Robust Adaptive Fixed-Time Sliding Mode Control for Cable Driven Parallel Robots

Cable-driven parallel robots (CDPRs) are a typical type of parallel robot in which the end-effector is actuated by lightweight cables. To achieve fast and high-precision trajectory tracking of the end-effector under input saturation, a fixed-time nonsingular fast terminal sliding mode control (NFTSMC) law based on a radial basis function neural network (RBFNN) is proposed. The proposed scheme integrates the RBFNN method with an NFTSMC structure. First, the RBFNN is employed to approximate the unknown dynamics and lumped disturbances of the CDPRs. Second, a fixed-time robust control term based on the NFTSMC surface is introduced to enhance robustness and convergence speed. Under the proposed scheme, the fixed-time stability of the closed-loop system in the presence of external disturbances is demonstrated using the Lyapunov theorem. Finally, simulation comparisons under three different initial conditions are conducted to validate the performance improvements of the proposed algorithm.