Switched time delay control based on neural network for fault detection and compensation in robot

IAES International Journal of Robotics and Automation (IJRA) Vol. 10, No. 2, June 2021, pp. 91~103 ISSN: 2722-2586, DOI: 10.11591/ijra.v10i2.pp91-103  91 Switched time delay control based on neural network for fault detection and compensation in robot Maincer Dihya1, Mansour Moufid 2, Boudjedir Chemseddine3, Bounabi Moussaab4 1,2Department Instrumentation and Automatic, Control Laboratory of Robots Parallelism Electroénergetic, University of Science and Technology Houari Boumediene, Algiers, Algeria 3Laboratory of Process and Control, Polytechnic National School, Algiers, Algeria 4Photovoltaic Communication and Conversion Devices Laboratory, Polytechnic National School, Algiers, Algeria Article Info ABSTRACT Article history: Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command. Received Aug 13, 2020 Revised Feb 2, 2021 Accepted Mar 5, 2021 Keywords: Artificial neural network Fault detection Multi-layer perceptron Robot manipulator (SCARA) Sliding mode control Time delay and control This is an open access article under the CC BY-SA license. Corresponding Author: Maincer Dihya Department Instrumentation and Automatic Control University of Science and Technology Houari Boumediene USTHB, B.P. 32, El-Alia, Bab-Ezzouar, 16 111, Algeria Email: 1. INTRODUCTION 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. They are connected to each other by joints so that the manipulators follow the reference trajectory, where articulations must be precisely controlled. To perform these tasks, the manipulators are usually quite complex which increases their factor for fault. Thus, in order to have a good fault diagnosis on a manipulator, it is necessary to have a precise knowledge of its mathematical model. However, it is very difficult to obtain a precise of model as the modeling of dynamic robot which is not always an obvious task. For this purpose, various problems can arise such as uncertainties, external disturbances, uncertain dynamics, and measurement noises, which cause deterioration of the fault detection performance by causing false alarms [1-3]. In this respect, fault detection in a robotic manipulator is necessary for monitoring and effective support in utilize of a manipulators as independent systems [4, 5]. Methods of defects detection and isolation are generally founded on the concept of production and residual analysis of the residuals. Many techniques have been assessed in order to be Journal homepage: http://ijra.iaescore.com 92  ISSN: 2722-2586 successfully applied. Taking into account the reliability which must be the most important criterion of the operation. These techniques allow reliable decisions to be made without knowledge of the mathematical system model. In this respect, artificial neural networks (ANNs) are suitable for such problems. One of this remarkable cleanliness is their ability to learn from their environment and improve their behavior from learning, in addition to the learning results in an adaptation (adjustment) of the weights and bias of the neural network [6, 7]. Ideally, after each learning step (iteration), performance improves. There are different learning approaches which differ from each other by the way of adjusting the weights and their structure depends on the architecture of the neural network and the task to be performed. Besides, neural networks have been searched and carried out in real systems [8, 9]; there are many ANN applications in data analysis, identification, and model control [10]. Amid various types of ANN, a multi-layer perceptron (MLP) is quite popular and used extensively in research. In order to achieve good fault compensation, controllers capable of effectively compensating for faults are necessary to enable them to perform their task independently and realistically. In this regard, numerous works have been developed to compensate for defects such as robust control algorithms, including synchronization control [11], ANN [12, 13], sliding mode control (SMC) [14, 15] and time delay control (TDC) [16, 17]. SMC are well known for their robustness against unknown systems dynamics. To eliminate external perturbations and nonlinear dynamics with delay signal, handy nonlinear control strategies for unmodeled disturbances have been developed, for example TDC. This last, its principal objective is to use past observation of the system response as a control input at the present time to immediately change the control actions instead than identify the parameters or adjust the controller gain of the control system, which leads to an independent model controller i.e., compensation without any use of dynamic model [18]. On the other hand, the big disadvantage of TDC is undesirable tracking errors and time delay estimation (TDE) errors. To compensate errors for TDE, many procedures have been tested by combining commands with a TDC. An auxiliary control [16-19] has been selected to settle its gains adaptively in order to have a switching control scheme. In [20] an auxiliary control in fuzzy sliding mode has also been chattering using fuzzy rules. In general, we caused that several works were realized by combining TDC and neural networks [21]. In other words, in order to eliminate TDE errors, a SMC [22] has been consolidated to allow quick tailoring of switching gains, which improves tracking performance compared to a conventional TDC. Especially, the use of fixed control gains from the TDC allows to ameliorate a performance of the system and the rapid adaptation of the gain [23]. TDC control combined with sliding mode [24], requires a gain adjustment. However, the TDCs adaptation law does not directly reflect current tracking errors or sliding variables, which leads to a slow convergence rate. Thus, it would be useful to develop a TDC control combined with sliding mode, wh (...truncated)