Composite Learning Neural Network Tracking Control of Articulated Soft Robots

Abstract

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)-VSAs to guarantee tracking performance without exact robot models. Composite learning resorts to memory regressor extension to enhance adaptive parameter estimation such that parameter convergence can be guaranteed without the stringent condition of persistent excitation. In the proposed method, an NN-based controller is constructed for the position tracking of AA-VSA-driven ASRs, and an NN weight update law based on composite learning is developed to enhance online modeling and control capabilities. Experiments are carried out on an ASR with three degrees of freedom and qbmove Advance actuators (a kind of AA-VSAs), which have validated the effectiveness and superiority of the proposed method in terms of modeling and tracking accuracy compared with existing control methods.