On the Development of Learning Control for Robotic Manipulators

Abstract

Learning control for robotic manipulators has been developed over the past decade and to the best of the authors' knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

A Tutorial on Robust Control, Adaptive Control and Robust Adaptive Control—Application to Robotic Manipulators

“…As a side note, learning control of robotics is quickly developed after the development of adaptive control of robotic manipulators. This is mainly used to address the problem of joint friction and other uncertainties [22][23][24][25][26][27][28][29][30][31][32][33]. We will not elaborate on each of the references cited.…”

“…In [29], a control system was developed by combining the model-based adaptive control, repetitive learning control and PD control in which the model-based adaptive control input dominates over the other inputs. In [33], the overall development of the learning control for robotic manipulators was presented, and some advantages and disadvantages of the current learning approaches are given. Details can be found in those references.…”

“…Details can be found in those references. Furthermore, the sliding mode control issue is studied in [33], in which the sliding mode control system design based on input-output stability and a computed torque approach for robots are studied. Furthermore, some difficulties in sliding mode control in real time are described in [34][35][36][37][38][39][40][41][42].…”

A constrained control-planning strategy for redundant manipulators

“…Recent advances in artificial intelligence (AI) and improvements in hardware have led to the development of sophisticated robotic systems. Industrial robots performing tasks on factory production lines since the 1970s have evolved to be extremely dexterous, have increased learning capabilities and precise movements [1]. The newer types of robots with humanoid shapes or more practical configurations are mobile and designed to perform tasks in cluttered and dynamic environments, which demand robust and safe behavior.…”

Smart Sensors and Devices in Artificial Intelligence

“…For example, among the applications in artificial intelligence is the learning control currently under developing. By using a learning control, uncertainties and variations can be effectively handled [ 1 ], whereas these cannot be handled traditional control systems. From another perspective, driving a vehicle at night can be used as an example.…”