This study aims to achieve the trajectory-tracking of an autonomous differential drive mobile robot (ADDMR) in the presence of friction torques using the proposed Fuzzy Sliding-Mode Control (FSMC). First, the complete model of the ADDMR is established including kinematic, dynamic and actuator models. Then, the desired trajectory is planned and generated via Bézier curve combined with cubic time mapping. The localization of the ADDMR is performed using the Monte Carlo Localization (MCL) technique and LiDAR point cloud data. Subsequently, the proposed FSMC utilizes nonlinear sliding surfaces inferred based on fuzzy logic to asymptotically attain the pose convergence. To investigate the feasibility and robustness of the proposed FSMC, simulations and experiments are conducted under different operation conditions. The results show that the proposed FSMC using LiDAR data in the feedback leads to excellent control performance even in the presence of friction torques. It was also shown through simulations and experiments that the established ADDMR model fits the actual ADDMR model very well.

Control of air–fuel ratio (AFR) plays a key role in the minimisation of the carbon dioxide and harmful pollutant emissions and maximisation of fuel economy. An inherent time‐varying delay existing in lean‐burn spark ignition (SI) engines is a major challenge for the AFR control. Herein an unstable internal dynamics with a parameter dependent system caused by time delay is established to represent a dominating feature of AFR. The proposed control scheme, LPV‐based fuzzy control technique, combines the features of LPV and fuzzy control to deal with the unstable internal dynamics of an AFR system with external disturbances and a high level of uncertainty in system parameters. Based on the desired error dynamics, an LPV dynamic error consisting of the unstable state and the AFR tracking error is determined. Subsequently, the proposed fuzzy control algorithm through a look‐up table is used to stabilise the LPV dynamic error. Then, the tracking error moves along the desired error dynamics towards zero. The system stability is assured via Lyapunov stability criteria. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed control scheme under different operating conditions. Also, compared with the baseline controller, i.e. proportional–integral controller with Smith predictor, demonstrates its superiority.

The work presented here deals with the trajectory-tracking control of omnidirectional mobile robots using a novel robust control technique. The aim is to achieve good tracking performance in the presence of frictional forces and torques, uncertainties, and saturated control inputs. First, a dynamic model of the omnidirectional mobile robot incorporating motor dynamics and frictional forces and torques is derived leading to a higher order system model. Subsequently, a novel cascaded sliding-mode tracking control scheme is proposed to perform trajectory-tracking with the omnidirectional mobile robot, which includes direct and indirect sliding-mode controllers in a cascaded structure. The indirect sliding-mode controller is utilized to attain tracking of the indirect reference input (i.e., desired trajectory) in the first subsystem and constitutes a direct reference input for the second one. A direct sliding-mode controller (i.e., actual control) is then designed to achieve tracking of the direct reference input. This way, the indirect reference input can be effectively manipulated through the proposed cascaded sliding-mode tracking control and individual subsystems are guaranteed to have not only Uniform Ultimate Boundedness but also asymptotic convergent performance. The stability of the closed-loop system is demonstrated via the Lyapunov stability criteria and validated through simulations. It is shown that the cascaded sliding-mode tracking control leads to good trajectory-tracking performance in the presence of uncertainties, friction, and saturated control input.