Pneumatic artificial muscles (PAMs) are artificial devices that can simulate the mechanics of human muscles, and have shown great promise in industries requiring human-robot interaction systems. Despite their potential, controlling the trajectory performance of PAM-based systems is challenging owing to their nonlinear characteristics. Now, researchers have developed a novel adaptive sliding mode controller that uses fuzzy logic to estimate PAM-based system's parameters, promising enhanced tracking accuracy and adaptability compared to traditional control methods.
In recent years, pneumatic artificial muscles have emerged as promising actuators for simulating human-like movements, with prominent applications in various industries including robotics, rehabilitation, and prosthetics. PAMs are usually composed of rubber and covered with braided yarn and can mimic the mechanics of human muscles. They can stiffen and contract on being supplied with pressurized air and soften and lengthen upon releasing the air.
To overcome these limitations and address this open problem, a group of researchers led by Associate Professor Ngoc-Tam BUI of the Innovative Global Program, College of Engineering, Shibaura Institute of Technology in Japan, along with Dr. Quy-Thinh Dao of Hanoi University of Science and Technology, has proposed a novel solution.
The researchers first developed a sliding mode controller with a control signal that incorporates a special variable to estimate the disturbances and improve the control performance. Next, they designed an adaptive fuzzy algorithm, wherein parameter vectors of the component rules are automatically updated by an adaptive law, to compute the disturbance variable. The stability of the developed ASFMC algorithm was then analyzed using the Lyapunov stability condition .
These findings thus point to the potential of the novel AFSMC approach for integration into robotic rehabilitation devices, assistive devices, and physical therapy equipment for precise and personalized therapy. Moreover, this approach can aid in the design and development of advanced prosthetic limbs for enhanced functionality and rehabilitation outcomes.
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