This article presents a comprehensive review of control approaches for industrial robotic manipulators, focusing on research conducted from 2020 onwards. The efficient functioning of robotic arms and successful task completion necessitate effective control strategies. Addressing real-world challenges, such as dynamic system variations due to environmental changes and unknown disturbances, remains crucial. To tackle these challenges, robust control strategies, including PID, H∞ and Model Predictive Control, are thoroughly surveyed. Commercially employed trajectory-planning techniques for manipulators are also extensively discussed. This paper concludes by providing valuable insights into prospective areas for future research, with the aim of enhancing the capabilities and performance of control strategies for industrial robotic manipulators. This study offers valuable knowledge to advance the field of robotic automation in Industry 4.0, fostering the development of efficient and intelligent manufacturing processes.

A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing the Newton-Euler approach. The cross-coupling between heave and yaw motion, an important dynamic of a class of compact ROVs that is barely reported, is investigated here. This work is thus motivated towards developing an understanding of the physics of the highly coupled compact ROV and towards developing model-based stabilizing controllers. The linearized EoM aids in developing high-fidelity experimental data-driven transfer function models. The coupled heave-yaw transfer function model is improved to an auto-regressive moving average with exogenous input (ARMAX) model structure. The acquired models facilitate the use of the multi-parameter root-locus (MPRL) technique to design baseline controllers for a cross-coupled multi-input, multi-output (MIMO) MicroROV. The controller gains are further optimized by employing an innovative Marine Predator Algorithm (MPA). The robustness of the designed controllers is gauged using gain and phase margins. In addition, the real-time controllers were deployed on an onboard embedded system utilizing Simulink′s automatic C++ code generation capabilities. Finally, pool tests of the MicroROV demonstrate the efficacy of the proposed control strategy.