Non-Leaner Control on the Pneumatic Artificial Muscles: A Comparative Study Between Adaptive Backstepping and Conventional Backstepping Algorithms

Abstract

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.

Design of a Robust Controller Based on Barrier Function for Vehicle Steer-by-Wire Systems

“…Further advancements can be explored by suggesting other control techniques, such as adaptive control schemes, observe-based control, backstepping-based control, and active disturbance rejection control for the sake of comparison [45][46][47][48][49][50][51]. Also, an improvement of the proposed controller can be presented by suggesting modern optimization techniques to optimize the design parameters of the controller [52][53][54][55].…”