In [23], an approach is proposed to address the complexity of solving the inverse kinematics in a seven-DOF serial manipulator through an algorithm based on the Artificial Bee Colony (ABC) optimization algorithm, where two control parameters are used in order  
Figure 1: Trial-and-error procedure. to adjust the search to optimize the distribution of the sources. In [24], an optimization approach is shown in the planning of the trajectories applied in a five-bar parallel robot for real-time control, minimizing the trajectory time and avoiding singularities in the parallel manipulator, achieving an error of less than 0.7\({}^{\circ}\) at the joints. Factorial experimental design is a statistical technique used to identify and measure the effect that one variable has on another variable of interest. In 1920, [PERSON] studied multiple factors in the agricultural field to determine the effect of each factor on the response variable, as well as the effect of the interactions between factors on this variable. This method is known as the factorial design of experiments. Factors are variables that determine the functionality of a product or process and significantly influence system performance and can usually be controlled. To evaluate the impact of each variable, the factors must establish at least two levels; therefore, given \(k\) factors with \(l\) levels, a complete factorial design that includes all the possible combinations between these factors and levels will produce a total of \(l^{k}\) experimental runs. Obviously, as \(k\) or \(l\) increases, the number of experiments may become unfeasible to carry out, since a significant number of factors would imply a large number of experiments. For this reason, fractional factorial designs have been introduced, which require only a fraction of a run, unlike a complete factorial design, and which allow estimating a sufficient number of effects [25; 26]. [PERSON] is considered to be the author of robust parameter design through a procedure focused on reducing variation and/or sensitivity to noise in the design of products or processes, which is based on the concept of fractional factorial design. Through the implementation of orthogonal arrays (OA) and fractional factorial design, it is possible to analyze a wide range of parameters through a reduced number of experiments, ensuring a balanced comparison between the factors involved and the interaction with their different levels [2; 27; 28]. The Taguchi method is applied in four stages:  
1. _Selection of design and noise variables._ In this stage, the most important parameters for the product/process are considered, taking into account the quality characteristics. Generally, there are variables that can be controlled by the user and others that cannot. These types of variables are known as design and noise factors, respectively, which have an important influence on the operation of the product/process. They can be determined mainly by answering the following questions: What is the optimal design condition? What factors contribute to the results and to what extent? What will be the expected result? 2. _Design and experimentation._ An OA is established, which contains the organization of the experiment taking into account the levels established for each of the factors in order to minimize the effects produced by noise factors. In other words, the adjustments made to the factors must be determined in such a way that there is the least variation in the response of the product/process, and the mean is established as close as possible to the desired objective. The OA allows the implementation of a balanced design in the weighting of the pre-established levels for each factor involved since it is possible to evaluate various factors with a minimum number of tests, obtaining a considerable amount of information through the application of few tests. The mean and variance of the response obtained in the OA configuration are combined into a single performance measure known as the signal-to-noise ratio (S/N). 3.