Adaptive control techniques and feed forward compensation of periodic disturbances in industrial manipulators

Bottero, Aldo; Gerio, Gian Paolo; Perna, Valerio; Gagliano, Alberto

doi:10.1109/mesa.2014.6935612

Cited by 8 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bottero et al [92] analyzed the compensation for periodic torque disturbances, characteristic of electrical permanent magnet motors. The compensation was obtained as a two-stage adaptive controller.…”

Section: Methodsmentioning

confidence: 99%

Robots in machining

et al. 2019

View full text Add to dashboard Cite

Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task.

show abstract

Section: Methodsmentioning

confidence: 99%

Robots in machining

et al. 2019

View full text Add to dashboard Cite

show abstract

“…A control scheme for the manipulation of two robots is designed, which improves the reliability, safety and intelligence of the robot. Bottero et al [7]used the closed-loop adaptive feed-forward control algorithm to identify the internal model of the disturbance, and a predictive feed-forward compensator is designed to compensate the periodic torque disturbance of permanent magnet motor, which ensures the stability and robustness of the system adaptive compensation technology. Jing et al [8]proposes an adaptive control method based on model error compensation.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fuzzy Control Method for Multi-joint Manipulator Based on Fuzzy Compensation

Zhang¹,

Yang²,

Wang

2023

Preprint

View full text Add to dashboard Cite

The manipulator is an important machine for assisting production and life and has been widely used in various fields. Aiming at the problem that the manipulator control system cannot realize fast and high precision trajectory tracking control when it is disturbed by random and non-linear factors, considering that the data information of the fuzzy system is known and the control variables of the system can be controlled and measured, an adaptive fuzzy control law based on fuzzy compensation is designed by using the approximation characteristics of the fuzzy system. This method can effectively suppress the external disturbance and friction disturbance in the process of manipulator motion, so that it can quickly and accurately track the position and speed of the manipulator. The simulation results of the proposed adaptive robust control method based on fuzzy compensation show that it can well suppress the influence of non-linear factors such as friction, disturbance and load variation, and improve the position tracking accuracy by 64.5% and the speed tracking accuracy by 67.3%.

show abstract

“…As shown, the adaptive and neural network control are not entirely new concepts, however they are still actively researched and extended nowadays as evidenced in [19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Archive of Mechanical Engineering

Woliński

2020

View full text Add to dashboard Cite

This paper deals with two control algorithms which utilize learning of their models' parameters. An adaptive and artificial neural network control techniques are described and compared. Both control algorithms are implemented in MATLAB and Simulink environment, and they are used in the simulation of a postion control of the LWR 4+ manipulator subjected to unknown disturbances. The results, showing the better performance of the artificial neural network controller, are shown. Advantages and disadvantages of both controllers are discussed. The usefulness of the learning algorithms for the control of LWR 4+ robots is discussed. Preliminary experiments dealing with dynamic properties of the two LWR 4+ robots are reported.

show abstract

Adaptive control techniques and feed forward compensation of periodic disturbances in industrial manipulators

Cited by 8 publications

References 5 publications

Robots in machining

Robots in machining

An Adaptive Fuzzy Control Method for Multi-joint Manipulator Based on Fuzzy Compensation

Archive of Mechanical Engineering

Contact Info

Product

Resources

About