An application of MNN trained by MEKA for the position control of pneumatic cylinder

As an important driving element, the pneumatic cylinder is being widely used in industrial applications because of simple, cheap and excellent performance. However, along with the developing of control technology, the requirement for control precision becomes higher and higher. In many case, in order to achieve satisfactory control performance, we have to consider the effect of a nonlinear factor contained in pneumatic cylinders. In order to solve the nonlinear problems of pneumatic servo system, in this paper we proposed a control scheme based on multilayer neural network (MNN) trained by multiple extended Kalman algorithm (MEKA). The test results of MNN controller trained by MEKA in a practical pneumatic servo system suggests the superior performance. As well as the experimental results also show that the proposed method has less sensitivity than the neural network trained by simple gradient descent training algorithms