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.