Fuzzy-Based Control for Multiple Tasks With Human–Robot Interaction

Driven by the rise of collaborative robots, a lot of work has focused on the transparency and stability of physical human–robot interaction (pHRI), in which most of the efforts do not take the requirement of multiple tasks into account. However, the spectrum of applications for collaborative robots has been continuously broadened, and robots without the ability to perform multiple tasks simultaneously may not be capable of collaborating in certain scenarios. In this article, we provide a fuzzy-based multitask intelligent control framework of collaborative robots for pHRI. Our controller formulation consists of “outer-loop” and “inner-loop.” In the “outer-loop,” a fuzzy logic system predicts human desired motion trajectory for the robot to track. In the “inner-loop,” the robot is driven by a hierarchical multitask controller to track the trajectory generated by the “outer-loop” and perform other subtasks simultaneously. With null space projections, the whole task stack can be implemented in a strict task hierarchy in order of priority. The weight-tuning law of the FLSs and the hierarchical multitask control law are given based on Lyapunov stability analysis. The proposed control framework is applied to a mobile manipulator and the effectiveness is verified by exploratory experiments. Results confirm the effectiveness of the proposed control framework and compare its performance with other approaches.