This paper presents original concepts of control systems for an electrical drive with an elastic mechanical coupling between the motor and the driven mechanism. The synthesis procedure of the speed controller uses a proposed quality index (cost function) of system operation ensures the minimization of both tracking errors and torsional vibrations. Proper selection of the cost function focusses more on the reduction of torsional vibrations due to their negative influence on the drive’s mechanical coupling vitality. The omission of the plant identification of an adaptive fuzzy controller was proposed. Two types of fuzzy controllers were analyzed, namely with type I and type II fuzzy membership functions. The novelty of the presented approach is in the application of a Petri transition layer in a type II fuzzy controller which reduces the numerical complexity in case of a large number of complicated type II fuzzy sets. The presented simulation and experimental results prove that the best dumping of mechanical vibrations ensures the adaptive fuzzy controller with type II functions and a Petri transition layer.

The paper proposes a Birch-inspired optimisation algorithm designed to optimise the PI controller gain and additional feedback coefficients used for robust speed control of a two-mass drive system. The technical issue we considered is torsional oscillation suppression in a two-mass drive system using a robust speed controller. To address this, we used a cascade control structure with additional feedback from torsional torque and its derivative. Since this torque is difficult to measure, a second-order integral disturbance observer was implemented. The integral type of the observer offers lower noise compared to classical derivative solutions. To tune the system, a Birch-inspired Optimization Algorithm was used. The tuned system has been verified through both MATLAB/Simulink environment and experimental verification, demonstrating the robustness and accuracy of the solution.

In the paper an adaptive fuzzy logic control based structure for damping of torsional vibrations is proposed. Firstly, the commonly-used control methodologies for two-mass drive system are presented. Then the mathematical model of the plant is introduced shortly. Next the adaptive control structure with fuzzy PID controller is described in detail. The computational effort of fuzzy controller is reduced by applied the additional Petri-like transition layer. This allows to implement much more complicated fuzzy controller using simple microprocessor. Different aspect of the proposed control structure is investigated: e.g. working under different trajectories, robustness to the parameter changes. The theoretical considerations and simulation study are confirmed by experimental tests.

The purpose of this paper is to show possibility of using Petri Transition layer in adaptive neuro fuzzy type II PID fuzzy controller. Large number of computations needed to be made in order to calculate controllers output value due to high dimension and presence of type II fuzzy sets may cause the system impossible to be used in real objects. Using Petri transition layer this big number of calculations may be reduced, making the solution possible to be implemented on the object. This paper aims to show possible profits of such combination. The ANFT2C has been modeled sing Matlab Simulink. Mentioned system is used as speed controller for DC shunt motor in cascade control structure. The system was modeled using SimPowerSystems library for Simulink. Both idea of PTL and resulting from its implementation profits were shown in the paper. Possible changes in T2 fuzzy functions parameters and their influence on the control process were analyzed and compared. In the last part of the paper so far advantages and future possibilities are shown.

The purpose of this paper is to show possibility and advantages of initial control plane reproduction for an adaptive fuzzy controller. Usually the fuzzy control is used when the object is not very well known. Yet the truth is, however, that some, at least general information about the object, is available. Usually, in such a case, optimization algorithms are used to tune the control structure. The purpose of this article is to show how to find a starting point that is closer to optimum than a statistically random point, and this way to obtain better results in a shorter time.