International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The integration of robotic systems is widespread across diverse industries, notably in defence, automotive, and industrial sectors. These systems are endowed with the capability to execute precise movements via software programming, facilitating object manipulation and trajectory adjustments. Nonetheless, careful oversight is imperative during operations to avert undesirable outcomes stemming from mishandling. Consequently, the management of robotic systems has emerged as a pivotal concern in contemporary industrial practices. The parameters governing robotic systems are subject to fluctuations contingent upon the loads they bear. Robust control, a methodology geared towards adapting the control system to accommodate such parameter variations, stands as a cornerstone for ensuring stability and optimal performance. This approach enables the maintenance of desired control levels even amidst shifting system parameters. To refine controller parameters, an objective function derived from error functions of the first and second robot arms was minimized. In this endeavour, the particle swarm optimization (PSO) method, renowned for its efficacy, was employed. The efficacy of this proposed control methodology is substantiated through graphical representations, underscoring its utility and effectiveness in real-world applications.

The utilization of robotic systems is prevalent in various industries, such as defence and automotive, and is commonly utilized in industrial settings. The movements of these systems can be controlled through software programming, allowing for the manipulation of objects and modification of trajectory as desired. However, it is important to exercise caution during these operations as improper manipulation may result in undesired outcomes. As a result, the control of robotic systems has become a crucial aspect in modern industry.
The parameters of robotic systems are subject to change based on the loads they carry. Robust control is a method that adapts the control system to accommodate these changes in parameters, thereby maintaining stability and performance. This control method allows for the desired level of control to be maintained even in the presence of changing system parameters. In contrast to traditional robust control methods, robust control utilizes variable parameters with a constant upper limit for parameter uncertainty. Control parameters are updated over time using cosine and sine functions, however, determining appropriate values for these parameters can be challenging. To address this issue, a neural network model utilizing fuzzy logic compensator is employed to continuously calculate the appropriate control parameter values. The effectiveness of this proposed control method is demonstrated through graphical representation.

This research proposes modelling, simulation and implementation of autonomous unmanned quadrotor prototype based on Matlab Simulink software, and Mission Planner for communicating with APM control board of the quadrotor. The goal is to Control attitude and altitude over a desired trajectory of the Quadrotor using PID control, with high precision and reliability. The mathematical model used for simulation takes into account all differential equations of motion of the quadrotor. A full quadrotor prototype was assembled for real experiments to do a comparison between real and simulated data. This comparison reveals the reliability and the accuracy of the PID controller and the mathematical model used in Matlab.