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Zhi L.,Lishui University | Zhu Y.,Lishui University | Wang H.,Lishui CA Steer by Wire Technological Co. | Wang H.,Swinburne University of Technology | And 2 more authors.
Neural Computing and Applications | Year: 2014

A new recurrent neural model for crack growth process of aluminium alloy is developed in this work. It is shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The output feedback loops in the neural model play the role of capturing the fine changes of crack growth dynamics. The Extreme Learning Machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable-amplitude loading can be accurately modeled. The simulation results with the averaged experimental data of the 2024-T351 aluminium alloy show that the excellent modeling and prediction performance of the recurrent neural model can be achieved for fatigue crack growth of aluminium alloys. © 2014 The Natural Computing Applications Forum


Wang H.,Hefei University of Technology | Man Z.,Swinburne University of Technology | Kong H.,Hefei University of Technology | Zhao Y.,Lishui CA Steer by Wire Technological Co. | And 4 more authors.
IEEE Transactions on Industrial Electronics | Year: 2016

This paper proposes a novel adaptive terminal sliding-mode (ATSM) control scheme for a Steer-by-Wire (SBW) vehicle. It is shown that the developed ATSM controller can drive the closed-loop error dynamics to converge to zero in a finite time, where adaptive laws are applied to estimate the uncertain bounds of the system parameters and disturbances in Lyapunov sense. Compared with the sliding-mode-based SBW control systems, the proposed ATSM control not only assures the finite-time error convergence and strong robustness with respect to parameter uncertainties and varying driving conditions, but also requires no prior knowledge of the system parameters and road information. Experimental results from an SBW vehicle are demonstrated to verify the remarkable performance of the proposed control in terms of the robustness, error convergence, and road disturbance attenuation, in comparison with other control strategies. © 1982-2012 IEEE.


Wang H.,Swinburne University of Technology | Wang H.,Lishui CA Steer by Wire Technological Co. | Xu Z.,Lishui CA Steer by Wire Technological Co. | Do M.T.,Swinburne University of Technology | And 3 more authors.
Neural Computing and Applications | Year: 2015

This study develops a neural-network-based robust control scheme for steer-by-wire systems with uncertain dynamics. The proposed control consists of a nominal control and a nonsingular terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the effects of uncertainties can be effectively eliminated in the closed-loop system. Using the proposed neural control scheme, not only the robust steering performance against parameter variations and road disturbances is obtained, but also both the control gain and the control design complexity are greatly reduced due to the use of the RBFNN. Simulation results are demonstrated to verify the superior control performance of the proposed control scheme, in comparison with other control strategies. © 2015, The Natural Computing Applications Forum.

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