A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems

<div>The formation tracking velocity error <svg height="10.3116pt" id="M262" style="vertical-align:-4.35069pt" version="1.1" viewbox="-0.0498162 -5.96091 15.1053 10.3116" width="15.1053pt" 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="M391 364C391 409 353 448 295 448C249 448 198 426 152 393C65 331 23 225 23 139C23 14 96 -12 146 -12C198 -12 280 9 367 101L351 124C300 78 242 48 194 48C129 48 109 107 109 162V191C208 213 391 266 391 364ZM313 350C313 305 268 261 113 223C132 334 187 381 217 398C227 404 244 405 261 405C290 405 313 385 313 350Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,5.317,3.132)"><path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,7.865,3.132)"><path d="M98 134C72 134 46 117 46 90C46 73 55 65 60 64C95 55 124 32 124 -4C124 -42 95 -68 44 -89L57 -123C122 -104 194 -60 194 22C194 94 136 134 98 134Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.022,3.132)"><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> in Case <a href="https://static.hindawi.com/articles/complexity/volume-2022/5093277/figures/#caseb2">B</a>(a) The formation tracking velocity error of the <i>x</i>-direction of the <i>i</i>th agent <i>i</i> = 1, 2, 3. (b) The formation tracking velocity error of the <i>y</i>-direction of the <i>i</i>th agent <i>i</i> = 1, 2, 3.</div>

Complexity

fig15

Figure 15

Figure 15: A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems 