Adaptive neural network control of cable-driven parallel robots with input saturation

Abstract

In this paper, an adaptive neural trajectory tracking controller with a bounded-input property is developed for cable-driven parallel robots (CDPRs). Due to the fact that the cables in these robotic systems should always remain in tension in a trajectory tracking task, a more precise tracking controller is needed for CDPRs comparing to the conventional rigid-link robotic systems. To achieve this objective, this paper proposes a new nonlinear controller with a learning ability for the robot dynamics. The controller includes an adaptive multi-layer neural network to compensate for the modeling uncertainties of the system, and utilizes an auxiliary dynamics to provide a priori bounded tension command for the cables. In addition to this novelty, a bounded-input controller is designed for the dynamics of the actuators, coupled with gearboxes, in order to follow the tensions, defined through the controller of robot dynamics. The boundedness feature of the controller facilitates considering the upper limit of the actuators in choosing the control gains. Stability of the whole system is well studied, and the uniformly ultimately bounded stability is guaranteed. The effectiveness of the proposed control scheme is validated through simulations on a 4-cable planar robot in both nominal and perturbed conditions.