An Adaptive Inertia Gain Model‐Free Control for Robust Uncertain Dynamics Tolerant Control of Hybrid Mechanism Under Torque Input Limitation

In a hybrid robot mechanism, disturbances and significant payload variations with bounded torque inputs constitute a significant problem. In view of these uncertain dynamics, a new model‐free robust adaptive control is developed using: nonlinear proportional‐derivative ideal velocity feedback (NPD‐IVF); novel inertia‐gain adaptation control; and time‐delay control (TDC). The proposed control scheme has four components: a time‐delay estimation (TDE), a new NPD‐IVF desired error dynamics, an inertia‐gain adaptation law, and a saturation function (applied to guarantee torque input boundedness). The TDE component is used to get the lumped system dynamics thereby establishing a practical model‐free framework. The NPD‐IVF is designed to enhance the controller, reduce inherent estimation error and improve the position tracking performance. A gain‐adaptation law exploits a neuro‐fuzzy network that leads to a real‐time dynamic update of the inertia gain matrix; this tolerates significant variations in payload and disturbances. Cumulative effects of these four components result in the proposed control being input torque bounded, model‐free, improved accuracy and robust. Experimental comparisons were made using a prototype hybrid mechanism for vehicle electro‐coating conveying with various payload conditions to verify the effectiveness of the proposed control.