Robust fuzzy sliding mode controller for a skid-steered vehicle subjected to friction variations
Skid-steered vehicles (SSV) are gaining huge importance in the market due to their applications like construction, agricultural work, material handling etc. The accuracy of performing such tasks require a robust control algorithm. The design of such controller is very challenging task due to externa...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Public Library of Science (PLoS)
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5862b0e26c0144e787d91d04d1ac1a7b |
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| Sumario: | Skid-steered vehicles (SSV) are gaining huge importance in the market due to their applications like construction, agricultural work, material handling etc. The accuracy of performing such tasks require a robust control algorithm. The design of such controller is very challenging task due to external disturbances caused by wheel-ground interaction and aerodynamic effects. This paper proposes robust fractional and integral order fuzzy sliding mode controllers (FSMC, FFSMC) for a skid-steered vehicles with varying coefficient of friction and a displaced center of gravity (CG). FFSMC controller reduces the outcome of forces generated as a result of ground tire interaction during skidding and friction variations. The proposed controllers are implemented for a four-wheel SSV under high-speed turning motion. A simulation environment is constructed by implementing the SSV dynamics with wheel-road model and the performance of the proposed algorithms is tested. The simulation test is conducted for a Pioneer-3AT (P-3AT) robot SSV vehicle with displaced CG and variable coefficient of tires friction. Simulation results demonstrate the efficiency of the proposed FFSMC algorithm in term of reduced state errors and minimum chattering. The proposed controller compensates the effect of different responses of the wheels generated as a result of variable CG. The chattering phenomenon generated by conventional SMCs is also minimized by fuzzy tuning approach. |
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