A New Adaptive Robust Sliding Mode Control for High-Precision PKMs: Design, Stability Analysis, and Experiments

This paper proposes a novel adaptive feedback sliding mode control for parallel kinematic manipulators (PKMs), built on the conventional model-based sliding mode control. This structure was chosen for its robustness towards uncertainties and external disturbances. The contribution of this research is the inclusion of a feedforward term based on the dynamic model of the PKM in the control design. This feedforward term compensates for high nonlinear dynamics, as well as avoids measurement noise in control inputs. Additionally, the fixed feedback gains of the sliding mode controller are redesigned as adaptive gains, which provide better correction actions for larger tracking errors. A stability analysis of the proposed control solution, based on Lyapunov's method, is provided. The effectiveness of the proposed controller is demonstrated through its application to a Gough-Stewart platform (MISTRAL parallel robot) in various real-time experimental scenarios. The proposed approach is compared with various controllers demonstrating its superiority. It ensures nominal root mean square tracking errors of (i) about 22 mu m in joint space, and (ii) about 27 mu m in traveling plate Cartesian position.