Hybrid Control of Robot Manipulator without Force Sensor

Generally, hybrid control is realized by sensor signal feedback of position and force. However, some robot manipulators do not have a force sensor due to the environment. Moreover, a precise force sensor is very expensive. In order to overcome these problems, we propose the estimation system of reaction force without using a force sensor. This system consists of the torque observer and the inverse dynamics calculation. Using both this force estimation system and <I>H</I>∞ acceleration controller which is based on <I>H</I>∞ control theory, it takes into account the frequency characteristics of both sensor noise effect and disturbance rejection. The experimental results in this paper illustrate the fine hybrid control of the three tested degrees-of-freedom DD robot manipulator without force sensor.

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Educational Force Control Using a Modular 2-DOF Serial Robot Manipulator and Low-Cost 2-DOF Force Sensor

Volume 3, Rapid Fire Interactive Presentations: Advances in Control Systems; Advances in Robotics and Mechatronics; Automotive and Transportation Systems; Motion Planning and Trajectory Tracking; Soft Mechatronic Actuators and Sensors; Unmanned Ground and Aerial Vehicles ◽

10.1115/dscc2019-9245 ◽

2019 ◽

Author(s):

Stephen Mascaro

Keyword(s):

Force Control ◽

Degrees Of Freedom ◽

Hybrid Control ◽

Robot Manipulator ◽

Impedance Control ◽

Low Cost ◽

Force Sensor ◽

Physical Contact ◽

Dynamic Force ◽

Control Techniques

Abstract This paper describes a modular 2-DOF serial robotic system and accompanying experiments that have been developed to instruct robotics students in the fundamentals of dynamic force control. In prior work, we used this same robot to showcase and compare the performance of a variety of textbook techniques for dynamic motion control (i.e. fast/accurate trajectory tracking using dynamic model-based and robust control techniques). In this paper we now add a low-cost 3D-printed 2-DOF force sensor to this modular robot and demonstrate a variety of force control techniques for use when the robot is in physical contact with the environment. These include stiffness control, impedance control, admittance control, and hybrid position/force control. Each of these various force control schemes can be first simulated and then experimentally implemented using a MATLAB/Simulink real-time interface. The two-degrees of freedom are just enough to demonstrate how the manipulator Jacobian can be used to implement directional impedances in operational space, and to demonstrate how hybrid control can implement position and force control in different axes. This paper will describe the 2-DOF robot system including the custom force sensor, illustrate the various force control methods that can be implemented, and demonstrate sample results from these experiments.

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Adaptive Hybrid Control for a Robot Manipulator Interacting with Uncertain Flexible Environments

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)57668-5 ◽

1996 ◽

Vol 29 (1) ◽

pp. 235-240 ◽

Cited By ~ 1

Author(s):

Jianqing Wu ◽

Zhiwei Luo ◽

Masaki Yamakita ◽

Koji Ito

Keyword(s):

Hybrid Control ◽

Robot Manipulator

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Hybrid Control Schemes Using Input Shaping And Full–State Feedback For A Flexible Robot Manipulator

Jurnal Teknologi ◽

10.11113/jt.v49.203 ◽

2008 ◽

Vol 49 (1) ◽

Author(s):

Mohd Ashraf Ahmad ◽

Herman Wahid ◽

Zaharuddin Mohamed

Keyword(s):

State Feedback ◽

Hybrid Control ◽

Robot Manipulator ◽

Input Shaping ◽

Flexible Robot ◽

Control Schemes ◽

Full State ◽

Full State Feedback

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Hammerstein modeling and hybrid control of force and position for a novel integration of actuating and sensing ionic polymer metal composite gripper

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220961127 ◽

2020 ◽

pp. 095440622096112

Author(s):

Hao Wang ◽

Jinhai Gao ◽

Yang Chen ◽

Lina Hao

Keyword(s):

Hybrid Control ◽

Critical Factor ◽

Force Sensor ◽

Hammerstein Model ◽

Metal Composite ◽

Ionic Polymer Metal Composite ◽

Linear Portion ◽

Ionic Polymer ◽

Output Displacement ◽

Polymer Metal

An innovative integration of actuating and sensing ionic polymer metal composite (IPMC) gripper is proposed and fabricated in this paper. The IPMC gripper is composed of a stationary copper finger and an IPMC finger attached to a force sensor. In order to make IPMC gripper useful in bio-manipulation application, control strategy is a critical factor to resist nonlinear characteristic of IPMC. Hammerstein model of IPMC output displacement is constructed with static nonlinear portion and dynamic linear portion. We utilize creep operator superposition and auto-regression (ARX) models to represent static nonlinear and dynamic linear portions respectively by modeling methods based on data. Then a novel control scheme is proposed and designed using inverse creep compensator for static nonlinear portion and uncertainty state feedback robust control based on state observer for dynamic linear portion. When IPMC reaches a constant displacement to grasp a miniature object, the grasping force may not be provided enough to complete grasping task. Finally, hybrid control of force and position strategy for IPMC gripper is conducted and realized on physical experimental platform. The experimental results demonstrate the effectiveness of control system to guarantee stable manipulation.

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Integrating a Grasp Exoskeleton Into a String-Based Interface for Human-Scale Interactions

Journal of Computing and Information Science in Engineering ◽

10.1115/1.3006304 ◽

2008 ◽

Vol 8 (4) ◽

Author(s):

Rasul Fesharakifard ◽

Maryam Khalili ◽

Laure Leroy ◽

Alexis Paljic ◽

Philippe Fuchs

Keyword(s):

Virtual Environments ◽

Degrees Of Freedom ◽

Force Feedback ◽

Control Method ◽

Hybrid Control ◽

Force Sensor ◽

Virtual Objects ◽

Rotary Encoder ◽

Human Scale ◽

Novel Design

A grasp exoskeleton actuated by a string-based platform is proposed to provide the force feedback for a user’s hand in human-scale virtual environments. The user of this interface accedes to seven active degrees of freedom in interaction with virtual objects, which comprises three degrees of translation, three degrees of rotation, and one degree of grasping. The exoskeleton has a light and ergonomic structure and provides the grasp gesture for five fingers. The actuation of the exoskeleton is performed by eight strings that are the parallel arms of the platform. Each string is connected to a block of motor, rotary encoder, and force sensor with a novel design to create the necessary force and precision for the interface. A hybrid control method based on the string’s tension measured by the force sensor is developed to resolve the ordinary problems of string-based interface. The blocks could be moved on a cubic frame around the virtual environment. Finally the results of preliminary experimentation of interface are presented to show its practical characteristics. Also the interface is mounted on an automotive model to demonstrate its industrial adaptability.

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A Modular 2-DOF Serial Robot Manipulator for Education in Robot Control

Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control ◽

10.1115/dscc2016-9878 ◽

2016 ◽

Author(s):

Stephen Mascaro

Keyword(s):

Robot Control ◽

Inverse Dynamics ◽

Position Control ◽

Sliding Mode ◽

Feedforward Control ◽

Robot Manipulator ◽

Impedance Control ◽

Force Sensor ◽

Control Techniques ◽

Serial Robot

This paper describes a modular 2-DOF serial robot manipulator and accompanying experiments that have been developed to introduce students to the fundamentals of robot control. The robot is designed to be safe and simple to use, and to have just enough complexity (in terms of nonlinear dynamics) that it can be used to showcase and compare the performance of a variety of textbook robot control techniques including computed torque feedforward control, inverse dynamics control, robust sliding-mode control, and adaptive control. These various motion control schemes can be easily implemented in joint space or operational space using a MATLAB/Simulink real-time interface. By adding a simple 2-DOF force sensor to the end-effector, the robot can also be used to showcase a variety of force control techniques including impedance control, admittance control, and hybrid force/position control. The 2-DOF robots can also be used in pairs to demonstrate control architectures for multi-arm coordination and master/slave teleoperation. This paper will describe the 2-DOF robot and control hardware/software, illustrate the spectrum of robot control methods that can be implemented, and show sample results from these experiments.

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Force Control of Robot Manipulator by Neural Network Model - Experiment and Evaluation of One-Degree-of-Freedom Manipulator -

Journal of Robotics and Mechatronics ◽

10.20965/jrm.1990.p0273 ◽

1990 ◽

Vol 2 (4) ◽

pp. 273-281 ◽

Cited By ~ 12

Author(s):

Masatoshi Tokita ◽

◽

Toyokazu Mitsuoka ◽

Toshio Fukuda ◽

Takashi Kurihara ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Force Control ◽

Pid Controller ◽

Robot Manipulator ◽

Force Sensor ◽

Robotic Manipulator ◽

The Neural Network ◽

The Stability

In this paper, a force control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of both the force sensor and objects. This proposed system consists of the standard PID controller, the gains of which are augmented and adjusted depending on objects through a process of learning. The authors proposed a similar method previously for the force control of the robotic manipulator with consideration of dynamics of objects, but without consideration of dynamics of the force sensor, showing only simulation results. This paper shows the similar structure of the controller via the neural network model applicable to the cases with consideration of both effects and demonstrates that the proposed method shows the better performance than the conventional PID type of controller, yielding to the wider range of applications, consequently. Therefore, this method can be applied to the force/compliance control problems. The effects of the number of neurons and hidden layers of the neural network model are also discussed through the simulation and experimental results as well as the stability of the control system.

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