Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Controller</td><td class="align_center">MSE</td><td class="align_center">Nbr. of iterations</td><td class="align_center">Learning rate</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">RBFN [<a href="/journals/mse/2020/8642915/#B9" target="_blank">9</a>]</td><td class="align_center">0.1427</td><td class="align_center">2000</td><td class="align_center">0.027</td></tr><tr><td class="align_left">DRNN [<a href="/journals/mse/2020/8642915/#B2" target="_blank">2</a>]</td><td class="align_center">0.0288</td><td class="align_center">800</td><td class="align_center">0.0354</td></tr><tr><td class="align_left">FFNN</td><td class="align_center"><span style="width: 69.1013ptpx;"><svg height="11.9413pt" id="M135" style="vertical-align:-0.3499002pt" version="1.1" viewbox="-0.0498162 -11.5914 69.1013 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132 310C155 323 189 337 227 337Z"></path></g><g transform="matrix(.013,0,0,-0.013,15.446,0)"><path d="M456 178V225H360V632H320C217 496 115 347 20 206V178H280V106C280 40 276 34 189 27V0H445V27C364 34 360 39 360 106V178H456ZM280 225H82C149 335 214 431 278 520H280V225Z"></path></g><g transform="matrix(.013,0,0,-0.013,21.686,0)"><path d="M456 178V225H360V632H320C217 496 115 347 20 206V178H280V106C280 40 276 34 189 27V0H445V27C364 34 360 39 360 106V178H456ZM280 225H82C149 335 214 431 278 520H280V225Z"></path></g><g transform="matrix(.013,0,0,-0.013,30.831,0)"><path d="M528 54L331 254L528 455L492 493L294 291L96 493L60 455L257 254L60 54L96 16L294 217L492 16L528 54Z"></path></g><g transform="matrix(.013,0,0,-0.013,41.368,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,47.608,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 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-12Z"></path></g><g transform="matrix(.013,0,0,-0.013,9.204,0)"><path d="M249 635C141 635 70 555 70 471C70 401 114 353 179 316C143 294 106 267 90 252C68 231 45 202 45 157C45 50 130 -12 237 -12C322 -12 435 52 435 169C435 256 372 304 303 343C349 374 375 398 383 407C401 429 411 458 411 487C411 569 344 635 249 635ZM238 603C285 603 337 567 337 482C337 422 310 385 276 358C205 393 145 426 145 500C145 552 179 603 238 603ZM248 20C183 20 125 70 125 163C125 218 158 268 206 300C284 261 355 217 355 143C355 66 308 20 248 20Z"></path></g><g transform="matrix(.013,0,0,-0.013,15.444,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,21.684,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 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635 137 610 109 578L64 515L88 493C112 529 154 573 208 573S303 542 303 482C303 409 233 370 141 341L149 308C165 313 190 319 215 319C272 319 341 283 341 193C342 98 292 43 222 43C163 43 122 72 96 94C88 101 79 100 70 94C61 87 47 73 46 60C44 47 48 37 62 23C76 10 118 -12 165 -12C238 -12 430 62 430 223C430 297 379 359 290 375V377Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,63.885,-5.741)"><path d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"></path></g></svg></span></td><td class="align_center">100</td><td class="align_center">0.01</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison of the performance index of the proposed controller and other controllers (example 3).</div>

Modelling and Simulation in Engineering

tab3

Table 3

Table 3: Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems 