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The Pneumatic actuation systems are widely used in industrial automation, such as drilling, sawing, squeezing, gripping, and spraying. Also, they are used in motion control of materials and parts handling, packing machines, machine tools, and in robotics; e.g. two-legged robot. In this paper, a Neural Network based PI controller and Neural Network based PID controller are designed and simulated to increase the position accuracy in a pneumatic servo actuator. In these designs, a well-trained Neural Network provides these controllers with suitable gains depending on feedback representing changes in position error and changes in external load force. These gains should keep the positional response within minimum overshoot, minimum rise time and minimum steady state error. A comparison between Neural Network based PI controller and Neural Network based PID controller was made to find the best controller that can be generated with simple structure according to the number of hidden layers and the number of neurons per layer. It was concluded that the Neural Network based PID controller was trained and generated with simpler structure and minimum Mean Square Error compared with the trained and generated one used with PI controller.