###### Abstract
Conversely, if the control gain is lowered, it is affected by disturbances. Therefore, a method to measure the disturbance is needed to reduce the chattering by lowering the control gain. Figure 1: Image data when driving snake robot. Nonlinearity, disturbances, and mathematical model uncertainties are major challenges in robotic system controller design. To solve this issue, researchers are using nonlinear control theory, disturbance observer, robust control, and adaptive control. Among them, intelligent control is being studied in many systems due to the development of computer technology. Among them, the artificial neural network (in this paper, called a neural network) can evade complex mathematical analyses such as high-order systems and can resolve nonlinear problems using multilayer neural networks and nonlinear activation functions [38; 39; 40; 41; 42]. In this study, the disturbance is approximated using the Radial Basis Function (RBF) neural network. The RBF neural network can approximate nonlinear functions with relatively simple mathematical analysis and simple structure compared with Multi-Layer Perceptron (MLP) [43; 44; 45; 46]. Due to these characteristics, it is helpful for online control in combination with a robot system. Nonetheless, the artificial neural network has problems such as non-continuous signals, large system output due to an error at the beginning of the system operation, delay in convergence speed, or system instability. Therefore, research and applications have been applied and studied in combination with control engineering. Among them, there is a study case that demonstrated excellent performance in combination with a controller [46; 47; 48; 49; 50; 51]. On the other hand, as mentioned above, neural networks have the problem of convergence delay or instability due to large signal changes or parameter changes or discontinuous signals in the initial weight update stage. This problem can also appear when combined with a controller. Therefore, in previous studies, to compensate for this problem, an additional input called the 'robust term' has been used [42; 43; 44; 45; 46; 47; 48]. The robust term is similar to the switching term and consists of an error sign function and constant control gain. The robust term additionally applies a constant control input according to the sign of the error function. The controller with the robust term was effective in improving control performance but did not respond rapidly to large signal changes, and chattering occurred when the control gain was increased to respond rapidly. Consequently, to solve all these problems, the control gain of the nonsingular terminal sliding mode control switching term must be increased. Therefore, the problem of large chattering exists. It is important to solve this issue for network-based control. Other recent research are [47; 48; 52; 53; 54; 55; 56]. Studies by [47; 48; 52; 53; 54] have employed a backstepping-based control scheme, which is a useful method for nonlinear systems. Their results proved effective in enhancing control performance, but backstepping control requires a complicated design process and faces the problem of 'explosion of term' problems. The studies by [55; 56] proposed prescribed performance control with a fuzzy-neural network for nonlinear systems with uncertainty and constraint conditions. Their research results demonstrated effectiveness through simulation; however, the implementation of the system requires strict performance function settings and a complex design process. For research the other way, there are [57; 58]. Ref. [57] compensated tremors using a learning network-based filter integrating a deep learning network and modified incremental learning algorithms. Their study presents interesting results, but the swing to be solved in this paper is quite large, and the convolutional neural network they use is not easy to apply to the controller. Ref.