SYSTEM ANALYSIS THROUGH BOND GRAPH MODELING by ...

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36 diagram is created <strong>by</strong> connecting the bond graph elements presented above, such that the power flow through the system is mapped. This is best presented via an example. Figure 3.6 shows a circuit diagram and the corresponding power flow diagram. The power flow diagram represents the power flow through the circuit. The bonds shown in the power flow diagram of figure 3.6 have been arbitrarily numbered to facilitate discussion. Figure 3.6. Circuit Example The sinusoidal input voltage of figure 3.6 is represented as an effort source in the power flow diagram. The 1-junctions represent locations in the circuit with common current flow. The 0-junctions of the power flow diagram represent the nodes of the circuit since, for each node, the voltages (efforts) are the same across each path of the node. Series circuit elements are connected at 1-junctions since the current flowing through the elements is the same for each element.
37 As seen in figure 3.6 the power flow diagram preserves the topological structure of the circuit diagram. The power flow diagram is an intuitive representation of the power flow through the circuit. The power flow diagram of figure 3.6 can be simplified, however. The voltage represented <strong>by</strong> the 0-junction that connects bonds 8, 10, and 11 is the ground voltage. Figure 3.7. Simplified Circuit Power flow Diagram Since this represents zero volts in the circuit, this 0-junction forces bonds 8, 10, and 11 to have a value of zero for each of their respective effort variables. Thus, the power-bonds 8, 10, and 11 have zero power in them, since power is effort * flow. Bonds with zero power can be erased from the diagram. The simplified diagram is shown in figure 3.7.
Page 1 and 2: SYSTEM ANALYSIS THROUGH BOND GRAPH
Page 3 and 4: 3 STATEMENT BY AUTHOR This disserta
Page 5 and 6: 5 DEDICATION To Shannon, for her un
Page 7 and 8: TABLE OF CONTENTS (continued) 4.3.3
Page 9 and 10: 9 LIST OF FIGURES Figure 3.1. Power
Page 11 and 12: LIST OF FIGURES (continued) Figure
Page 13 and 14: LIST OF FIGURES (continued) Figure
Page 15 and 16: 15 LIST OF TABLES Table 3.1. Effort
Page 17 and 18: 17 CHAPTER 1: Introduction 1.1 Prob
Page 19 and 20: 19 Chapter 5 provides a means of me
Page 21 and 22: 21 CHAPTER 2: Related Work 2.1 Intr
Page 23 and 24: 23 dissipated energy in the form of
Page 25 and 26: 25 of a thermo-fluid bond graph can
Page 27 and 28: 27 between the describing parameter
Page 29 and 30: 29 variables associated with it. Na
Page 31 and 32: 31 3.2.2 Bond Graph Junctions Power
Page 33 and 34: 33 power. They are called 1-port el
Page 35: 35 the conjugate variables. As seen
Page 39 and 40: 39 between all of the variables are
Page 41 and 42: 41 Figure 3.11. Possible Causal Ass
Page 43 and 44: 43 Figure 3.14. Possible Causal Ass
Page 45 and 46: 45 3.2.7 Bond Graph Causal Mark Ass
Page 47 and 48: 47 information is obvious from the
Page 49 and 50: 49 3. Use the conjugate variables a
Page 51 and 52: 51 3.2.9 Conversion of Bond Graph V
Page 53 and 54: 53 obtaining dynamic equations, and
Page 55 and 56: 55 3.3.1 Lagrangian to Hamiltonian
Page 57 and 58: 57 dL ∂L ∂L ∂L & (3.22) ∂q
Page 59 and 60: 59 An underlying assumption in the
Page 61 and 62: 61 7. Develop the Hamiltonian for t
Page 63 and 64: 63 The partial of the Lagrangian wi
Page 65 and 66: 65 bond graph shows a single degree
Page 67 and 68: 67 of a Lagrangian/Hamiltonian appr
Page 69 and 70: 69 d dt through 3.48 and the simple
Page 71 and 72: 71 since flows sum around a 0-junct
Page 73 and 74: 73 2 2 ( + A′ )& θ − ( A + B
Page 75 and 76: 75 of the bond graph, i.e., they ar
Page 77 and 78: 77 The simplest bond graph equation
Page 79 and 80: 79 2 ( A + B′ )&& φ sin θ + ( A
Page 81 and 82: 81 ω & 3 = θ (3.102) The method d
Page 83 and 84: 83 Also, the bond graph maps the fl
Page 85 and 86: 85 4.2 Dymola The Dymola framework
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87 such that the user can assign a
Page 89 and 90:
89 A possible set of equations for
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91 Vδ1 = V 2 −V1 Vδ 2 = Vi −V
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93 The icon window has been left bl
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95 derive the equations for a bond
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97 ⎡ 1 ⎛ q ⎤ B4 ⎞ 1 ⎛ qB4
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99 f f f 2 3 1 : : : x 2 1 1 1 x 1
Page 101 and 102:
101 4.2.3.1 Structural Singularitie
Page 103 and 104:
103 Figure 4.10. Gear Train Bond Gr
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105 This section shows that bond gr
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107 [Pan88]. Also, note that the co
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109 The iconic representation of th
Page 111 and 112:
111 Figure 4.14. A-Causal Bond As s
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113 The bond models are complete an
Page 115 and 116:
115 Figure 4.19. Three-Port One The
Page 117 and 118:
117 Figure 4.22 shows a bond graph
Page 119 and 120:
119 Figure 4.25. C-Element Model Fi
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121 The transformer model defines t
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123 Figure 4.32. Modulated Effort S
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125 from the bond graph 3-tuple, an
Page 127 and 128:
127 Figure 4.38. Q Sensor Naturally
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129 These models can now be used to
Page 131 and 132:
131 Figure 4.43. Gyroscope Model: E
Page 133 and 134:
133 4.4.2 Inertial Rate Sensor Mode
Page 135 and 136:
135 The gyroscope in figure 4.45 is
Page 137 and 138:
137 4.4.2.3 Roll Gyro Figure 4.50 s
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139 Figure 4.53. Sensor Delays The
Page 141 and 142:
141 As seen in figure 4.55, the pla
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143 The icon labeled Platform_Cntrl
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145 pointed at a fixed inertial poi
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147 in the camera, a pitch command
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149 The icon of the completed model
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151 inertial axes. These inertial c
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153 The yaw achieved response is sh
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155 80 Camera: Pitch Pitch Angle (d
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157 bond graph and drop it into a h
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159 can be used to monitor the syst
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161 5.2.1 Servo Positioning System:
Page 163 and 164:
163 diode is seen in figure 5.3 as
Page 165 and 166:
165 Figure 5.5. Gear Train and Fin
Page 167 and 168:
167 The modulated effort source is
Page 169 and 170:
169 Figure 5.7. Linear Fin Dynamics
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171 Here the electrical dynamics ar
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173 Similarly, the state space equa
Page 175 and 176:
175 Magnitude (dB) 50 0 -50 -100 -1
Page 177 and 178:
177 showing up on terms (3, 4) and
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179 outputs are fin position and se
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181 5.4 Servo Controllers Separate
Page 183 and 184:
183 Figure 5.14. Linear Controller/
Page 185 and 186:
185 5.4.3 Non-Linear Control Scheme
Page 187 and 188:
187 Figure 5.17. Content of the Y3
Page 189 and 190:
189 The power signal vector is pass
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191 6 5 (deg) Step: Hinge Moment =
Page 193 and 194:
193 1.5 x 106 5 (deg) Step: Hinge M
Page 195 and 196:
195 PID1 delivers less energy to th
Page 197 and 198:
197 Figure 5.28 shows a clear and c
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199 The step responses and efficien
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201 Figures 5.34 and 5.35 correspon
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203 Figures 5.38 and 5.39 correspon
Page 205 and 206:
205 Figure 5.40. Two Non-Linear Act
Page 207 and 208:
207 Figure 5.43 through 5.47 show t
Page 209 and 210:
209 5.3 5 (deg) Step: Hinge Moment
Page 211 and 212:
211 Figures 5.49 through 5.54 show
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213 20.3 20.2 20 (deg) Step: Hinge
Page 215 and 216:
215 CHAPTER 6: Optimal Gain Compari
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217 need to be optimized further, o
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219 S ref C N 2 πd = (6.3) 4 2 1.5
Page 221 and 222:
221 Figure 6.5 shows a Dymola model
Page 223 and 224:
223 The equations used to execute t
Page 225 and 226:
225 Angle of attack and body accele
Page 227 and 228:
227 A Dymola model of the above aut
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229 1 3-Loop AP: -12.92 G step resp
Page 231 and 232:
231 Figure 6.18. Three Loop AP: Clo
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233 6 3-Loop AP: -12.92 G step resp
Page 235 and 236:
235 than the missile response. Prop
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237 Equation 6.15 shows that C N is
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239 M δ Q * S * d * C ref Mδ I yy
Page 241 and 242:
241 5 0 5 Deg. Fin Deflection, at S
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243 0 -0.5 3 Loop AP: -12.92 G step
Page 245 and 246:
245 can be exploited to place the c
Page 247 and 248:
247 Although equations 6.44 through
Page 249 and 250:
249 Figures 6.32 through 6.34, show
Page 251 and 252:
251 Figures 6.35 and 6.36, show how
Page 253 and 254:
253 6 Complete System: -12.92 G ste
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255 view. Gain set 1 rises higher i
Page 257 and 258:
257 Figures 6.43 and 6.44 show the
Page 259 and 260:
259 compare controller efficiencies
Page 261 and 262:
261 1.2 x 10-6 Complete System: -12
Page 263 and 264:
263 It is interesting to note, that
Page 265 and 266:
265 where −1 T K = −R B P (6.52
Page 267 and 268:
267 ∂J − ∂t * = H 1 ⎛ ∂J
Page 269 and 270:
269 u * = KC −1 * [ y − Du ] (6
Page 271 and 272:
271 Where −1 ⎡1⎤ [ − K D] K
Page 273 and 274:
−1 T ( Q − SR S ) 273 Ch = (6.1
Page 275 and 276:
275 real matrix, and an imaginary m
Page 277 and 278:
277 ~ the characteristic polynomial
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279 6.6.6 Nonlinear Autopilot Resul
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281 15 10 Complete System: -12.92 G
Page 283 and 284:
283 0.07 Complete System: 1 G step
Page 285 and 286:
Page 287 and 288:
287 Shifting the cg towards the nos
Page 289 and 290:
Page 291 and 292:
291 much increase in efficiency red
Page 293 and 294:
293 CHAPTER 7: Summary 7.1 Contribu
Page 295 and 296:
295 Various control schemes were pr
Page 297 and 298:
297 Naturally this expansion is not
Page 299 and 300:
299 Real Hric[1, 2]; Real Lric[1, 1
Page 301 and 302:
Text( extent=[-16, -62; 18, -80], s
Page 303 and 304:
Modelica.Blocks.Interfaces.OutPort
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Text(extent=[36, 38; 78, 24], strin
Page 307 and 308:
307 AB2 = A*AB1; AB3 = A*AB2; for j
Page 309 and 310:
309 for j in 1:n loop Reigvec_outpu
Page 311 and 312:
311 eig3[2] = eig_input.signal[7];
Page 313 and 314:
313 APPENDIX A5: Dymola Models, Mis
Page 315 and 316:
315 APPENDIX B1: Symmetry of Hamilt
Page 317 and 318:
317 APPENDIX B2: Vandermonde Repres
Page 319 and 320:
319 APPENDIX C: Glossary of Terms 2
Page 321 and 322:
321 Cur84 Dym Elm94 Fah99 Fah94 Fav
Page 323 and 324:
323 Mas91 Mat McB05a Maschke, B.,
Page 325:
325 Zar02 Zei95 Zho96 Zarchan, P.,
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SYSTEM ANALYSIS THROUGH BOND GRAPH MODELING by ...

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