SVM-Based Control System for a Robot Manipulator

Abstract

Real systems are usually non‐linear, ill‐defined, have variable parameters and are subject to external disturbances. Modelling these systems is often an approximation of the physical phenomena involved. However, it is from this approximate system of representation that we propose ‐ in this paper ‐ to build a robust control, in the sense that it must ensure low sensitivity towards parameters, uncertainties, variations and external disturbances. The computed torque method is a well‐established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose the estimation of the dynamics model of the nonlinear system with a machine learning regression method. The output of this regressor is used in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator. In cases where some of the parameters of the plant undergo a change in their values, poor performance may result. To cope with this drawback, a fuzzy precompensator is inserted to reinforce the SVM computed torque‐based controller and avoid any deterioration. The theory is developed and the simulation results are carried out on a two‐degree of freedom robot manipulator to demonstrate the validity of the proposed approach

Minimizing torque requirements in robotic manipulation through elastic elements optimization in a physics engine

“…We calculate τ l using (10) and normalize the root mean squared error using the standard deviation of the recorded real-world load torques. 29 We choose to minimize the error between recorded load torques as this encapsulates joint velocity, joint acceleration, and actuator torque. Furthermore, as described in the “Actuator energy consumption and torque requirements” section, our load torque formula incorporates a real-world characterization of the employed actuators.…”

Minimizing Torque Requirements in Robotic Manipulation Through Elastic Elements Optimization in a Physics Engine

“…We calculate t l using (10) and normalize the root mean squared error using the standard deviation of the recorded real-world load torques. 29 We choose to minimize between recorded load torques as this encapsulates joint velocity, joint acceleration, and actuator torque. Furthermore, as described in the "Actuator energy consumption and torque requirements" section, our load torque formula incorporates a real-world characterization of the employed actuators.…”

A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

“…Support vector machines (SVMs) are an effective machine learning method; they were originally designed for pattern recognition and classification tasks. Due to the good generalization property, SVMs have been successfully used in a wide variety of classification problems in robotics [ 25 ]. Compared to the conventional learning algorithm, SVM classifications may be more accurate than the widely-used alternatives such as classification by maximum likelihood, decision tree and neural network-based approaches [ 26 ].…”