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This paper proposes a dynamic sliding mode control (SMC) approach to the robust voltage regulation of dc–dc boost converters by using interval type-2 fuzzy neural networks (IT2FNNs). First, uncertainties caused by the perturbation of the input inductor and the output capacitor are represented with some bounded approximation errors, by the utilization of a Takagi–Sugeno (T–S) fuzzy modeling approach. Based on the represented model of the boost converter, a new type of sliding surface is designed depending on the duty cycle and reference inputs of the converter. Then, a dynamic SMC law is designed, by considering that the perturbation of the uncertain parameters, including input inductor, output capacitor, load resistor, and input voltage, is bounded. Meanwhile, we adopt an exponential plus power approaching law in the sliding mode controller for fast reachability of the sliding surface and a small chattering in the duty cycle input. Moreover, in terms of the considered uncertainties, a novel IT2FNN-based dynamic SMC law is derived, by applying simplified ellipsoidal-type membership functions in the type-2 fuzzy neural network. To improve the capacity to manage the uncertainties, some online learning algorithms for the updating of the IT2FNN are designed by a gradient descent method (GDM), without the requirement of the boundedness of the uncertainties. The resulting tracking error system is synthesized to be bounded stable based on the designed IT2FNN-based dynamic SMC. Finally, the effectiveness of the proposed adaptive IT2FNN-based dynamic SMC method is verified by some comparative simulation results. [ABSTRACT FROM AUTHOR]

Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance. [ABSTRACT FROM AUTHOR]