Ha Tang T. Ngo

This study presents a PRISMA-based systematic review of 343 publications focused on tracking how AI-based methods have evolved within robot manipulator control from 2015 to 2025. The review examines how AI has been incorporated into the control pipeline by organizing prior work according to functional roles, including perception and estimation, planning, learning-based control, interaction and safety, and learning and adaptation. In addition to this functional taxonomy, the study analyzes publication growth, application domains, robot types, evaluation settings, and methodological patterns to characterize the evolution of the field over the past decade. The results show that research activity has been concentrated primarily in learning control, while other functional roles have received comparatively less attention. The data also reveals an uneven distribution across application areas and robot platforms in the literature, with a strong reliance on simulation and limited evidence of integrated real-world deployment. These patterns indicate that, despite rapid growth and methodological diversity, the field remains imbalanced in both research focus and validation maturity. Rather than summarizing individual studies in isolation, this review provides a high-level perspective on where effort has been concentrated, where major gaps persist, and which directions are most critical for advancing AI-based robot manipulator control toward reliable and scalable real-world use.

This paper presents a novel hybrid learning-based control scheme for position control of robot manipulators whose structure is based on a closed-kinematic-chain mechanism (CKCM). The developed control scheme integrates two complementary control components: the feedback controller and the learning controller. The feedback controller is designed using linearization about a desired trajectory and a PID control law whose gains are selected by a tuning algorithm to guarantee semi-global stability of the linearized closed-loop feedback system. The learning controller incorporates PID-type iterative learning strategy to generate additional control inputs to compensate for modeling uncertainties and unmodeled dynamics. By updating the control input iteratively from trial to trial, the learning controller progressively improves the overall control performance. The effectiveness of the developed control scheme is demonstrated through computer simulations conducted on a six-degree-of-freedom CKCM robot manipulator. Simulation results are presented and discussed to evaluate the tracking accuracy of the developed approach.

This study presents a structured analysis of 343 publications on artificial intelligence-based methods for robot manipulator control published between 2015 and 2025. The review examines how AI has been incorporated into the control pipeline by organizing prior work according to functional roles, including perception and estimation, planning, learning based control, interaction and safety, and learning and adaptation. In addition to this functional taxonomy, the study analyzes publication growth, application do-mains, robot types, evaluation settings, and methodological patterns to characterize the evolution of the field over the past decade. The results show that research activity has been concentrated primarily in learning control, while other functional roles have received comparatively less attention. The literature also reveals an uneven distribution across ap-plication areas and robot platforms, with a strong reliance on simulation and limited evidence of integrated real-world deployment. These patterns indicate that, despite rapid growth and methodological diversity, the field remains imbalanced in both research focus and validation maturity. Rather than summarizing individual studies in isolation, this review provides a high-level perspective on where effort has been concentrated, where major gaps persist, and which directions are most critical for advancing AI-based robot manipulator control toward reliable and scalable real-world use.

This paper presents a novel hybrid learning-based control scheme for position control of robot manipulators whose structure is based on a closed-kinematic-chain mechanism (CKCM). The developed control scheme integrates two complementary control components: The Feedback Controller and the Learning Controller. The Feedback Controller is designed using linearization about a desired trajectory and a PID control law whose gains are select-ed by a tuning algorithm to guarantee semi-global stability of the closed-loop system. The Learning Controller incorporates PID-type iterative learning strategy to generate additional control inputs to compensate for modeling uncertainties and unmodeled dynamics. By up-dating the control input iteratively from trial to trial, the Learning Controller progressively improves the overall control performance. The effectiveness of the developed control scheme is demonstrated through computer simulations conducted on a six-degree-of-freedom CKCM robot manipulator. Simulation results are presented and discussed to evaluate the tracking accuracy and robustness of the developed approach.

Robot-assisted rehabilitation has demonstrated significant efficacy in improving motor function among patients with physical and neurological impairments. The development of effective rehabilitation robots requires careful integration of mechanical design and control systems to ensure safe, compliant, and intention-oriented human–robot interaction while delivering appropriate therapeutic assistance and feedback. Parallel robot manipulators have increasingly gained attention in rehabilitation applications due to their superior precision, structural stiffness, and high load capacity compared to their serial counterparts. This paper presents a scoping review of control strategies specifically implemented in parallel rehabilitation robots between 2015 and 2025. The control strategies include position control, force control, compliance control, adaptive control, intelligent control, and hybrid control. Our analysis showed a progressive shift from traditional position-based control toward more sophisticated adaptive and intelligent strategies that better accommodate patient-specific needs and therapeutic requirements.

This paper deals with the mechanical design and kinematic analysis of an autonomous wrist for space assembly (AWSA) whose actuators are activated by DC motors and ball screw drives. This robotic wrist was developed and built as a prototype to investigate in-space robotic operations, including maintaining and repairing spacecraft of the US National Aeronautics and Space Administration (NASA), such as the International Space Station (ISS) or satellites. Despite its disadvantages, such as a small workspace and low maneuverability, a parallel structure instead of a serial structure was selected for the design of the AWSA due to several advantages it has over a serial robot manipulator (SRM), including higher payload, greater stiffness, and better stability. The present paper also introduces a hybrid concept for robotic space operations, which combines an SRM performing gross motion and a parallel robot manipulator (PRM) performing fine motion. It then discusses the design and construction of the DC motor actuators and ball screw drives and presents the kinematic equations developed for the AWSA. This paper provides a closed-form solution to the inverse kinematics of the AWSA and a numerical solution using the Newton–Raphson method for its forward kinematics.

This paper deals with the mechanical design and kinematic analysis of an autonomous wrist for space assembly (AWSA) whose actuators are activated by DC motors and ball screw drives. This robotic wrist was developed and built as a prototype to investigate in-space robotic operations including maintaining and repairing spacecraft of the US National Aeronautics and Space Administration (NASA) such as the International Space Station (ISS) or satellites. Parallel structure instead of serial structure was selected for the design of the AWSA due to several advantages it has over a serial robot manipulator (SRM), including higher payload, greater stiffness and better stability. The present paper also introduces a hybrid concept for robotic space operations, which combines a SRM performing gross motion and a parallel robot manipulator (PRM) performing fine motion. It then discusses the design and construction of the DC motor actuators and ball screw drives and presents the kinematic equations developed for the AWSA. The paper provides a closed-form solution to the inverse kinematics of the AWSA and a numerical solution using Newton-Raphson Method to its forward kinematics.

This paper deals with the design, analysis and control of an autonomous wrist for space assembly (AWSA) whose actuators are activated by dc motors and ball screw drives. This manipulator was developed and built as a prototype to investigate in-space robotic operations including maintaining and repairing NASA spacecraft such as the International Space Station (ISS) or satellites. Parallel structure instead of serial structure was selected for the design of the AWSA due to several advantages it has over a serial robot manipulator (SRM), including higher payload, greater stiffness and better stability. The present paper also proposes a hybrid concept for robotic space operations, which combines a SRM performing gross motion and a parallel robot manipulator (PRM) performing fine motion. It then discusses the design and construction of the dc motor actuators and ball screw drives and presents the kinematic equations developed for the AWSA. The paper proposes several control schemes including a Cartesian control scheme, a joint-space control scheme and an adaptive control scheme for motion control of AWSA.

This paper presents the results of real-time experiments conducted to evaluate the performance of a developed adaptive control scheme applied to control the motion of a real closed-kinematic chain mechanism (CKCM) robot manipulator with two degrees of freedom (DOFs). The developed control scheme, referred to as the decentralized adaptive synchronized control scheme (DASCS), was the result of the combination of model reference adaptive control (MRAC) based on the Lyapunov direct method and the synchronization technique. CKCM manipulators were considered in the experimental study due to their advantages over their open-kinematic chain mechanism (OKCM) manipulator counterparts, such as higher stiffness, better stability, and greater payload. The conducted computer simulation study showed that the DASCS was able to asymptotically converge tracking errors to zero, with all the active joints moving synchronously in a prescribed way. One of the important properties of the DASCS is the independence of robot manipulator dynamics, making it computationally efficient and therefore suitable for real-time applications. The present paper reports findings from experiments in which the DASCS was applied to control the above manipulator and carry out various paths. The DASCS’s performance was compared with that of a traditional adaptive control scheme, namely the SMRACS, when both schemes were applied to track the same paths.

This paper presents a new decentralized adaptive control scheme for motion control of robot manipulators built based on a closed-kinematic chain mechanism (CKCM). By employing the synchronization technique and model reference adaptive control (MRAC) based on the Lyapunov direct method, the Decentralized Adaptive Synchronized Control scheme (DASCS) is developed. The DASCS can ensure global asymptotic convergence of tracking errors while forcing all active joints to move in a predefined synchronous manner in the presence of uncertainties and sudden changes in payload. Furthermore, the control scheme has a simple structure, independent of the manipulator’s dynamic model, ensuring computational efficiency. Results of computer simulations conducted to evaluate the performance of the control scheme applied to controlling the motion of a CKCM manipulator with six degrees of freedom are reported and discussed.