## 1 Introduction
If the external noise component \(\underline{q}\) passes but the indeterminate constant parameter still exists, we have the inverse control method of the model [2]. The reverse learning or Li-Slotine method applies when the parameter \(\underline{q}\) is arbitrary. The song must be constant and must have \(\underline{t}_{d}(t)=0\)[3]. In cases where both external noise components and uncertain constant parameters exist, there is a sliding control method [4; 5]. On the other hand, chattering occurs when the system slides on the sliding surface too often, which is a drawback of the sliding control approach. To improve this vibration phenomenon, there is also a high-order sliding control method [6], but it still requires an estimated value of the maximum standard deviation of the model caused and cannot eliminate it. In addition, to reduce the chattering phenomenon, people use neural networks [7] to estimate the uncertainty components based on collecting information from robots (through \(\underline{q}\) and its derivative \(\underline{\hat{q}}\)) by the output estimation component and then uncertainty compensation through control signals. The intelligent control method mainly applied to robots mentioned in this article is the iterative learning method [8; 9]. This integrated control method with cyclic working systems requires the same state. At the same time, the parameters \(\underline{q}(t)\) and \(\underline{\uptau}_{d}(t)\) are also required to be periodic and have the exact change period as the working cycle of industrial robots. The primary iterative learning control method is only sometimes applied to meet the desired control quality. There have been many improvements to iterative learning to improve control quality and expand the scope of practical applications. The first improvement is the improvement of combining iterative learning with traditional control methods, often called indirect iterative learning or direct transmission iterative learning [9; 10; 11]. These improvements include [12] when the friction component alone cannot be determined [13], when that is not possible, or [14] when the learning function parameters cannot be found, as well as when the learning function parameters need to be changed. In relation to change during work cycles, a control trend for a class of robotic systems is model matching control, including [15]. This control method requires the mathematical model (1), which will encounter the problemsand disadvantages of the previous traditional control methods. The next innovation is to use model-based control but to almost completely omit the mathematical model (1) of the robot, applicable to both cases where \(\underline{q}(t)\) and \(\underline{\tau}_{d}(t)\) are dependent. This depends on time and does not require periodicity, so it can be considered an intelligent model-matching control method. This method is based on theoretical results on the optimal control of each segment on the time axis available in [16] and applying disturbance estimation to control self-balancing two-wheeled vehicles [17]. The main content of this article is to analyze and evaluate additional improvements to essential iterative learning to improve the quality of motion control of robot systems or microrobot systems [18; 19; 20] and to expand the scope of practical applications, specifically. Intelligent control based on model matching (model matching) almost wholly does not need to use the mathematical model of the robot system, as applicable to the case where \(\underline{q}(t)\) and \(\underline{\tau}_{d}(t)\) depends on time, and there is no need for circulation for the robot system. The content of this article includes six parts. Part 1 is the problem of researching iterative learning for robot systems. Part 2 presents the results of the 2-DOF robot dynamic equation. Part 3 presents two robot control structures using iterative learning. Part 4 presents the first structure controls for the robot using iterative learning.