Model reference adaptive control for robotic manipulators without velocity measurements

1994 ◽  
Vol 8 (3) ◽  
pp. 279-285 ◽  
Author(s):  
Howard M. Schwartz
Keyword(s):  
Adaptive Control ◽  
Robotic Manipulators ◽  
Model Reference Adaptive Control ◽  
Velocity Measurements ◽  
Model Reference ◽  
Model Reference Adaptive

scholarly journals Adaptive Control Design and Stability Analysis of Robotic Manipulators

Actuators ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 89 ◽  
Author(s):  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Stability Analysis ◽  
Motion Control ◽  
Force Control ◽  
Control Design ◽  
Robotic Manipulators ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
Model Reference Adaptive

In this paper, the author presents the adaptive control design and stability analysis of robotic manipulators based on two main approaches, i.e., Lyapunov stability theory and hyperstability theory. For the Lyapunov approach, the author presents the adaptive control of a 2-DOF (degrees of freedom) robotic manipulator. Furthermore, the adaptive control technique and Lyapunov theory are subsequently applied to the end-effector motion control and force control, as in most cases, one only considers the motion control (e.g., position control, trajectory tracking). To make the robot interact with humans or the environment, force control must be considered as well to achieve a safe working environment. For the hyperstability approach, a control system is developed through integrating a PID (proportional–integral–derivative) control system and a model reference adaptive control (MRAC) system, and also the convergent behavior and characteristics under the situation of the PID system, model reference adaptive control system, and PID+MRAC control system are compared.

Analysis and a Case Study of Model Reference Adaptive Control for Robotic Mechanisms

2016 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Robotic Manipulators ◽  
Model Reference Adaptive Control ◽  
Future Research ◽  
Model Reference ◽  
Robotic Arms ◽  
Hybrid Controller ◽  
Effect Model ◽  
Model Reference Adaptive

Motion control accuracy of robotic manipulators affects the overall robotic system performance. When the end-effector grasps different payloads, the joint motion of robotic manipulators will vary depending on the different payload masses. Traditional controllers have the problem of not being able to compensate the payload variation effect. Model reference adaptive control has been proposed to address the above problem. This paper discusses the model reference adaptive control of robotic manipulators initially raised by Roberto Horowitz and its associated development by other authors. A case study for a hybrid controller, which is derived from the model reference adaptive control system, is presented. Very few recent papers can be found in the area of model reference adaptive control of robotic manipulators, and this study can provide a guideline for future research in the direction of model reference adaptive control for robotic arms.

Review and Discussion on Model Reference Adaptive Control for Mechanical Mechanisms

2017 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Adaptive Control ◽  
Robotic Manipulators ◽  
Model Reference Adaptive Control ◽  
Coupled Systems ◽  
Future Research ◽  
Model Reference ◽  
Highly Nonlinear ◽  
Ideal Solutions ◽  
Self Tuning ◽  
Model Reference Adaptive

Traditional control systems are not able to properly balance out the load variation impact when robotic mechanisms carry and transport a variety of payloads. Adaptive control, particularly the model reference adaptive control (MRAC), is one of the ideal solutions that one can resort to address the mentioned problem. Adaptive control can be categorized into the following, model reference, self-tuning and gain-scheduled. Here, the authors mainly focus on the MRAC category. To the best of the authors’ knowledge, not so many recent papers can be found on MRAC for robotic manipulators because robotic manipulators are usually highly nonlinear and coupled systems, and sometimes it is not easy to design a stable MRAC in the robotic systems. This paper reviews and discusses the MRAC that is used in robotic manipulators and some issues of MRAC for robotic manipulators are presented as well. This review is able to give a general guideline for the future research in the MRAC of robotic manipulators.

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