Modelling and PD control of a novel reconfigurable robotic machining manipulator

Abstract

Robotic machining is being applied in the manufacture of a broad spectrum of products and in a diverse industrial sectors ranging from aerospace, automotive to fashion. This paper presents a conceptual design, modelling and proportionalderivative (PD) control of a four degree-of-freedom (DOF) robotic machining manipulator (RMM) for use in a reconfigurable manufacturing system (RMS). The conceptual design of the RMM is introduced, followed by its mathematical modelling, and then the PD controller for the multi-input/multioutput (MIMO) system is designed for position tracking in absence of a dither signal. The simulation results justify the application of the PD controller through the set point tracking performances.

Iterative Learning Control with Desired Gravity Compensation under Saturation for a Robotic Machining Manipulator

“…The workspace of the realized configuration is customized for small parts in the range of 150 mm × 150 mm × 150 mm (6 × 6 × 6 ). This workspace is suitable for small parts in industrial production [47].…”

“…The other end connects the manipulator through the motor case for revolute motion 3 ( ) (position 4) to link two (position 5). Link two is coupled with the prismatic link 3, 4 ( ), designated as a complex tool holder consisting of a linear actuator case and machining motor case (position 6) [47].…”

“…The Lagrange formulation leads to a compact system of equations of motion, which is appropriate for modelling and control engineering applications and incorporates considerable redundancy. We follow the Lagrange formulation in this paper [47,51].…”

“…RMM was presented in [47], with conceptual design and physical and mathematical models being introduced. Note that we chose a serial-link robot, which is a more challenging case than a parallel-link robot structure, in terms of rigidity.…”

“…(1) Design of hybrid proportional + derivative controller with desired gravity compensation and the iterative learning control approach for a 4-degree-of-freedom (DOF) nonlinear, complex, and uncertain RMM under bounded actuators' inputs. [47]. Legend: 1: support frame, 2: complex rolling slider, 3: robot manipulator, 4: direct drive motor case, 5: link 2, 6: tool post, 7: cutting tool, and 8: work holder.…”