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This study presents an adaptive sliding mode control (SMC) scheme for the output voltage regulation of the three‐phase AC/DC voltage source converters, based on interval type‐2 (IT2) Takagi–Sugeno (T–S) fuzzy model. Parameters of the whole system are considered with bounded perturbations, and the non‐linearities together with corresponding uncertain parameters existing in the input term are dealt with lower and upper membership functions of type‐2 firing intervals. SMC technique is employed to control the output capacitor voltage while keeping the power factor close to one, using the T–S fuzzy model. An integral sliding mode surface, in terms of the error dynamics generated by the system states and their references, is first designed with normalised central membership functions. Then, adaptive technique is applied to compensate the uncertain coefficients in the IT2 membership grades. Finite‐time reachability of the sliding mode is guaranteed under the resulting adaptive SMC. Finally, the robust and effective tracking performance of the proposed controller is tested and validated in the presence of the uncertain parameter perturbations. [ABSTRACT FROM AUTHOR]

This study proposes an extended state observer‐based sliding mode control (ESO‐SMC) strategy for trajectory tracking of a four mecanum wheeled mobile platform (FMWMP) with unknown disturbances and model uncertainties (UDMU) considered. Especially, the extended state observer (ESO) is designed to estimate not only the UDMU but also the unmeasured velocities of FMWMP. Based on the designed ESO, a sliding mode control (SMC) scheme is utilised to ensure the tracking performance as expected. By using Lyapunov synthesis, it is shown that all the signals of the whole system can be guaranteed to be uniformly ultimately bounded. To verify the effectiveness of the proposed control strategy, simulations and experiments are carried out with two different kinds of reference trajectories. Furthermore, a comparative work is done to show that the ESO‐SMC controller has better control performance than traditional proportional–integral–derivative controller. [ABSTRACT FROM AUTHOR]