Chebyshev-neural-network-based adaptive fixed-time control of bilateral teleoperation

Most bilateral teleoperation researches focus on the system stability within time-delays. However, practical teleoperation tasks require high transient performance, such as convergence rate and precision, besides system stability. This paper investigates bilateral teleoperation controller design with transient performances. To ensure the transient performances and system stability simultaneously, an adaptive non-singular fixed-time controller is proposed to achieve practical fixed-time stability in the presence of system uncertainties and time-delays. In addition, a novel switching scheme is introduced to eliminate singularity phenomenon, which is caused by conventional terminal sliding manifolds. Moreover, Chebyshev neural network and adaptive law are combined with the fixed-time controller to estimate real-time system uncertainties. Finally, numerical simulations demonstrate the effectiveness and validity of the proposed methods.