This paper presents an integrative review of control strategies in robotics, covering classical control methods (linear quadratic regulator, proportional-integral-derivative), modern methods (adaptive, sliding mode, model predictive, and H-infinity), intelligent control methods (neural network, fuzzy logic, and machine learning), and hybrid control methods (integration of classical, modern, and intelligent control methods) to identify the advantages, limitations and gaps for future. A brief comparison of control methods between the types of control strategies is conducted with respect to robustness, stability, and complexity of implementation on 3 different levels of evaluation criteria: high, average, and low; advantages; limitations; and robotic applications, including examples. This paper discusses the theoretical and practical advancements and the classification of control strategies according to controller types (linear, nonlinear, and learning-based), approaches (model-based and model-free), and classifications (centralized, decentralized, and modal control). The review highlights the strengths, limitations, and potential research directions in bridging classical, modern, intelligent, and hybrid control paradigms to achieve safe, efficient, and adaptive robotic behavior in complex, uncertain environments. We discuss the future direction: autonomy, human-robot collaboration, and enhanced learning and challenges: cost, reliability, safety of control strategies, concluding with recommendations for future research.

Human rehabilitation improved significantly after traumas, surgery, or accidental cross-link events with human health. During the last six decades, exoskeletons have played a significant role in human activities related to body training and post-trauma or surgery treatment, especially in gait rehabilitation. The main goal of rehabilitation training is to restore patients’ physical abilities to average by improving and monitoring their posture and gaining weight. In this paper, a classification of various types of exoskeletons is provided, a comparison between the different lower limb exoskeletons for gait rehabilitation presents, the gait anatomy, mechanical design, and control strategy for the prototype of lower limb exoskeleton studies, and the end, some concluding remarks are stated that may be useful for future work. The paper concludes with conclusions and a significant reference list.

This paper presents the mechatronic design of a proposed six-degree-of-freedom (6-DOF) stationary lower limb rehabilitation exoskeleton (SLLRE) robot, developed through a comprehensive mechatronic design methodology that includes mechanical design, actuator and sensor selection, and the development of a control structure based on a safety-oriented control approach. The proposed 6-DOF SLLRE mechanical design introduces a comprehensive mechanical structure comprising bilateral hip, knee, and ankle joints, degrees of freedom, joint ranges of motion, human parameters for rehabilitation training, as well as the frames and structural elements of the proposed system. Furthermore, the selection of appropriate sensors, actuators, and control architecture for the proposed device is explained in detail based on the requirements of the safety control strategy. As a result of this study, a human-exoskeleton interface design as a safety mechanism proposed and designed for next step as a creation process based on addressed critical requirements for lower limb gait rehabilitation including stroke recovery, spinal cord injury therapy, and gait retraining applications. The recommendations for future research offer as assistance at the end of this paper.

This research provides a detailed guideline for implementing and evaluating Proportional Integral Derivative (PID) control frameworks in lower limb rehabilitation exoskeleton robotics. It examines the role of control systems within rehabilitation robotics, outlines the principles of PID control, describes exoskeleton architecture, explores applications of PID control, reviews optimization strategies, presents experimental validations, and considers future developments in the field. The proposed control framework incorporates aspects of mechanical design, actuator and sensor selection, and PID-based control algorithms, thereby promoting safe, accurate, and individualized rehabilitation support. Recommendations and effective guidance for future work are also presented.