An extensive critique of sliding mode control and adaptive neuro-fuzzy inference system for nonlinear system. (English) Zbl 07887155
Summary: Nonlinear control hypothesis is the region of control hypothesis which manages frameworks that are nonlinear, time-variant, or both. Nonlinear control structures have extended high prevalence fundamentally because of the wide use of hypothetical intends to deal with credible issues in different zones of engineering and medical aspects. Sliding mode control (SMC) is a nonlinear control strategy applied when the deterministic model of the nonlinear system is discrepant with actual plant in terms of plant parameters and undetermined external disturbances. SMC is a sort of nonlinear control that accentuates zero error convergence modeling error, demonstrating nonlinearities and outside aggravations. Adaptive neuro-fuzzy inference system (ANFIS) captures neural associations and soft reason guidelines in a single framework. ANFIS causes fewer errors and is more obvious to the customer isolated from the ANN. The paper centers that neural organizations accept a critical capacity in controlling nonlinear frameworks. This review aims an overview of existing methods for nonlinear control and application of SMC for nonlinear system based on ANFIS.
© 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd
© 2021 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd
MSC:
| 93-XX | Systems theory; control |
Keywords:
nonlinear system identification; sliding mode control (SMC); adaptive neuro-fuzzy inference system (ANFIS)References:
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