A magnetic levitation system (MLS) controls the position of a steel ball with the magnetic force of the electromagnetic actuator. A disturbance observer (DOB) could improve the disturbance rejection and command tracking performance of the voltage-controlled MLS. This paper studied control boost of MLS using current and position DOB. The current-controlled MLS had a higher control performance than the voltage-controlled MLS. The combination of PID position and PI current controls provided stable levitation and a wide operation range of MLS. When DOB was applied to PI current control, it could compensate for inductance change according to the position of the steel ball. In addition, when another DOB was introduced to the PID position control, it improved the disturbance removal performance. Finally, we discussed the effectiveness and limitations of the DOB-based current and position control by measuring closed-loop frequency responses.

The fourth industrial revolution led to advanced servo systems, enhancing productivity across industries. However, designing these systems remains challenging due to the performance-stability trade-off. This paper presents a model-based motion control of a linear motor motion stage in frequency domain. A user-code for the PowerPMAC commercial controller was developed to excite motion control system so that we could get a frequency response. The theoretical frequency response of the servo algorithm was compared with the experimental frequency response. Based on this, a tuning graphical user interface (GUI) was developed to predict performance when the servo loop gain is changed. Especially, to compensate for residual vibrations caused by high acceleration and deceleration and to improve tracking error, DOB (Disturbance Observer) and ILC (Iterative Learning Control) control techniques were applied in the frequency domain. Through the design of the frequency domain motion controller, the control performance of the linear motor motion stage could be predicted with over 96% accuracy, resulting in a 54.32% improvement in tracking error and a 93.56% improvement in settling time, 85.29% in RMS error.

Drone is an innovative industry that can combine the application of various technologies in the fourth industrial era, such as big data, artificial intelligence, and ICT. Although the synergy effects of these technologies will be great in various industrial ecosystems, drones are vulnerable to gusts such as "building wind" or "valley wind". Herein, the frequency domain of a mini drone was identified and a model-based disturbance observer (DOBs) was applied to implement the drone robust resistance against gusts. The frequency response of the Parrot Mambo or mini drone was measured with multi-sine excitation and the system dynamic parameters were identified. Based on the identified model, DOBs were designed and applied to the drone’s altitude, position, and yaw control. The effectiveness of the DOBs was verified with a sinusoidal disturbance. With the model-based DOB, 84.5% of the drone altitude responses, 50.7% of x responses, 52.1% of y responses, and 79.7% of yaw responses against sinusoidal disturbances were reduced. Flight responses were measured against wind disturbances with changing speed and direction. With the model-based DOBs, the drone"s altitude decreased by 87.7%, the x position by 53.0%, the y position by 60.6%, and the yaw angle by 56.2%.

Magnetic levitation system (MLS) is a typical nonlinear system that controls the position of a steel ball with the magnetic force of the electromagnetic actuator. Since disturbances, due to various external forces and modeling errors, may cause excessive vibration or poor command following, disturbance suppression is necessary to improve the control performance of the MLS. This paper presents a control performance improvement approach of an MLS with a disturbance observer (DOB). First, a mathematical model of the MLS was introduced and validated with the measured frequency response. The MLS steel ball was levitated with a proportional–integral–derivative (PID) controller and a DOB was designed based on the physical model of the MLS. Both disturbance rejection and command tracking performances of the MLS with the DOB were investigated with several design parameters such as PID gains and Q filter. The disturbance rejection and command tracking performances were improved by 76.1% and 64.7%, respectively by using DOB. Finally, the disturbance rejection and command-following performances of the MLS with the DOB were verified experimentally. The effectiveness and limitations of DOB were explained with measured closed-loop frequency responses.

In this study, a super-twisting sliding mode controller with a non-linear disturbance observer for a ball-screw servo system was designed to obtain a precise motion and fast convergent control performance. Unknown dynamics of the servo system were approximated into pre-assumed diagonal constants for rapid controller design in the real industry to avoid expensive and time consuming experimental identification process. Moreover, uncertainties due to nonlinear friction, axis misalignment and dead zone were estimated by a nonlinear disturbance observer, which is combined with the designed super-twisting controller. The designed controller and observer systems were applied to the 2-axis ball screw servo system to verify the efficacy of the proposed control system via simulation and experiment.

This paper describes an adaptive model free speed control algorithm for DC motors, based on a recursive least-squares with forgetting factor. In order to control the speed of a DC motor, only the factors of output speed and voltage values have been used without a mathematical model of the DC motor. As the relationship between the input voltage and the DC motor speed in a specific region can be approximated as a first order system, the coefficient that represents the approximated first order system has been estimated by using a recursive least-squares approach with a forgetting factor model. Also, the error between the actual system and the approximated first order system has been estimated by a disturbance observer. Based on the estimated coefficient of the first order system, as well as this disturbance, an optimal input for tracking the desired velocity has been computed by using the Lyapunov direct method. Weighting factor adaptation rules have been proposed to enhance control performance. This performance evaluation has been conducted in a MATLAB/Simulink environment using a DC motor dynamic model for realistic evaluation. The evaluation results show that the developed adaptive DC motor speed control method ensures good tracking performance by using only the input voltage and the output speed information.

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.