Abstract
In this paper, a novel adaptive robust control (ARC) scheme is proposed for electro-hydraulic servo systems (EHSSs) with uncertainties and disturbances. All dynamic functions in system dynamics are effectively approximated by multi-layer radial basis function neural network (RBF NN)-based approximators with online adaptive mechanisms. Moreover, neural network-based disturbance observers (NN-DOBs) are established to actively estimate and efficiently compensate for the effects of not only the matched/mismatched but also the imperfections of RBF NN-based approximators on the control system. Based on that, the nonlinear robust control law which integrates RBF NNs and NN-DOBs is synthesized via the sliding mode control (SMC) approach to guarantee the high-accuracy position tracking performance of the overall control system. Furthermore, the problem of the combination between DOBs and RBF NNs is first introduced in this paper to treat both disturbances and uncertainties in the EHSS. The stability of the recommended control mechanism is proven by using Lyapunov theory. Finally, numerical simulations with several distinct frequency levels of reference trajectory are conducted to convincingly demonstrate the effectiveness of the proposed approach.
Highlights
Hydraulic servomechanisms have been broadly employed in various industrial appli cations—e.g., hydraulic robot manipulators [1], hydraulic press [2], load simulators [3], vehicle active suspension systems [4], and so on—due to their superiorities such as a high power-to-weight ratio, fast and smooth response, high stiffness, and ability to generate a tremendous force/torque [5]
Model-based control strategies for electrohydraulic servo systems (EHSSs) such as feedback linearization control (FLC) [7], backstepping control (BC) [8], and adaptive control (ADC) [9] have been widely employed to achieve a better tracking performance. In these control methods, the system dynamics are taken into account in control design, they are sensitive to the modeling uncertainties and disturbances that naturally exist in EHSSs
The results demonstrate the effectiveness of the suggested control strategy in which the combination of disturbance observer (DOB) and neural networks (NNs) is able to effectively deal with full unknown system dynamics and both mismatched and matched disturbances
Summary
Hydraulic servomechanisms have been broadly employed in various industrial appli cations—e.g., hydraulic robot manipulators [1], hydraulic press [2], load simulators [3], vehicle active suspension systems [4], and so on—due to their superiorities such as a high power-to-weight ratio, fast and smooth response, high stiffness, and ability to generate a tremendous force/torque [5]. Extended state observers (ESOs) were employed [27,28] for EHSSs to estimate both unmeasurable system states and matched/ mismatched disturbances; high-accuracy tracking performances were achieved In another approach to cope with disturbances, some DOB designs based on exact differentiators [29] have been applied in hydraulic systems. A robust control law based on the RBF NNs and DOBs is synthesized to guarantee the high-accuracy tracking performance of the EHSS control system under the impacts of large model uncertainties and disturbances. 4. The combination of RBF NNs and DOBs in order to take all their advantages is originally introduced to efficiently treat both full model uncertainties and disturbances in the dynamics of EHSSs. The remainder of the paper is organized as follows.
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