Design Hybrid Iterative Learning Controller for Directly Driving the Wheels of Mobile Platform against Uncertain Parameters and Initial Errors

In this paper, we develop a hybrid iterative learning controller (HILC) for non-holonomic wheeled mobile platform to achieve trajectory tracking with actual complex constraints, such as physical uncertain parameters, and initial errors. Unlike the traditional (ILC), control variable selects rotation speed of two driving wheels instead forward speed. The considers constraints robot’s motors can effectively handle parameters errors system. Without errors, improve convergence by adding other types error signals; otherwise, achieves designing signal compensation Then, effectiveness proposed is proven relationship between input, output, status signals. Finally, simulations demonstrate that tracked various trajectories directly controlling under constraints. Furthermore, results show did not significantly depend on system’s structural parameters.