 2.

  • (b) SFG Y(s)/R(s) = ?

  • 1. (b) 2. 1. 2. SFG 3. SFG

  • 1. x1(t) x2(t) (3-54) X1(s) = [X2(s)/s] + [x1(t0)/s] 2.

  • 3-18 X1(s) = [X2(s)/s] + [x1(t0)/s] 1. 2. 3. 4. 5. 6.

  • B- - 1. n n i = 12n xi (t) i uj (t)j = 12p j wk(t) k k = 12v 2. y1(t)y2(t)yq(t) q j = 12q (dynamic equation)

  • 3. - 1) 2) (5-3) 3) (5-5) 4) 4.

  • x (t0) u (t) w (t)(t t0) 1. 2. (t) n n x(0) t = 0 (t) t 0

  • 3. (sI A) (t) eAt t 0 eAt At

  • 1. (free response) 2. (t) t = 0 t 1. () 2.

    (5-27) e At (5-30) 1(t) (5-24)

  • 1. 2. x(0) t = 0 t0 x(t0) u(t) w(t) t 0 (5-41) t = t0 x(0)

  • 3. t 0 u (t) = 1 t 0 (t) x(t)

  • 1. A B 2. A 3. t 0

  • 1. SFG (5-40) 2. t0 (5-40) 3. Xi (s)i = 12n 4.

  • 1. n2.

  • 3. (phase-variable canonical form, PVCF)4. -

  • 5.

    1. (5-80) 2.

  • 3. - x(t) 3 1 u(t) 4. 1. nth-order system: The state variable must be chosen such that they will eliminate the derivatives of u in the state equation. 2. n state variables:

  • where are determined from With the present choice of state variables, we obtain

  • In terms of vector-matrix equations, Equation (3-36) and the output equation can be written as and

  • 1. x(t) = n 1 y(t) = q 1 u(t) = p 1 w(t) = v 1 2. x (0) = 0 (5-89)

  • Gu(s) w(t) = 0 u(t) y(t) q p Gw(s) u(t) = 0 w(t) y(t) q v 5-5

  • 1. 2. s 3.

  • :

    : Determine of A

  • 8.( A A

    A 1. A 2. 12n A A A 3. ii = 12n A A' 4. A ii = 12n 1/ii = 12n A1 A

  • ii = 12n A pi A i (5-38) A

    1. A 2. 1 = 1 2 = 1 3.

  • 1=1 p1 2 = 1(5-126) eigenvalue1. A A (5-120) 2. A n q(< n) q i i i = 12q 3. j m (m n q) m

  • 11. A A

    1. A 1 = 22 = 3 = 1 2. 1 = 2 (5-128) p11 = 2 p21 = 1 p31 = 2 (5-131) 3. 2 = 1 (5-129) (5-133) (5-134) 3 = 1 (5-129)

  • 1. - (SISO) : 2. 3.

  • 4. (5-140) t 1.

  • 2. (CCF) 1. (5-137) (5-138) 2. A 3.

  • (

  • (OCF) 1. 2. (5-164) 3. (5-141) (5-142)

  • 4. M V (observability matrix) V 1 OCF

    1. M (5-160)

  • 2. 3. OCF 4. OCF (DCF) 1. 2. A

  • 12n A n (DCF) 3. DCF T pii = 12n i 4. n n

  • A CCF DCF (Vandermonde) DCF

    1. 1 = 12 = 2 3 = 3

  • 2. A CCF DCF (5-183) 3. A DCF JORDEN (JCF) 1. A 2. JCF

  • 1. A 2. A 3. 1 4. 1 5. A n n A r (r < n) 6. r 7. 1 n r 3. 4. T JCF

  • ADCF

    1. A 211 2. DCF DCF

  • 1. 2. n SISO U(s) Y(s) CCF 1. s 2. X(s) 3. 4.

  • 5. 5-6 CCF 1) x1(t)x2(t)xn(t) 2) u(t) 3)

  • State equation:Output equation: OCF 1. 2. s n

  • 3. CCF 4.

  • 1. 2. 5-10 3. u(t) SFG

  • 4. u(t) y(t) 5-10 1. 2. 3.

  • 1. 2. DCF JCF 3. 4. (5-217) 5. 6.

  • DCF JCF

    1.

  • 2.

  • (controllability) (observability) (Kalman) 1. 2. 5-14(a)

  • 3. K 4. (a) (b) 5. K (ABK) 6. 7. K (ABK)

  • 1. 2. (unconstrained) u (t) (completely controllable) 3. 4. u(t) x1(t) x2(t) u(t) (tf t0) x2(t0) x2(t) x2(tf)

  • 5-16 1. x(t) n 1 u (t) r 1 y(t) p 1 ABC D 2. (tf t0) 0 u(t)x(t0) x(tf) x(t) t = t0 x(t0) n nr n [ AB ] S n

  • S n n SS' SS' S n r = 1 - (SISO) A B CCF CCF [ AB ] A DCF JCF B [ AB ] Ex. JCF A B B A B b31 0b32 0b41 0 b42 0

  • S = [B AB]

    1. )2. S

  • 1. S 2. A 1 = 22 = 2 3 = 1 1

  • 1. 2. 3. 5-17 x2 y(t) y(t) x1(t) x1(t) = y(t) x2 y(t) 1.

  • 2. u(t) tf t0 t0 t < tf u(t) ABC D t0 t < tf y(t) x(t0) x(t0) A C n np n C 1 n V n n V (SISO) ( r = 1 p = 1) A C OCF OCF [ AC ]

  • A DCF JCF C [ AC ] A C 5-21 5-17

  • 1. [ AC ] 2. A DCF C x2(t) --- - Ex. SISO

  • 1. A x1 (C O) x2 (C UO) x3 (UC O) x4 (UC UO) 2. DCF 3. (5-240) - -

  • CCF OCF [A] CCF: 1. CCF CCF [ AB ] 2. CCF [ AC ] [B] OCF: 1. OCF OCF [ AC ] 2. (5-247) OCF [ AB ]

  • 3~5 ..()...,.,.*

    **