## 1 Introduction  
The transfer of robotics into everyday life is currently accelerating, with 20 million robots expected to be in use worldwide by 2030 [1]. The ability of robotic arms to accurately follow specified trajectories is important in a large field of applications. Control structure methods for the successful tracking of such trajectories by robotic arms represent a problem that has been studied for decades [2] and require either an accurate dynamic model or aggressive tracking with high-gain feedback. However, for most robotic arms, an accurate dynamic model can be difficult to obtain due to nonlinearities (e.g., friction) and uncertainties (e.g., inertial parameters) in these dynamic systems [3], making implementation on robotic arms difficult. Thus, the inability of these joint servo controllers to address these nonlinearities and uncertainties can lead to the degradation of accuracy in trajectory tracking [4]. Generally, in industrial robots, the solution consists of finding a compromise between a reduction in the cycle time and an improvement in tracking accuracy [5]. This situation is currently accentuated by the attempts to develop 3D-printed and low-cost robotic arms, which can have higher friction and cheaper motors. The most common control architecture consists of a joint torque control using a linear controller, such as a traditional proportional-integral-derivative (PID) controller. To compensate for the response time of the controller, a feedforward compensation using the dynamic model of the system is usually added. Widespread alternatives can use simplified models [6] or machine learning methods [3]. Notably, iterative methods, such as iterative learning control, are based on task repetition and allow for efficient trajectory tracking either with the addition of a dynamic model [5] or without any information on the dynamic model [7; 8]. However, if these methods are useful for repetitive tasks, such as working on assembly lines, the learned representation is not easily transferable between different trajectories. Control structures based on neural networks (NNs) have attracted attention with their potential ability to generalize beyond a training set [5]. For trajectory tracking especially, the power of approximation of NNs has been used in control structures to learn either the forward or inverse dynamics of the system [9; 10; 11; 12]. According to [5], when using an NN architecture, the control problem falls either in reinforcement learning [13; 14], adaptive control [15], or optimal control [16]. To approximate a system's forward dynamics, several NN architectures, such as recurrent neural networks (RNNs), feedforward networks, or radial basis function (RBF) neural networks, have been used, e.g., [12; 17; 18; 19]. In [20], NNs were used to learn inverse dynamic models, and they were included as a feedforward component for dynamic compensation. It has already been shown that NNs--especially RNNs and Long Short-Term Memory (LSTM) neural networks--can outperform other techniques, such as Gaussian Processes, to learn inverse dynamics when there are sufficient training data [21; 22]. However, most of these works operate at a torque-level control, which usually requires many parameters for the NNs--in the order of \(10^{5}\) in [23], for a 7-degrees of freedom (DOF) robot arm--or several neural networks to approximate different elements of the dynamic model. Moreover, when torque control is required, a bridge exists between the results in simulations and in reality. In [24], an adaptive controller based on a neural network was used for robot manipulator trajectory tracking, but solely the simulation results were presented. Even if the design of a controller plays a major role in control structures, Ref. [25] reminds us that the design of the control structure also answers the following questions: \"Which variables should be controlled and measured, and which inputs should be manipulated?\" \"Which control configurations (e.g., cascade control, feedforward control, etc.)