Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Abstract

Four-wheeled, individual-driven, nonholonomic structured mobile robots are widely used in industries for automated work, inspection and exploration purposes. The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure. The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots. However, there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion. As a result, the mobile robot has limited performance, such as chattering during curved movement. In this research work, a three-tiered adaptive robust control with fuzzy parameter estimation, including dynamic modeling, direct torque control and wheel slip control is proposed. Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller (ARC) parameters and tracking the trajectories with less tracking error as well as high tracking accuracy. This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique. Our suggested approach outperforms the existing control methods such as Fuzzy, proportional–integral–derivative (PID) and adaptive robust controller with discrete projection (ARC–DP). The experimental results show that the scheduled performance index decreases by 2.77% and 4.76%. All the experimental simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.