Mobile Manipulator with Resolved Acceleration and Knowledge-Based Fuzzy Active Force Control

Perfect motion control of a mobile robotic system combining both the kinematic and dynamic aspects is still regarded as a challenging and complex problem to deal with. The proposed research study is aimed towards realizing the solution through the application of a novel and robust intelligent Active Force Control (AFC) based strategy to control a differentially-driven wheeled Mobile Manipulator (MM) system with nonholonomic constraint. The scheme incorporates an intelligent mechanism using a Knowledge-Based Fuzzy (KBF) algorithm to compute the essential estimated parameter in the AFC loop to trigger the compensation effect. A set of knowledge is investigated based on a priori knowledge with respect to a hypothesis that there exists a close relationship between the signal patterns of the generated tracking error with the actual velocity and the estimated inertial parameters of the MM system. The feasibility of implementing a Resolved Acceleration Control (RAC) technique as a kinematic-based feedback controller for the MM is first explored. The system is further consolidated with the inclusion of an intelligent AFC with KBF element that is directly embedded in cascaded form with the RAC part, serving as a dynamic-based scheme for the enhancement of the overall control scheme. The robustness of the proposed AFC-based scheme is rigorously tested with the application of the introduced disturbances in the form of constant braking torques, impact and vibratory excitations. The robust and accurate trajectory tracking performance of the system is particularly highlighted in the study to illustrate the practical viability of the proposed scheme.

Keywords: Mobile manipulator, knowledge-based fuzzy active force control.