This study was based on the dynamic modeling and parameter characterization of the one-link robot arm driven by pneumatic artificial muscles. This work discusses an up-to-date control design based on the notion of a conventional and optimal backstepping controller for regulating a one-link robot arm with conflicting biceps and triceps positions supplied by pneumatic artificial muscles. The main problems found in systems that utilize pneumatic artificial muscle as actuators are primarily the large uncertainties, non-linearities, and time-varying features that severely impede movement performance in tracking control. In consideration of the uncertainty, high nonlinearity, and external disturbances that can exist during the motion. Lyapunov-based backstepping control technique was utilized to assure the stability of the system with improved dynamic performance. The bat algorithm optimization method is utilized in order to modify the variables used in the design of the controller to enhance the efficiency of the suggested controller. According to the conclusions, a quantitative comparison of the response in the PAM actuated the arm model in the current study and earlier investigations with the Backstepping controlled system revealed fair agreement with a variation of 37.5% from the optimal classical synergetic controller. In addition, computer simulations were utilized in order to compare the effectiveness of the proposed conventional controls and the optimal background. It has been proven that an optimal controller can control the uncertainties and maintain the controlled system’s stability.

This paper focuses on the control of Pneumatic Artificial Muscles (PAMs) used in arm manipulator modeling and the dynamic model of the Pneumatic Artificial Muscles. PAMs have become popular in robotics due to their fast work capabilities, direct action mechanisms, and safety implementation. However, these systems often suffer from uncertainty, nonlinearity, and time-varying features, which negatively impact tracking control performance and cause instability in motion outcomes. To address these issues, this study presents a comparison of two controllers: an adaptive backstepping controller and a backstepping convolution controller. Computer simulations were used to evaluate the performance of both controllers. The results demonstrate that the adaptive backstepping controller effectively eliminates chattering, reduces error, and maintains stability in the controlled system, leading to smoother signal curves and improved overall response in the arm model. In conclusion, the study provides evidence that the adaptive backstepping controller is a more effective control solution for PAM-led arm manipulator systems, offering improved control of uncertainties and better motiontracking performance. These findings have important implications for the development of advanced robotic systems using PAMs.