A Backstepping Controller with the RBF Neural Network for Folding-Boom Aerial Work Platform

<div>The tracking error of <svg height="12.4698pt" id="M132" style="vertical-align:-3.18147pt" version="1.1" viewbox="-0.0498162 -9.28833 11.2181 12.4698" width="11.2181pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M475 507C475 612 440 712 326 712C139 712 23 420 23 215C23 96 58 -12 180 -12C369 -12 475 293 475 507ZM391 522C391 486 387 448 379 394H126C155 538 222 677 310 677C386 677 391 571 391 522ZM373 346C344 193 283 22 189 22C126 22 106 114 106 196C106 243 111 293 118 346H373Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,6.149,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"></path></g></svg> under the action of (a) backstepping control and (b) NN-based backstepping control.</div>

Complexity

fig5

Figure 5

Figure 5: A Backstepping Controller with the RBF Neural Network for Folding-Boom Aerial Work Platform 