Due to the under-actuated and strong coupling characteristics of quadrotor aircraft, traditional trajectory tracking methods have low control precision, and poor anti-interference ability. A novel fuzzy proportional-interactive-derivative (PID)-type iterative learning control (ILC) was designed for a quadrotor unmanned aerial vehicle (UAV). The control method combined PID-ILC control and fuzzy control, so it inherited the robustness to disturbances and system model uncertainties of the ILC control. A new control law based on the PID-ILC algorithm was introduced to solve the problem of chattering caused by an external disturbance in the ILC control alone. Fuzzy control was used to set the PID parameters of three learning gain matrices to restrain the influence of uncertain factors on the system and improve the control precision. The system stability with the new design was verified using Lyapunov stability theory. The Gazebo simulation showed that the proposed design method creates effective ILC controllers for quadrotor aircraft.

A novel iterative learning control (ILC) algorithm for a two-wheeled self-balancing mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties is presented to resolve the trajectory tracking problem in this research. A kinematics model and dynamic model of a two-wheeled self-balancing mobile robot are deduced in this paper, and the combination of an open-closed-loop PD-ILC law and a variable forgetting factor is presented. The open-closed-loop PD-ILC algorithm adopts current and past learning items to drive the state variables and input variables, and the output variables converge to the bounded scope of their desired values. In addition, introducing a variable forgetting factor can enhance the robustness and stability of ILC. Numerous simulation and experimental data demonstrate that the proposed control scheme has better feasibility and effectiveness than the traditional control algorithm.

A new iterative learning control (ILC) approach combined with an open-closed-loop PD scheme is presented for a flexible manipulator with a repeatable motion task in the case that only the endpoint pose of the flexible link is measurable. This approach takes advantage of the fact that the ILC performance is independent of the model used, thereby overcoming the drawback of the heavy reliance of PD controllers on the modeling accuracy. The open-closed-loop PD controller is mainly used to simultaneously reduce the effects of the modeling error and disturbances to enhance the controller's robustness. Meanwhile, an angular correction term is introduced by using the angular relationship of the system outputs to reward or penalize the ILC law. Specifically, when the current output tends toward the expected trajectory, the ILC law is enhanced accordingly; otherwise, it is penalized. The convergence conditions for the proposed approach are obtained through theoretical analysis, and experiments using a real flexible manipulator are presented. The results show that the proposed ILC scheme can overcome the impact of the endpoint error caused by link flexibility and has a good control effect. INDEX TERMSFlexible manipulator, angular relationship, PD, open-closed-loop, iterative learning control.

An open-closed-loop iterative learning method for multiple flexible manipulator systems with repeatable motion tasks was proposed to achieve the consensus tracking of a specified desired reference trajectory. The open-closed-loop iterative learning control scheme was used to reduce the effects of model error and disturbances, as the boundedness of both the tracking error and the control input can be simultaneously guaranteed. In addition, when combined with a novel rotational joint with a continuously adjustable stiffness, the open-closed-loop iterative learning method enhances the adaptability to meet the strict requirements of the next generation of robots with the physical human-robot interaction and highly dynamic motion. The convergence conditions of the approach were obtained by the theoretical analysis. The simulation results show that this control algorithm has a good tracking accuracy and a fast convergence rate when used in the high-precision trajectory control for robots. INDEX TERMSMultiple flexible robot, open-closed-loop, iterative learning control, stiffness adjustment.

We propose an iterative learning control algorithm (ILC) that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.