In this paper, a fuzzy disturbance observer and a high-gain disturbance observer based on a variable structure controller are applied to deal with imprecise multi-shaft with web materials linkage systems taking into account the variation of the moment of inertia. Specifically, a high-gain disturbance observer and an adaptive fuzzy algorithm are separately applied to estimate system uncertainties and external disturbances. The high-gain disturbance observer is designed with auxiliary variables to avoid the amplification of the measurement disturbance, and the fuzzy disturbance observer has the advantage that it does not depend on model information. The convergence properties of the tracking error are analytically proven using Lyapunov’s theory. The obtained numerical results demonstrate the validity and the adaptive performance of the proposed control law in case the system is exposed to uncertainties and disturbances. Important remarks on the design process and performance benchmarks of the two observers are also demonstrated.

Series Elastic Actuators (SEA) provide improved control, safety, energy efficiency, and performance compared to traditional rigid actuators, making them well-suited for various applications in robotics, rehabilitation, and human-robot interaction. However, the inherent flexibility of SEAs can lead to oscillations in the position of SEA robots. This paper introduces a radial basis function (RBF) neural network-based adaptive super-twisting sliding mode control approach for position tracking of SEA robots. The robust control part of the proposed strategy, the super-twisting sliding mode control, effectively provides stability and robustness, demonstrates finite-time convergence, and suppresses oscillations caused by the joints. Beyond implementing robust control, the Radial Basis Function (RBF) neural network coordination is crucial for effectively approximating the unknown components of the manipulator dynamical model and uncertainties encountered in practical applications. The unknown nonlinearities are approximated through an RBF neural network, wherein the network's weight parameters are dynamically adjusted in real-time based on adaptive laws. Leveraging the RBF model, an adaptive control algorithm is formulated via the Lyapunov synthesis approach. Simulation outcomes corroborate the efficacy of the proposed controller in attaining accurate position tracking while effectively reducing oscillations. Mathematics Subject Classification (2020) 93C10 · 93C40 · 93B52 ·70Q05