This paper presents a system control design on 2 axes “Delta Industrial Robot”. The control system utilizes both the embedded technology and the “Raspberry Pi" which is a low power consumption PC with open source operating system. The user can communicate with the PC and FPGA (Field Programmable Gate Array) via the system GUI interface. The FPGA acts as dedicated hardware to provide both the efficiency and flexibility for the closed-loop servo control system.To calculate all related kinematics equations for the robot, look up table approach has been implemented to reduce the calculation complexity. Python programs are also implemented to create and search the look up tables. The inverse kinematics is the key to find the position of the robot. A test load of 0.2 kg at the velocity of 2.618 m/s, the S.D. of the end effector’s position of the test result is within 0.06 mm.

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Because of the dead-zone nonlinearity characteristic in pneumatic system, high steady state error and overshoot have occurred in the position response as a typical imperfect implementation of associated control dynamic. In this situation, there is a necessity to be able to effectively utilizing the intelligent controller framework. The primary purpose of this research is to develop a control algorithm based on precompensated neuro-fuzzy for a typical pneumatic servo system. This precompensator neuro-fuzzy combined with fuzzy controller will be implemented to enhance the overall performance. Experiments were carried out and the experimental results illustrated that the above precompensated intelligent framework can improve the overall the performance.