In this paper, a finite time robust controller is proposed for tracking the speed and position of high speed trains. Considering the aerodynamic flag, mechanical rolling resistance, additive resistance and wind gust, the dynamics of high speed train with time-varying delay is established in longitude. Aerodynamic flag and mechanical rolling resistance are seen as the coefficient of the state variables, wind gust and additive resistance are seen as the external disturbance, and time-varying delay in speed are considered as the train is often running in bad weather or severe working conditions. Then a delay-dependent finite time robust controller is designed. This controller not only guarantees the closed-loop error dynamics error is finite time boundness, but also is robust to the external disturbance consisting of wind gust and additive resistance in finite time. Sufficient conditions of the controller design are given, and the gain matrices of the controller are formulated as linear matrix inequality. A 5-car simulation example is given to show the effectiveness of the proposed method.
In this paper, novel fault estimation and fault-tolerant control methods are proposed for dynamics of high-speed train based on descriptor systems with uncertainties in finite frequency domain. Dynamics of high-speed train is established based on multi-particle model considering that basic resistance is seen as the coefficient of state variables, and additive resistance and the operating noise are seen as multi-source disturbance. Concurrent actuator, sensor faults, and wind gust are considered simultaneously; wind gust is modeled as a disturbance generated by the exogenous system, and an uncertain descriptor system with actuator fault and the exogenous disturbance is established by seeing the sensor fault of high-speed train as the state variables. A robust disturbance-observer-based fault estimation method is proposed to decouple the non-linearity of the descriptor system, so that the combining estimation of the fault and wind gust is implemented. This observer has an unknown input structure, and its gain matrices are formulated as linear matrix inequalities. The observer not only guarantees the augmented state estimation error is asymptotic stable but also the actuator fault estimation and wind gust estimation errors are robust to the multi-source disturbance and the uncertainties. Based on the estimation results, the fault-tolerant controller associated with the state estimation, faults estimation, and wind gust estimation results is proposed to implement a stable close-loop fault-tolerant control for dynamics of high-speed train. Simulation examples are given to illustrate the effectiveness of this method.
In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.
In this paper, an optimized long short-term memory (LSTM) network is proposed for the remaining useful life (RUL) prediction of the rolling bearings based on whale optimized algorithm (WOA). The multi-domain features are extracted to construct the feature dataset as the single domain features are difficult to characterize the performance degeneration of the rolling bearing. Considering the possible gradient explosion by training of the rolling bearing lifetime data and the difficulties in selecting the key network parameters, an optimized LSTM network, namely, WOA-LSTM network is proposed. Experiment results show that, compared with the LSTM network, the RUL prediction accuracy of the rolling bearing are improved by the proposed WOA-LSTM network.
In this paper, a spider monkey optimisation (SMO) algorithm is utilised to identify the parameters of the permanent magnet synchronous motor (PMSM), considering the parameters vary during the motor operation, which affects the sensorless control (SC) performance of the motor. An improved sliding mode observer (SOBS) is proposed for estimating the position and speed of the rotor. First, the SMO algorithm is used to identify the parameters of PMSM. Then, based on the identification results, an improved SOBS is proposed by a piecewise Sigmoid function. Furthermore, the stator position and speed are estimated by extended state observer (ESO) and phase-locked loop (PLL). Finally, a comparison simulation scenario is provided to demonstrate the efficacy of the suggested approach.
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