<p>In recent decades, the use of industrial robots in various fields such as automotive, medical, and surgical applications have significantly increased, aiming to enhance accuracy, speed, and efficiency in manufacturing processes. One of the major challenges in robotics is the precise control of the positions of robot components, especially arms, which requires the design of advanced control systems with high performance and quick response times. BLDC (Brushless Direct Current) motors are ideal for robotic applications due to their high efficiency, precision, and longer lifespan. In this context, the use of predictive control based on mathematical models of the system can significantly improve system performance. This paper approximates the high computational load required to achieve a long predictive horizon using a number of discrete orthogonal basis functions, such as Laguerre polynomials. The main advantage of this approach is the optimization of coefficients with fewer orthogonal functions instead of optimizing the control path itself, allowing for the selection of a longer predictive horizon. This research demonstrates that the practical implementation of these methods can be beneficial in various robotic applications such as surgery, manufacturing, and automotive industries. In this paper, we first address the modeling of the system dynamics and then simulate and implement hardware for the predictive control method using Laguerre functions. The results from the experiments show that the use of Laguerre functions in predictive control, especially at high predictive horizons, has significant advantages over conventional predictive control.</p>