Modeling and analysis of a cable-driven manipulator based on computed torque control

Abstract

Cable-driven manipulators have garnered increasing attention in robotics owing to their lightweight structure, flexible transmission mechanisms, and large operational workspace. However, the strong nonlinear dynamics, parameter coupling, and external disturbances inherent in cable-driven systems pose significant challenges to achieving high-precision motion control. To address these challenges, a robust trajectory-tracking control framework is developed by integrating computed torque control (CTC), time-delay estimation (TDE), and proportional–derivative (PD) feedback control. First, the nonlinear dynamics of the manipulator are transformed into an equivalent linear system using the computed torque method. The TDE mechanism is then employed to estimate modeling uncertainties and external disturbances. This approach reduces the reliance on accurate dynamic modeling. In addition, a PD feedback term is introduced to suppress high-frequency noise caused by the time-delay estimation process. To improve the accuracy and efficiency of feedforward compensation, an adaptive-weight particle swarm optimization (PSO) algorithm is developed to solve the inverse kinematics problem of the manipulator. The inertia weight is dynamically adjusted during the optimization process, which significantly improves the convergence speed and solution accuracy. Both simulations and experiments were conducted on a 12-degree-of-freedom cable-driven manipulator platform. The results verify the effectiveness of the proposed method.