Adaptive boundary iterative learning vibration control using disturbance observers for a rigid–flexible manipulator system with distributed disturbances and input constraints

Abstract

In this article, an adaptive boundary iterative learning vibration control is developed for a class of the rigid–flexible manipulator system under distributed disturbances and input constraints. With the help of the virtual work principle, the dynamics of the rigid–flexible manipulator are modeled and described by coupled ordinary differential equations and partial differential equations. Based on the original infinite dimension system model, three compounded adaptive boundary iterative learning vibration control laws are constructed with disturbance observers and adaptive vibration laws, aiming to track the desired joint angular positions and achieve vibration suppression simultaneously. Three disturbance observers are proposed to determine the upper bounded approximation of the unknown external disturbances. Hyperbolic tangent and saturation functions are incorporated into adaptive vibration laws to handle input constraints. It is proved by Lyapunov–Krasovskii-like composite energy functions that elastic vibrations and tracking errors can asymptotically converge to zero along the iteration axis. Finally, the efficacy of the developed adaptive boundary iterative learning vibration control approach is illustrated by the simulation results under distributed disturbances and input constraints.

Cascade Proportional Derivative Controller For A Flexible Link Robot Manipulator Using The Bees Algorithm

“…Different controllers are used in flexible link control [4,5]. PID controllers are one of the most used structures in controlling systems, thanks to their efficiency and durability [6][7][8][9].…”

Iterative Learning Tracking Control for a Vehicle-Manipulator System With Input Constraint

“…Besides, the mobile flexible manipulator is applied broadly in repetitive works and possesses a good prospect of application that has been applied to different scenarios like logistics material handling, machine loading and unloading and so on. As a kind of highly reliable control method, iterative learning control (ILC) has attached more attention from scholars in different areas and widely used in control study of robotic manipulators, high-speed trains, flexible string and other distributed parameter systems (DPSs) [12]- [19]. For example, In [12,13], a class of the rigid-flexible manipulator systems with input constraints are researched, ILC integrated with boundary control, disturbance observer and adaptive technology is applied to handle with the vibration control and input constraint problems.…”

“…As a kind of highly reliable control method, iterative learning control (ILC) has attached more attention from scholars in different areas and widely used in control study of robotic manipulators, high-speed trains, flexible string and other distributed parameter systems (DPSs) [12]- [19]. For example, In [12,13], a class of the rigid-flexible manipulator systems with input constraints are researched, ILC integrated with boundary control, disturbance observer and adaptive technology is applied to handle with the vibration control and input constraint problems. In [14], the operation control of high-speed trains with velocity and displacement constraints is considered and a novel ILC scheme based backstepping design is constructed.…”

Observer-based independent joint control for a coupled rigid-flexible manipulator with actuator saturation based on distributed parameter model

“…An adaptive neural network boundary control and the backstepping technique are researched in Ren et al (2020) for a one-link flexible robot under input constraints. Based on ODEs-PDEs model for rigid-flexible robotic manipulator system, the saturation functions are used to cope with the input constraint Zhou et al (2022). At present, the actuation constraint problem are always solved by using saturation functions, which leads to discontinuity in the controller.…”