3 accepted papers
Controlling articulated soft robots (ASRs) driven by variable stiffness actuators (VSAs) is challenging because they are highly nonlinear and difficult to model accurately. This paper proposes an efficient neural network (NN) learning control solution for ASRs driven by agonistic-antagonistic (AA)-V
Articulated soft robots (ASRs) driven by variable stiffness actuators (VSAs) are challenging to control well due to their highly nonlinear dynamics and difficulties in accurate modeling. The paper proposes a locally weighted learning (LWL)-based robust composite learning control (RCLC) solution for
Variable stiffness actuators (VSAs) are an important class of compliant actuators that can benefit the robustness and task adaptability of robotic systems. However, controlling VSAs is still challenging as VSAs are difficult to model exactly and have highly nonlinear characteristics. This study appl