In this paper, observer-based command filtered adaptive neural network tracking control problem is addressed for fractional-order chaotic permanent magnet synchronous motor (PMSM) with immeasurable state, parameter uncertainties and external load disturbance. First, Chebyshev neural networks are introduced to approximate the nonlinear and unknown functions. Next, a neural network reduced-order state observer is designed to obtain the unmeasured state. Then, the command filtering approach based on first-order Levant differentiator is developed to solve the “explosion of complexity” issue of backstepping, and a novel fractional-order error compensation mechanism is employed, which can remove the filtering errors in finite-time. After that, the continuous frequency distributed model is investigated to design proper Lyapunov function, it is demonstrated that the proposed control method not only ensures all signals in the fractional-order PMSM system are bounded, but also suppresses chaotic oscillation. Finally, simulation studies are provided to verify the correctness and effectiveness of the proposed scheme.

The tiltrotor aircraft consists of three primary flight modes, which are helicopter flight mode in low forward speed flight, airplane flight mode in high forward speed flight and conversion flight mode. This paper presents an active disturbance rejection controller for tiltrotor aircraft conversion flight. First, a tiltrotor aircraft flight dynamics model is developed and verified. Then, conversion flight control laws, designed via the active disturbance rejection control (ADRC) and sliding mode control (SMC) techniques, are proposed for the tiltrotor aircraft with model uncertainties and external disturbance, which are estimated with an extended state observer. Finally, the simulation of automatic conversion flight is carried out, which shows the effectiveness of the developed controller.

In this paper, an adaptive neural network (NN) control based on command filtered backstepping approach is presented for fractional-order permanent magnet synchronous motor (PMSM) with parameter uncertainties and unknown time delays. For the convenience of controller design, the state trajectories and phase portrait of the system are investigated to analyze the dynamics of the fractional-order PMSM. The unknown parameters and load torque disturbance in the system dynamics are approximated by using NNs, and the number of adaptive laws for the weight vector is curtailed to just one. To ensure orderly decay of the desired error trajectory, a model reference technique is also introduced to backstepping approach. The command filter technique, which can solve the “explosion of complexity” issue of backstepping, is extended to fractional-order nonlinear systems, and the error compensation mechanism is designed to overcome the shortcoming of the classical dynamics surface filter. The effects of time delays uncertainties are suppressed by employing proper Lyapunov-Krasovskii functions. From the Lyapunov stability theory, the design of the controller ensures all signals in the fractional-order PMSM system remain bounded, while the output error converges to a small region of the origin. Numerical simulations are given to show the correctness and effectiveness of the new design technique.

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.

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.

This article considers the problem of adaptive neural network control via command filtering for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints and parameter uncertainties. First, a neural network state observer based on a K-filter is established to reconstruct unmeasured feedback information. Then, the command filtered technology is used to overcome the inherent “explosion of complexity” problem under fractional-order framework. Furthermore, to eliminate the errors generated by filters, an error compensation system is used. Meanwhile, the nonlinear unknown functions are approximated by using neural networks. In addition, the barrier Lyapunov functions are designed to avoid the violation of the state constraints. Finally, the availability of the proposed control algorithm is revealed by numerical simulations.