Abstract
This work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.
Highlights
In various industrial processes robot manipulators have invaded the mode of technology; they are used to carry out complex and repetitive tasks quickly and efficiently [1, 2]
A new concept of fault detection, isolation, and compensation based on neural networks and time delay control (TDC) has been developed and applied to a SCARArobot
The proposed controller composed of two independent schemes that switches between each other based on the artificial neural network (ANN) results
Summary
In various industrial processes robot manipulators have invaded the mode of technology; they are used to carry out complex and repetitive tasks quickly and efficiently [1, 2]. A methodology has been developed in order to Fault Detection, Isolation and compensate for sensor (FDI) in a robot manipulator, which is based on the concept of residual generation with neural network and compensation with a TDC controller. The main contribution of this paper is summarized as follows: (1) Model-free control scheme is proposed to detect and compensate the effects of sensors defaults Both the TDC and the detection-based ANN are model independent, where only the inputs and outputs were used to achieve the required performances.
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