This paper proposes a new adaptive time- delay control (ATDC) scheme for cable-driven manipulat- ors. The proposed ATDC scheme contains three elements, i.e., time-delay estimation (TDE), injected dynamics and adaptive laws. The TDE utilizes intentionally time-delayed signals to estimate the unknown system dynamics and brings a fascinating model-free nature. The injected dyna- mics employ a fractional-order nonsingular terminal sliding mode (FONTSM) manifold and a fast-TSM-type reaching law, thus it can ensure fast dynamical response and high tracking accuracy in both sliding mode and reaching phases. The adaptive laws are utilized to obtain good robustness and effective suppression of the chatters simultaneously. Moreover, the continuous and chatter-free adaptive gains are used to enhance the control performan- ce under time-varying disturbances. The tracking error is proved to be uniformly ultimately bounded (UUB) using Lyapunov approach. Finally, the superiorities of the propo- sed ATDC scheme are demonstrated by three experiments using a cable-driven manipulator.

For high control performance of cable-driven manipulators, we design a new adaptive time-delay control (ATDC) using enhanced nonsingular fast terminal sliding mode (NFTSM). The proposed ATDC uses time-delay estimation (TDE) to acquire the lumped dynamics in a simple way and founds a practical model-free structure. Then, a new enhanced NFTSM surface is developed to ensure fast convergence and high control accuracy. To acquire good comprehensive performance under lumped uncertainties, in this article we propose a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately. Benefitting from the TDE and the proposed enhanced NFTSM surface and adaptive control gain, our proposed ATDC is model-free, fast response, and accurate. Theoretical analysis concerning system stability, and control precision and convergence speed are given based on Lyapunov theory. Finally, the advantages of our ATDC over existing methods are verified with comparative experiments.