## 1 Introduction
Additionally, it was noted that for an electromechanical system to be highly dynamic and robust, it may be necessary in some applications to take disturbances into account, such as by estimating them [2,9,10]. This complicates the control structure and introduces further dynamical behavior and computation delay into the control loops. In summary, if assumptions about the expected dynamics of the system are not made, a properly tuned PI/PID controller can be used instead of a more complex control structure [11, 12, 13, 14, 15]. In the broader approach to speed regulation, one can distinguish complex methods such as the _Linear Quadratic Regulator_ (LQR), extended into _Model Predictive Control_ (MPC), and those based on _Artificial Neural Networks_ (ANNs). An LQR controller ensures optimal control of state variables and outputs but requires an accurate mathematical model of the system, along with an observer and strict rules for controlling state variables [16, 17, 18, 19]. The main advantage of MPC is that it allows the optimization of the current state while accounting for future predicted states [9,16]. However, this approach faces challenges due to the non-linear system model and complex non-linear optimization rules. Many ANN architectures have been developed and applied to improve the performance of nonlinear control in motor systems [20]. In this work, the focus was on a classical approach, specifically finding a tuning method as opposed to a learning-based method. For the above assumptions regarding drive systems, the relationships describing the selection of controller parameters for various system structures are very well and clearly presented in [21]. It is worth treating this as a starting point for interesting scientific and engineering work. In this study, as presented in [22], a _Reference Bias Control_ algorithm was designed to further improve control performance while maintaining the structure of a conventional PI control algorithm. Considering the extensive literature on the topic, the authors decided to further develop one of the issues. The solution proposed in this article combines the simplicity of a PI/PID control system with the ability to achieve a higher dynamic range without the need for complex control and estimator systems. During the experimental studies and research laboratory practices, it was observed that incorporating the sum of past control errors into the current control method improves the quality of control. This observation became the foundation for further research. Paradoxically, adding delayed signals enhanced the dynamics of the motion control system. Analyzing the mathematical approach, this type of solution has its roots in signal theory and is referred to as a finite impulse response filter, which is well described in [23]. A similar concept has been used for speech recognition in [24] using an artificial neural network, called _the tapped delay line_. The application of FIR filters in the control of electrical drives encompasses several areas, including sensor noise reduction and signal smoothing [25; 26]. The authors of [26] propose a novel disturbance rejection control scheme for PMSM drives. Based on the framework of modified disturbance observer control, the final topology of the proposed disturbance rejection proportional-integral controller includes a prefilter and a controller in a PI form. Some applications even focus on representing the entire control system as a mathematical filter. These are approaches that include stochastic noise reduction [27], delay compensation [28], and complex compensator systems [18; 19]. The PID controller gains in work [19] are formulated as optimal state feedback gains, corresponding to the standard quadratic cost function involving the state variables and the controller effort. The following is noted in detail, including which of the solutions may have an aspect like the industrial one.