Abstract
A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.
Highlights
An industrial robot manipulator frequently works at high velocities to reach its desired position
The algorithm proposed has been implemented to compensate for the proportional derivative (PD) control of a two-degree-of-freedom robot manipulator (TDOFRM)
A PD control was selected because the common knowledge that if designed with gravity compensation, it can reach asymptotic stability
Summary
An industrial robot manipulator frequently works at high velocities to reach its desired position. The adaptive controller is implemented to estimate unstructured uncertainties and error reconstruction In He et al (2018), one Radial Basis Function Neural Network (RBFNN) is used to estimate the unknown dynamics robotic manipulator. Gandolfo et al (2019) propose a control scheme that combines a classical PD and a robust adaptive compensator based on NNs. adaptive controllers are addressed for systems with non-linear and uncertainties dynamics, their slow convergence can lead to performance degradation or even affect operational safety. In Wang et al (2012), the authors proposed an RBFNN to compensate for nonlinear dynamics of the robotic manipulator and a robust control designed to suppress the modeling error of NN. A PD control with a scheme based on NNs in cascade is designed to manage the compensation of uncertainties in a robot manipulator.
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