A Novel Bio-Intelligent Real-Time Redundancy Resolution Method for Cable-Driven Parallel Manipulators

Cable-driven parallel manipulators (CDPMs) are widely used where there is a need for high speeds and high accelerations, thanks to the low inertia and low cost of their links. However, cables can only exert tensional force. Due to this inherent property of the cables, CDPMs are required to be designed with at least one degree of redundancy in their joint space, to be able to generate an acceptable range of wrench within the workspace. Meanwhile, redundancy in joint space leads a problem called redundancy resolution, which is basically a force distribution from workspace to joint space. In this research, inspired by a simple mathematical model of the limbic system of the brain, a novel real-time redundancy resolution method is proposed. The presented method utilizes the kinematic information of the end-effector to find the solution to the redundancy resolution problem. The results show that this Brain Emotional Learning (BEL) method has a better performance in comparison with some common numerical optimization methods solved with a nonlinear programming solver like fmincon function in MATLAB, in terms of computational cost, continuity of the profile of the cables forces, and robustness.