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A Prelude to Control Theory |
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1 | (14) |
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An ancient control system |
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1 | (2) |
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Examples of control problems |
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3 | (6) |
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Open-loop control systems |
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3 | (2) |
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Closed-loop control systems |
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5 | (4) |
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Stable and unstable systems |
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9 | (1) |
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A look at controller design |
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10 | (4) |
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14 | (1) |
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Mathematical Models in Control |
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15 | (70) |
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Introductory examples: pendulum problems |
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15 | (14) |
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15 | (5) |
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Example: inverted pendulum on a cart |
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20 | (9) |
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State variables and linear systems |
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29 | (3) |
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Controllability and observability |
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32 | (2) |
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34 | (8) |
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Damping and system response |
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36 | (1) |
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Stability of linear systems |
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37 | (2) |
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Stability of nonlinear systems |
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39 | (2) |
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41 | (1) |
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42 | (6) |
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State-variable feedback control |
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48 | (5) |
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48 | (2) |
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50 | (3) |
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Proportional-integral-derivative control |
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53 | (24) |
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Example: automobile cruise control system |
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53 | (8) |
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Example: temperature control |
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61 | (10) |
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Example: controlling dynamics of a servomotor |
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71 | (6) |
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Nonlinear control systems |
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77 | (1) |
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78 | (2) |
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80 | (5) |
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85 | (48) |
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Fuzziness and linguistic rules |
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85 | (1) |
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86 | (4) |
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90 | (14) |
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Minimum, maximum, and complement |
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90 | (2) |
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Triangular norms, conorms, and negations |
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92 | (9) |
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101 | (3) |
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104 | (4) |
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Extreme measure of sensitivity |
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104 | (2) |
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106 | (2) |
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108 | (6) |
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110 | (1) |
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110 | (1) |
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111 | (1) |
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Takagi-Sugeno-Kang (TSK) model |
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112 | (1) |
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113 | (1) |
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Truth tables for fuzzy logic |
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114 | (2) |
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116 | (1) |
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117 | (3) |
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119 | (1) |
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120 | (1) |
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120 | (3) |
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120 | (1) |
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Height-center of area method |
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121 | (1) |
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122 | (1) |
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122 | (1) |
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123 | (1) |
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Level curves and alpha-cuts |
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123 | (3) |
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124 | (1) |
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Images of alpha-level sets |
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125 | (1) |
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126 | (2) |
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128 | (5) |
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133 | (32) |
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A fuzzy controller for an inverted pendulum |
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133 | (4) |
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Main approaches to fuzzy control |
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137 | (7) |
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Mamdani and Larsen methods |
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139 | (1) |
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Model-based fuzzy control |
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140 | (4) |
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Stability of fuzzy control systems |
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144 | (2) |
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146 | (11) |
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Example: automobile cruise control |
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146 | (5) |
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Example: controlling dynamics of a servomotor |
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151 | (6) |
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157 | (8) |
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Neural Networks for Control |
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165 | (36) |
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What is a neural network? |
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165 | (3) |
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Implementing neural networks |
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168 | (4) |
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172 | (3) |
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175 | (4) |
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The backpropagation algorithm |
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179 | (4) |
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Example 1: training a neural network |
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183 | (2) |
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Example 2: training a neural network |
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185 | (7) |
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Practical issues in training |
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192 | (1) |
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193 | (8) |
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201 | (28) |
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Why neural networks in control |
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201 | (1) |
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202 | (2) |
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Neural networks in direct neural control |
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204 | (1) |
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Example: temperature control |
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204 | (12) |
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A neural network for temperature control |
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205 | (4) |
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Simulating PI control with a neural network |
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209 | (7) |
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Neural networks in indirect neural control |
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216 | (9) |
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217 | (2) |
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Example: system identification |
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219 | (4) |
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Instantaneous linearization |
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223 | (2) |
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225 | (4) |
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Fuzzy-Neural and Neural-Fuzzy Control |
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229 | (20) |
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Fuzzy concepts in neural networks |
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230 | (2) |
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Basic principles of fuzzy-neural systems |
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232 | (4) |
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Basic principles of neural-fuzzy systems |
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236 | (9) |
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Adaptive network fuzzy inference systems |
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237 | (1) |
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238 | (7) |
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245 | (1) |
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246 | (3) |
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249 | (42) |
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A survey of industrial applications |
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249 | (1) |
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Cooling scheme for laser materials |
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250 | (6) |
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256 | (6) |
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Identification of trash in cotton |
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262 | (17) |
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Integrated pest management systems |
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279 | (11) |
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290 | (1) |
Bibliography |
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291 | (6) |
Index |
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297 | |