Neural Adaptive Approach-Application to Robot Force Control in an Unknown Environment

2006 ◽  
Vol 18 (5) ◽  
pp. 529-538 ◽  
Author(s):  
Yacine Amirat ◽  
◽  
Karim Djouani ◽  
Mohamed Kirad ◽  
Nadia Saadia ◽  
...  
Keyword(s):  
Force Control ◽  
Reference Model ◽  
Dynamic Properties ◽  
Parallel Robot ◽  
Smooth Transition ◽  
Environment Interaction ◽  
Adaptive Approach ◽  
Neural Network Controller ◽  
Interaction Dynamic ◽  
Robot Force Control

This paper presents an effective neural adaptive approach for robot force control with changing/unknown robot-environment interaction dynamic properties. In this approach, a multilayered neural network controller is trained at first off line from data collected during contact motion in order to perform a smooth transition from free to contact motion. Then, an adaptive process is implemented online through a desired impedance reference model such that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot-environment interaction. The effectiveness of the proposed approach has been evaluated for the force control of a 6 DOF (Degree Of Freedom) C5-links parallel robot executing rectangular peg-in-hole insertions with weak tolerances. The experimental results demonstrate that the robot’s skill improves effectively and force control performances are good even if robot-environment interaction dynamic properties change.

scholarly journals On the Dynamic Properties of Flexible Parallel Manipulators in the Presence of Type 2 Singularities

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Sébastien Briot ◽  
Vigen Arakelian
Keyword(s):  
Optimal Control ◽  
Parallel Manipulator ◽  
Dynamic Properties ◽  
Parallel Manipulators ◽  
Parallel Robot ◽  
Inverse Dynamic ◽  
Suggested Approach ◽  
Singular Configurations ◽  
Optimal Force

In the present paper, we expand information about the conditions for passing through Type 2 singular configurations of a parallel manipulator. It is shown that any parallel manipulator can cross the singular configurations via an optimal control permitting the favorable force distribution, i.e., the wrench applied on the end-effector by the legs and external efforts must be reciprocal to the twist along with the direction of the uncontrollable motion. The previous studies have proposed the optimal control conditions for the manipulators with rigid links and flexible actuated joints. The different polynomial laws have been obtained and validated for each examined case. The present study considers the conditions for passing through Type 2 singular configurations for the parallel manipulators with flexible links. By computing the inverse dynamic model of a general flexible parallel robot, the necessary conditions for passing through Type 2 singular configurations are deduced. The suggested approach is illustrated by a 5R parallel manipulator with flexible elements and joints. It is shown that a 16th order polynomial law is necessary for the optimal force generation. The obtained results are validated by numerical simulations carried out using the software ADAMS.

scholarly journals A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1365
Author(s):  
Yuan Liu ◽  
Song Xu ◽  
Seiji Hashimoto ◽  
Takahiro Kawaguchi
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Reference Model ◽  
Mimo System ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Model Based ◽  
Neural Network Controller ◽  
Highly Nonlinear

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

scholarly journals Two-degree-of-freedom internal model position control and fuzzy fractional force control of nonlinear parallel robot

2019 ◽  
Vol 50 (12) ◽  
pp. 2261-2279 ◽  
Author(s):  
Shuhuan Wen ◽  
Di Zhang ◽  
Baowei Zhang ◽  
Hak Keung Lam ◽  
Hongbin Wang ◽  
...  
Keyword(s):  
Force Control ◽  
Internal Model ◽  
Position Control ◽  
Parallel Robot ◽  
Degree Of Freedom ◽  
Two Degree Of Freedom

The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes

2012 ◽  
Author(s):  
Marco Grasso ◽  
Bianca Maria Colosimo ◽  
Giovanni Moroni
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Reference Model ◽  
Time Window ◽  
End Milling ◽  
Monitoring Methods ◽  
Time Data ◽  
Machining Processes ◽  
Adaptive Approach ◽  
Adaptive Monitoring

In different manufacturing applications the assessment of the health conditions of a machine tool, together with the quality and stability of the process, requires the capability of dealing with response variables described in terms of profile data. In the frame of in-process monitoring of sensor signals this is the case, for instance, of monitoring either series production of large lots of parts or machining processes characterized by cyclic signals, where both the condition of the machine components and the final quality of the worked piece may be correlated with the stability of repeating signal profiles in time. However, as far as real time data acquisition is concerned, and when measurements are performed with high sampling frequency, data are likely to be auto-correlated, and hence it is of fundamental importance to develop adaptive monitoring tools robust with respect to non-steady state conditions. The paper deals with the utilization of profile monitoring approaches for in-process monitoring of manufacturing operations and investigates their applicability to the problem of monitoring auto-correlated signals. In particular Principal Component Analysis (PCA) is applied in combination with an adaptive approach based on a moving time window for continuously revise the reference model is evaluated and discussed. A real case study is used to test the performances of the method: the task is to detect tool chipping and breakage in end milling operations by means of real-time monitoring of cutting force signals. The evolution of tool wear imposes a trend in observed signals which leads to the need for an adaptive approach to properly isolate the breakage event from the slow pattern change due to wear mechanism.

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