Abstract
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.
Highlights
Two-wheeled self-balancing mobile robots [1,2,3] are naturally unstable systems with characteristics such as nonlinear, multivariable, time-varying, and incomplete under-actuativity, and these characteristics can be used to study variable control algorithms
Kazuo Yamafuji first introduced the concept of a two-wheeled self-balancing robot in the nineteen eighties
Zhou [19] presents an estimation and compensation learning control of statedependent nonlinearity for EID-based modified repetitivecontrol system to improve the tracking performance. This algorithm is extended to include the open-closedloop PD-iterative learning control (ILC) scheme [20,21,22] for two-wheeled self-balancing mobile robot systems to improve the performance of the algorithm
Summary
Two-wheeled self-balancing mobile robots [1,2,3] are naturally unstable systems with characteristics such as nonlinear, multivariable, time-varying, and incomplete under-actuativity, and these characteristics can be used to study variable control algorithms. Zhou [19] presents an estimation and compensation learning control of statedependent nonlinearity for EID-based modified repetitivecontrol system to improve the tracking performance. This algorithm is extended to include the open-closedloop PD-ILC scheme [20,21,22] for two-wheeled self-balancing mobile robot systems to improve the performance of the algorithm. To reduce buffeting caused by robot system uncertainty and disturbances, this control law has been improved by the introduction of a variable forgetting factor, which can dynamically and precisely compensate for system errors This combination stabilizes the robot's balance and maintains the motion of the robot to track the desired trajectory.
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