Lateral control of intelligent vehicles using radial basis function neural networks with sliding mode control based on fractional order calculus

Purpose This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle motion. Design/methodology/approach In this presighting and kinematic models were established, new mode isokinetic convergence law devised based on fractional order calculus make front wheel turning angle approach desired value quickly. On basis, gradient descent algorithm adjust radial basis function (RBF) neuron parameter update rules compensation speed neural network. Findings The simulation results revealed that, compared traditional strategy, designed controller eliminated jitter control, sped up response controller, reduced overshoot system parameters facilitated accurate fast tracking path when changed lanes at low speeds. Originality/value combines idea with algorithm, applied RBF network adjustment position width neurons.