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

Research Article Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators Dihya Maincer1 · Moufid Mansour1 · Amar Hamache2 · Chemseddine Boudjedir3 · Moussaab Bounabi4 Received: 3 September 2020 / Accepted: 13 February 2021 © The Author(s) 2021  OPEN 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. Keywords Fault detection isolation (FDI) · Time delay and control (TDC) · Artificial neural network (ANN) · Multilayer perceptron (MLP) · Robot manipulator (SCARA) 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 [1, 2]. 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 [3]. In this respect, fault detection in a robot manipulator arm is necessary for monitoring an effective support in utilization of a manipulators as independent systems [4, 5]. Methods of fault detection and isolation (FDI) are generally founded on the concept of production and residual analysis of the residuals. Many techniques have been assessed in order to be successfully applied. * Dihya Maincer, ; Moufid Mansour, ; Amar Hamache, ; Chemseddine Boudjedir, ; Moussaab Bounabi, | 1Instrumentation and Automatic Control Department, Laboratory of Robots Parallelism Electroénergetic, University Of Science And Technology Houari Boumediene, Algiers, Algeria. 2Laboratory of Vision Artificial and Automatic Systems, University Mouloud Mammeri Tizi-Ouzou, Tizi Ouzou, Algeria. 3Laboratory of Process and Control, Polytechnic National School, Algiers, Algeria. 4Photovoltaic Communication and Conversion Devices Laboratory, Polytechnic National School, Algiers, Algeria. SN Applied Sciences (2021) 3:424 | https://doi.org/10.1007/s42452-021-04376-z Vol.:(0123456789) Research Article SN Applied Sciences (2021) 3:424 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 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 experiments have been developed to compensate for fault such as robust control algorithms, including synchronization control [11], artificial neural networks (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 of identifying the parameters or adjusting 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 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 [15] has been consolidated to allow quick tailoring of switching gains, which improves tracking performance compared to a conventional TDC. In particular, the use of fixed control gains from the TDC allows to ameliorate a performance of the system and the rapid adaptation of Vol:.(1234567890) | https://doi.org/10.1007/s42452-021-04376-z the gain [11]. TDC control combined with sliding mode [22, 23], requires a gain adjustment. However, the adaptive time delay control (ATDC) adaptation law does not directly reflect current tracking errors or sliding va (...truncated)