Abstract
This study researched, analyzed, and applied a field-programmable gate array (FPGA) to develop a control system for the DELTA robot with three pneumatic actuation subsystems. To achieve positioning accuracy, a dual-feedback control framework is first applied to control the position of a rodless pneumatic actuator. The inner pressure difference between the actuator and cylinder was used as feedback signals. Then, the derived dynamic model is included in the control strategy to conduct decoupling and system linearization on the nonlinear parallel manipulator using inverse dynamic control. Additionally, to realize the operation of the integrated control system under the FPGA environment, a multilayered neural network framework is designed to learn the inverse dynamic control behaviors. The motion control experiments involving single- and three-axis translational parallel manipulators revealed that the established FPGA-based control system exhibits high precision over the reachable workspace regarding the DELTA robot's three-dimensional trajectory tracking control.
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