Design of intelligent fuzzy neural network control for variable stiffness actuated manipulator for uncertain payload

Compliant manipulators with variable stiffness actuation systems are crucial for safety in physical human-robot interactions, improving performance during unexpected collisions. However, their inherent compliance poses motion control challenges, especially with rapid stiffness changes and uncertain end-effector loads, adversely affecting controller stability and accuracy. To address uncertainties from stiffness variations and changing payloads, this paper proposes the Bidirectional Fuzzy Brain Emotional Learning controller—a fuzzy neural network with a unique bidirectional brain emotional learning algorithm for weight adaptation—that effectively handles varying stiffness and payload uncertainties. Additionally, a novel algorithm for systematic establishment of fuzzy layers is presented which significantly reduces the effort and time for implementation. Simulation and experimental results demonstrate the proposed controller’s superior adaptability and tracking performance under varying stiffness and payload uncertainties compared to the conventional PID controller. This new algorithm for fuzzy layer setup can serve as a default for all fuzzy neural network-based controllers, enhancing their ease of use across various applications. The controller can also be directly extended to grasping uncertain objects using variable stiffness actuated systems, improving safety and reliability in physical human-robot interactions.