In this paper, a passive-based adaptive robust super-twisting nonlinear controller (PBARSNC) is proposed for high accuracy torque tracking control of the novel electro-hydraulic loading system with disturbances and uncertainties. The construction of the stability of this electro-hydraulic control system is given using passivity theory that results in a passivity-based controller (PBC). Considering parameter uncertainties and constant or slowly varying disturbances, adaptive law is adopted in the passivity-based controller. Furthermore, super-twisting second-order sliding mode control is used to reject modeling uncertainties and matched disturbances. Passivity theory, adaptive method and super-twisting algorithm are synthesized via the recursive design method. The proposed passive-based adaptive robust super-twisting nonlinear control can guarantee the torque tracking performance in the presence of various uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive simulations are carried out to verify the high-accuracy tracking performance of the proposed control strategy.
Abstract. Accurate and significant wave height prediction with a couple of hours of warning time should offer major safety improvements for coastal and ocean engineering applications. However, significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction simulation a non straightforward task. The aim of the research presented in this paper is to improve predicted significant wave height via a hybrid algorithm. Firstly, empirical mode decomposition (EMD) is used to preprocess the nonlinear data, which are decomposed into several simple signals. Then, least square support vector machine (LSSVM) with nonlinear learning ability is used to predict the significant wave height, and particle swarm optimization (PSO) is implemented to automatically perform the parameter selection in LSSVM modeling. The EMD-PSO-LSSVM model is used to predict the significant wave height for 1, 3 and 6 hours leading times of two stations in the offshore and deep-sea areas of the North Atlantic Ocean. The results show that the EMD-PSO-LSSVM model can remove the lag in the prediction timing of the single prediction models. Furthermore, the prediction accuracy of the EMD-LSSVM model that has not been optimized in the deep-sea area has been greatly improved, an improvement of the prediction accuracy of Coefficient of determination (R2) from 0.991, 0.982 and 0.959 to 0.993, 0.987 and 0.965, respectively, has been observed. The proposed new hybrid model shows good accuracy and provides an effective way to predict the significant wave height for the deep-sea area.
This paper investigates the attitude control of rigid spacecraft with uncertainties and actuator faults. An immersion and invariance sliding mode control with time-delay estimation (IISMCTDE) is proposed to cope with uncertainties, disturbances, and actuator faults. First, time-delay estimation is utilized to reconstruct the total disturbances including uncertainties, disturbances, and actuator faults. Then, based on the time-delay estimation, an immersion and invariance sliding mode control (IISMC) scheme is developed to achieve accurate attitude tracking from the reconstructed knowledge. The proposed IISMCTDE can not only achieve finite-time convergence but also be simple and easy to operate. Simulations are provided to demonstrate the effectiveness of the proposed approach.
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