Fig 7 - uploaded by Damien Charles Chablat
Content may be subject to copyright.
Source publication
In this paper, model-free based control strategies are applied to a finger of a gripper activated by Mckibben pneumatic muscles. In order to emulate the grasping phase of an object using a simple finger, a ”hybrid” type model-free based control strategy is proposed to manage the effort control phase, and the object release phase. The strategy is di...
Contexts in source publication
Context 1
... return of the finger to its initial position is ensured by the stiffness of the pneumatic muscle. The parts of the finger are made from machined aluminum whereas the gripping surface is made from polymer ( Figure 7) that makes the contact softer and increases the sliding coefficient. ...
Context 2
... measurement is provided by two sensors: an angle sensor (Magnetic Encoder RLS RM08) integrated in the rotation axis of the finger and a force sensor FSR (IE FSR X 402) between the polymer/aluminum contact surface and the finger (Fig. 7). These two sensors provide analog 0-5V signals. These latter are filtered thanks to a standard 20 Hz-fourth-order Butterworth filter 1 . The velocity is obtained by the differentiation of the position ...
Citations
... For example, in the work [11], a control design based on reinforcement learning was introduced for a pneumatic gearbox actuator. Other modern and adaptive approaches have also been considered and applied to PAM systems, such as model-free techniques for gripper fingers [12] and adaptive controllers for PAM subjects [13][14][15][16]. Fuzzy logic control, which offers advantages in handling complex systems, has been extensively studied and combined with conventional methods to enhance control quality [17][18][19][20][21][22][23], etc. ...
This study presents a novel approach to enhance the control of Pneumatic Artificial Muscle (PAM) systems by combining Sliding Mode Control (SMC) with the Radial Basis Function Neural Network (RBFNN) algorithm. PAMs, when configured antagonistically, offer several advantages in creating human-like actuators. However, their inherent nonlinearity and uncertainty pose challenges for achieving precise control, especially in rehabilitation applications where control quality is crucial for safety and efficacy. To address these challenges, we propose an RBF-SMC approach that leverages the nonlinear elimination capability of SMC and the adaptive learning ability of RBFNN. The integration of these two techniques aims to develop a robust controller capable of effectively dealing with the inherent disadvantages of PAM systems under various operating conditions. The suggested RBF-SMC approach is theoretically verified using the Lyapunov stability theory, providing a solid foundation for its effectiveness. To validate its performance, extensive multi-scenario experiments were conducted, serving as a significant contribution of this research. The results demonstrate the superior performance of the proposed controller compared to conventional controllers in terms of convergence time, robustness, and stability. This research offers a significant contribution to the field of PAM system control, particularly in the context of rehabilitation. The developed RBF-SMC approach provides an efficient and reliable solution to overcome the challenges posed by PAMs’ nonlinearity and uncertainty, enhancing control quality and ensuring the safety and efficacy of these systems in practical applications.
