Abrham Tadesse Kassie

The study of the control of quadrotors has been addressed extensively in different UAV research areas. In this work, the nonlinear dynamic model of the quadrotor has been developed using the Newton–Euler technique. By considering the system's under-actuated and strongly coupled characteristics, the controllers have been then designed. As a main objective, design and comparative analysis of conventional sliding mode controller (SMC), supper twisting sliding mode controller (ST-SMC), and third order sliding mode controller (TO-SMC) schemes are realized for a nonlinear model of quadrotor. The controller's performance is highly dependent on the sliding surface coefficients and the controller constants. Therefore, a hybrid type of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), HGAPSO, has been formulated to find the best controllers’ parameters. The effectiveness of GA, PSO, and HGAPSO are investigated with TO-SMC in terms of trajectory tracking and ro-bustness. The control algorithms are focused on the position, altitude, and attitude states manipulated by four control inputs. The performance comparison of the controllers is evaluated under the effect of wind disturbance. Model development and simulation of the proposed system are carried out using MATLAB Simulink and a set of simulations are conducted to illustrate the states and analysis the performance of the proposed controllers. Finally, the HGAPSO tuned TO-SMC has performance supremacy in trajectory tracking, chattering elimination, settling time, rise time, ITAE minimization, and robustness over HGAPSO tuned ST-SMC and SMC. In addition, the HGAPSO reveals attractive performance over PSO and GA alone.

These days, robots excel at speed, precision, and reliability, surpassing human capabilities. Articulated manipulators, though, pose challenges due to their complex, nonlinear nature and susceptibility to uncertainties such as parameter changes, joint friction, and external disturbances. Designing robust trajectory tracking control for these dynamics is a key focus. This paper introduces a novel method that integrates SolidWorks modeling to create precise digital representations of the robot’s mechanical structure, facilitating easier development and simulation of control algorithms. To drive the robot joints, a permanent magnet direct current motor is used. Initially, sliding mode control (SMC) was employed, but it resulted in chattering in the control’s input response. To mitigate this issue and enhance trajectory tracking, this paper designs a super‐twisting SMC (STSMC). Intelligent particle swarm optimization (PSO) is employed to obtain optimal parameter values for STSMC, ensuring consistency, stability, and robustness. A comparative analysis was conducted among PSO–STSMC, STSMC, PSO–SMC, and classical SMC. Numerical simulations revealed that the tracking error and root mean square error (RMSE) improvements were approximately 18.33%, 16.66%, and 14.29% for PSO–STSMC compared to STSMC, and 79.50%, 78.04%, and 25.0% compared to PSO–SMC for each of the three joints under ideal conditions, respectively. Numerical simulations demonstrate the proposed controller’s robustness to external disturbances, parameter variations, and joint friction, effectively mitigating chattering effects in the control signal. Notably, this article highlights the potential of the PSO–STSMC for practical implementation in articulated robotic systems and underscores its significance in advancing robust trajectory tracking control, providing insights into articulated robot control strategies.

Active Magnetic Bearing system (AMB) is a nonlinear mechatronic device utilized for levitating the rotating components of a machine without physical contact with the stationary parts. The nonlinearity properties pose a challenge for ensuring system stability. To achieve stable and optimal performance, controllers must be incorporated. A model of the proposed AMB has been derived and results a 2X2 MIMO system. To simplify it, the model is linearized using Taylor series expansion technique. Simulation experiments have been conducted using MATLAB. In this paper, a neuro-fuzzy sliding mode controller (NFSMC) has been designed to control the position of the AMB system. Simulink models of the AMB with different control strategies have been developed for conducting simulation studies. The comparison results indicate that, with SMC, the position of the rotors follows the reference input but it is affected by chattering. It is further observed that FSMC reduces the chattering, while NFSMC controllers automatically eliminate the chattering problem and operate more robustly and smoothly. The system’s stability has been analyzed using the Lyapunov theorem. According to the results gained, the NFSMC improves the overall system performance. The NFSMC controller yields an optimal result having no overshoot, a settling time of 0.5 sec, and a rise time of 0.10 sec in comparison with FSMC controller which exhibits overshoot of 0.392%, a settling time of 0.75 sec, and a rise time of 0.133 sec, while the SMC shows overshoot of 9.047%, a settling time of 7.5 sec, and a rise time of 0.17 sec.