It is undeniable that the development of robot manipulators has gained significant attention due to its numerous applications in various sectors. In order to address and resolve several issues for robotic manipulators, this paper provides a review of recent control approaches that have been conducted. In the beginning, a succinct comprehensive introduction of the pertinent terminology, configurations, components, advantages/disadvantages, and applications is given. A state-of-the-art review of the various control strategies for robotic manipulator systems is then presented, along with potential solutions when the systems are faced with various obstacles and impediments. An analytical study is discussed showing percentages of the papers discussed in this study in terms of the different control strategies, link types, models, applications, and degrees of freedom of robotic manipulators. For academic, research, medical, and industrial uses, some off-the-shelf developments in robotic manipulators are also presented.

This work presents an innovative hybrid control scheme for a quadrotor unmanned aerial vehicle (UAV) model to improve the disturbances rejection capability and body jerk performance by utilizing an active force control (AFC)-based robust intelligent control system via a simulation study. The proposed intelligent control approach incorporates a proportional-integral-derivative (PID) and an intelligent active force control (IAFC) element yielding a robust PID-IAFC scheme. A detailed mathematical model of a quadrotor system with six degrees of freedom (DOFs) was first derived using the Newton-Euler method taking into consideration the gyroscopic terms, disturbances, aerodynamics, and friction effects. In the derived model, the PID controller was first designed to stabilize the quadrotor model and achieve the required altitude and attitude motions. In addition, different types of external disturbances in the form of sinusoidal waves and repeated impulses (pulsating) were added. An AFC strategy, known as PID-AFC, was designed and incorporated into the PID controller, and was initially tuned heuristically. Then, an artificial intelligence (AI)-based method employing an iterative learning (IL) algorithm was designed and implemented into the AFC (ILAFC) to estimate the control parameters automatically while on-line. Thereafter, the performance of the ILAFC was compared to the AFC with fuzzy logic (FL) which became known as FLAFC. Also, a selftuning (ST) PID controller was designed and employed based on the FL method to automatically tune the PID gains based on the prescribed operating and loading conditions. Moreover, a comparative study of the system performance was carried out utilizing the PID, PID-AFC, ILAFC, FLAFC, and ST-FPID-AFC schemes to analyze the system characteristics. Furthermore, the effectiveness of the AFC-based intelligent controller was investigated in connection with the body jerk performance in the presence of external disturbances. The simulated results reveal the effectiveness and robustness of the proposed control strategy based on the IAFC technique in improving the disturbance rejection capability and body jerk performance by 17% in the presence of uncertainties and external disturbances.

This paper centers around an experimental investigation into the effectiveness of an innovative hybrid control approach based on an intelligent active force control (IAFC) strategy to stabilize a twin-rotor helicopter model and improve its ability to reject external disturbances efficiently. The intelligent algorithm was based on an iterative learning (IL) method integrated into the main control loop to estimate control parameters automatically while on-line. A mechatronic test rig with the IAFC-based control algorithm was incorporated into a Quanser Aero twin-rotor model in a laboratory setting as a verification platform to evaluate the applicability and efficacy of the proposed control algorithm via a practical real-time implementation. The hybrid IAFC-based control design was rigorously examined to test its feasibility and durability in countering various forms of external disturbances while executing the trajectory tracking tasks. Notably, the efficiency of the IAFC-based control unit was mainly studied and compared with other control plans under different operating conditions for benchmarking. The experimental results show the ability of the controller based on the IAFC strategy to effectively improve the disturbance rejection capability compared to the other control schemes considered in the study. About 27% improvement of the system performance in terms of lowering the root mean square error (RMSE) was observed compared to the other control systems counterparts.

This study proposes the design and development of an in-pipe mini crawler (or robot) capable of performing its various tasks with the ability to reject undesired disturbances resulting from friction and viscosity, as it was modeled, simulated, and experimented using an iterative learning algorithm (ILA)-based active force control (AFC) strategy. The crawler motion was executed based on a rapid and successive push-pull action plus friction that causes the crawler to move in an earthworm-like manner using a linear voice-coil actuator (VCA). A novel self-adjusted mechanism was designed to ensure that the crawler remained concentric in the pipe as it slides along the inner surface of the pipe. A novel control strategy was also proposed consisting of the AFC-based controller cascaded with a proportional-integral-derivative (PID) controller to control the crawler movement and expel off the applied perturbations. An intelligent PD-type ILA was employed to automatically tune the AFC while online. For the validation part, a prototype was designed, developed, and later experimented with using the proposed technique for a given set of conditions. The system integration employed a hardware-in-the-loop (HIL) test configuration utilizing LabVIEW. Experimental results are in good agreement with the simulation counterpart, thereby indicating the practicality and feasibility of the control system in performing accurate and robust trajectory tracking. This shall serve as a good basis for designing more challenging tasks related to miniature crawling mechanism in-pipe applications.