An Indirect Type-2 Fuzzy Neural Network Optimized by the Grasshopper Algorithm for Vehicle ABS Controller

Model nonlinearity, structured and unstructured uncertainties as well external disturbances are some of the most important challenges in controlling wheel slip moving vehicles. Based on interval type-2 fuzzy neural network, we construct an indirect exponential sliding-mode (ESM) controller for improving performance vehicle antilock braking systems (VABSs) face uncertainties. Lyapunov stability postulate is used to verify closed-loop system also extract adaptation rules. In this scheme, reaching law sliding surface regulated based order eliminate produced chattering. Selecting appropriate constants rules leads quicker signal convergence a better management control restrictions. These optimized by defining cost function employing grasshopper optimization algorithm (GOA) search optimal solution. Thus, provide robust adaptive ESM with GOA VABS. The efficacy proposed method verified analyzing obtained results comparing its other schemes various road conditions driving maneuvers. work affirm that designed makes significant improvement VABS control.