Abstract
In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.
Highlights
Pneumatic actuators have been widely used in industrial applications due to the advantages of having a high power/mass ratio, being low cost, clean, and serviced [1,2,3]
The real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control
This paper aims to synthesize a control law for a single-rod pneumatic cylinder system to follow x1r as closely as possible and to determine how to compensate unmodeled dynamics, disturbances, and residual estimation errors with a neural networks (NN) under the Adaptive robust control (ARC) frame
Summary
Pneumatic actuators have been widely used in industrial applications due to the advantages of having a high power/mass ratio, being low cost, clean, and serviced [1,2,3]. Neural networks (NN) have been used in controller design for their excellent ability to approximate arbitrary unknown nonlinearities [21], including the unmodeled dynamics and random disturbances. NN controllers have been designed for high nonlinear pneumatic systems to obtain better model compensation [22,23,24,25]. The ARNNC approach is used to estimate the unknown parameters of the known structure part but to approximate the unmodeled dynamics, random disturbances, and residual estimation errors. With these compensations, a better trajectory tracking performance of the pneumatic system is achieved. A single-rod pneumatic cylinder depicted in Figure 1 is considered as the object in this paper
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