# Review of Fault-Tolerant Control Systems Used in Robotic Manipulators
## 2 Artificial Intelligence Methods in FTCS
The work described in [26] is another example of the application of artificial intelligence methods in fault identification. In this case, neural networks and machine learning were used in control, where fuzzy logic was used to tune the proposed method. In [27], the authors proposed the use of a dual neural network (DNN) [28] for fault diagnosis and fault tolerance. This was the first study found in the literature to present the possibility of controlling a redundant manipulator when more than two axes fail. The paper presented the realization of the task of chalking a circle when as many as three of the available seven axes fail at different time periods. Prior to this, the control task at the time of axis failure was to exclude a single axis or a single sensor. The authors of [29] proposed and compared two approaches to FD error detection in a welding robot based on the prediction of accelerations of individual axes. The first acceleration prediction approach to fault detection was the use of a self-organizing map neural network (SOMNN). This network is shown in Figure 4a. The second approach used a radial basis function neural network (RBFNN), which is shown in Figure 4b. The paper demonstrated the feasibility of using RBFNN and SOMNN to predict the acceleration of individual axes for either detecting robot faults or predicting the remaining life of the device. The authors of [30] presented decentralized control strategies based on a radial basis function neural network (RBFNN). In this control method, each joint was treated as a separate subsystem. A fault in the joint was detected using a velocity observer. The presented law of adaptive control was based on information from individual joints. It can be applied to any robotic arm configuration. \[u_{a}=W^{T}(e_{1}\dot{e}_{1})\Theta, \tag{2}\]4 (SFT-PID-NFTSM) with (TDE) [25]. \[u_{a}=W^{T}(e_{1}\dot{e}_{1})\Theta, \tag{2}\]5 (**a**) Self-organizing map neural network (SOMNN) [29]. (**b**) Radial basis function neural network (RBFNN) [29]. \[u_{a}=W^{T}(e_{1}\dot{e}_{1})\Theta, \tag{2}\]6 Control architecture with observer (ANHVSO) and controller (AMFBVSC) [26]. The authors of [31] proposed a radial basis function neural network (RBFNN) for estimating system failures in order to avoid a diagnostic error. The task of the neural network was to compensate for the external disturbances and actuator failures, which were recognized in the proposed method through an adaptive disturbance observer. In [32], an adaptive fixed-time fault-tolerant constraint control (AFTFTCC) for trajectory tracking was proposed. The system was tested by the authors on two manipulators working together. The described method differs from other methods in that it does not need components such as a state estimator or a fault observer. The method used (RBFNN) to tease out the upper limit of uncertainty. The authors of [33] presented a control system that is robust to four types of faults: free-swinging joint faults, locked joint faults, incorrectly measured joint positions, and incorrectly measured joint velocities. Artificial neural networks in the form of MLPs and RBFNs were used to detect free-swinging defects and locked robot axes. The operation of neural networks in this solution was based on the detection of failures by the MLP, where each axis was represented by a separate MLP. The information in the form of a mapped error vector was then passed to the RBFNN, which used the vector to classify the error information. Due to the fact that the proposed control system detects the fault and then isolates it and reconfigures the controller, it was able to control the arm in case of multiple faults. However, this was not confirmed by tests in this work. The described control system is shown in \[u_{a}=W^{T}(e_{1}\dot{e}_{1})\Theta, \tag{2}\]7