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In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.

Automatic parking path optimization is a key point for automatic parking. However, it is difficult to obtain the smooth, accurate and optimal parking path by using traditional automatic parking optimization algorithms. So, based on the automatic parking path optimization model for cubic spline interpolation, an improved automatic parking path optimization based on the immune moth flame algorithm is proposed for intelligent vehicles. Firstly, to enhance the global optimization performance, an automatic parking path optimization model for cubic spline interpolation is designed by using shortest parking path as optimization target. Secondly, an improved immune moth flame algorithm (IIMFO) based on the immune mechanism, Gaussian mutation mechanism and opposition-based learning strategy is proposed, and an adaptive decreasing inertia weight coefficient is integrated into the moth flame algorithm so that these strategies can improve the balance quality between global search and local development effectively. Finally, the optimization results on the several common test functions show that the IIMFO algorithm proposed in this paper has higher optimization precision. Furthermore, the simulation and semi-automatic experiment results of automatic parking path optimization practical cases show that the improved automatic parking path optimization based on the immune moth flame algorithm for intelligent vehicles has a better optimization effect than that of the traditional automatic parking optimization algorithms.

In this paper, an improved auto disturbance rejection controller (ADRC) is applied to permanent magnet synchronous motor (PMSM) speed control system. Specifically, the improved ADRC is proposed to solve the defect that the nonlinear function of traditional ADRC is not differentiable at the piecewise point. In addition, considering that there are numerous parameters to be optimized in ADRC, an improved moth-flame optimization (MFO) is proposed to obtain ADRC parameters, and adaptive inertia weight coefficient and optimization mechanism based on the fusion of refraction operation and chaotic search are proposed to improve the convergence speed and optimization accuracy for MFO. Simulation and experimental results show that the improvement strategies proposed in this paper can increase the optimization performance for MFO algorithm effectively so that obtained more appropriative ADRC parameters. The improved ADRC can effectively improve the tracking control performance of PMSM speed control system.

A finite-time adaptive neural network position tracking control method is considered for the fractional-order chaotic permanent magnet synchronous motor (PMSM) via command filtered backstepping in this paper. Firstly, a neural network with a fractional-order parametric update law is utilized to cope with the nonlinear and unknown functions. Then the command filtered technique is introduced to address the repeated derivative problem in backstepping. In addition, a novel finite-time control method is proposed by employing the fractional-order terminal sliding manifolds, designing the error compensation mechanism and the new virtual control laws. The finite-time convergence of the tracking error can be guaranteed by the proposed controller. Finally, the designed control method is verified by simulation results. IntroductionFractional calculus is an evolving theory in many relevant sciences which is opening new areas in mathematics. It is a generalization of conventional differentiation and integration to arbitrary order [1]. Due to its potential applications and interesting properties, the fractional calculus has captured considerable attention from scholars in many fields [2][3][4][5]. Currently, many interesting results associated with the fractional calculus have been given [6][7][8]. The research shows that the fractional-order controllers are more advantageous than that of traditional integer-order ones. And also, some meaningful results have been reported on the stability problems in the scope of fractional calculus. For instance, by utilizing the fractional-order Lyapunov stability criterion, the robust consensus tracking problem is investigated in [9] for fractional-order multiagent systems with external disturbances and heterogeneous unknown nonlinearities. Based on the Chebyshev neural network (NN) technique, an adaptive synchronization approach is proposed in [10] for a class of fractional-order micro-electro-mechanical systems with chaotic oscillation. Therefore, the research of fractional-order system is a meaningful work.