This article presents a neural network–based adaptive composite dynamic surface control strategy for high-accuracy tracking control of electro-hydraulic system with very low velocity. The input saturation of the servo-proportional valve is tackled by designing a smooth function. Since the unknown nonlinear friction mainly centralizes in the Stribeck effect area, and acts as the dominant factor affecting the control performance, the very low–velocity trajectory is always difficult to be tracked in the usual electro-hydraulic system. To efficiently estimate and compensate the unknown nonlinear friction of the electro-hydraulic system, a serial–parallel estimation model and radial basis function neural network are developed. Dynamic surface control technique is utilized to design the composite controller, and the “explosion of complexity” problem which is inherent in the traditional backstepping method is avoided. Compensating signals are designed to eliminate the effect of the known error caused by the first-order filter in traditional dynamic surface control method. And, the corresponding compensated tracking errors are integrated into the adaptive law to improve the estimation capability of the radial basis function neural network. Simulation and experimental results are given to demonstrate the effectiveness of the proposed control method, and this control method can be extended to other strict-feedback systems with unknown nonlinearity.
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