In this article, a novel adaptive sliding mode control strategy is presented for attitude and altitude stabilization of a quadrotor unmanned aerial vehicle (UAV), where the input saturation is taken into consideration. The nonlinear dynamic model of quadrotor UAV subject to system uncertainty and external disturbance is first derived using the Euler–Lagrange mechanical equation. To ensure system stability, as well as eliminating the negative effects of saturation features, a saturated adaptive sliding mode control law (ASMCSAT) is proposed, which takes advantages of the sliding mode control and the adaptive control. Compared with conventional control strategies, better tracking performance and disturbance and uncertainty rejection can be achieved with the root mean square of the tracking error being reduced by 34.91% in vertical takeoff and 30.11% in trajectory tracking. Simulations and experiments are provided to verify the effectiveness and practicability of the proposed control method.

This paper proposes a practical adaptive fractional order (FO) terminal sliding mode control (SMC) strategy for tracking control of the linear motor. Compared with conventional fast nonsingular SMC, the proposed approach, with a FO integral sliding surface and the adaptive switching input, can obtain higher convergence precision, even though the motion control system suffers from system uncertainties. The adaptive term is designed to guarantee finite-time high-precision convergence of the sliding mode variable, and meanwhile to degenerate the effect of uncertainties by selecting the proper adaptive gain. Moreover, continuous input due to cancelling the sign term ensures that the motion control system is chattering-free. Finally, to further improve precision, we introduce the super-twisting sliding mode disturbance observer for reducing unknown bounded disturbance, i.e., the quantization noise caused by velocity estimation. Experimental results indicate that the control system with the proposed controller is easily implemented, and has higher tracking precision and considerable robustness to uncertainties compared with the existing controllers.

This paper presents a practical discrete-time fractional order terminal sliding mode (DFOTSM) control strategy for high-precision tracking tasks based on a linear motor. In particular, the practical parametric uncertainties involving sliding friction, uncertain payload, and disturbance in tracking tasks are considered in this paper. Combining Grünwald-Letnikov fractional order definition and terminal sliding mode technique, the proposed method synthesizes a novel DFOTSM control law to drive the sliding mode dynamics into the stable region in finite steps theoretically, even though the system is suffering from uncertainties and disturbances, and the motion on the surface can guarantee higher tracking precision than the conventional discrete-time terminal sliding surface by selecting suitable controller parameters. The theoretical analyses give out the guideline of parameter selection, and experiments are carried out on the linear-motor-based test platform to demonstrate that the proposed controller is easily implemented and can achieve high-precision tracking, fast response, and considerable robustness to uncertainties.

In this article, a fixed-time observer-based sliding-mode control strategy is proposed for a permanent-magnet synchronous motor. Both the current regulation loop (inner loop) and the speed-tracking loop (outer loop) are controlled by the designed fixed-time convergence sliding-mode controller, improving the control performance and guaranteeing the convergence in a fixed-time manner. To guarantee the robustness of the proposed control algorithm, an observer is constructed to estimate and attenuate the parameter uncertainties and load disturbance in the outer loop. The fixed-time convergence of the inner and outer loop error systems are proved. Moreover, a parameter tuning method of the designed controller is proposed to provide guidance for practical applications. Finally, experiments are conducted by using three different control schemes, that is, PI control, super-twisting sliding-mode control, and the controller designed in this article, the results of which show the effectiveness and advantages of the proposed control scheme.

In this article, a new fault diagnosis framework for proton exchange membrane fuel cells (PEMFCs) based on electrochemical impedance spectroscopy (EIS) and the deep learning method is proposed. Specifically, this work employs the PEMFC knowledge to drive the training process of the deep learning network, which makes it possible to improve fault diagnosis performance by deep learning algorithms with a limited scale of actual measured EIS data. A pretraining network is developed to predict the equivalent circuit model (ECM) parameters, which could reduce the time consumption of ECM parameter identification. Besides, considering that the ECM parameters are susceptible to significant changes due to nonfault operation, a fine-tuning network is designed to generate robust diagnosis features, which could support the proposed framework working in different environments. Moreover, the complex neural networks are adopted in the proposed framework to extract features from EIS data, which is composed of complex impedances. Finally, a new evaluation metric PScore is proposed to assess the performance of the diagnosis framework from the perspective of practical applications. The experiments are performed to demonstrate the effectiveness of each component in the framework, and the proposed algorithm has significant improvements in fault diagnosis performance and computational efficiency over traditional algorithms.

In this article, an observer-based prescribed performance speed control method is proposed for permanent magnet synchronous motors. A transformed speed error is introduced and a suitable controller is designed to make it converge to zero, while guaranteeing the original speed error evolves strictly within a prescribed region. The controller is designed based on a backstepping approach. A linear extended state observer is applied to estimate and feed forward the external constant load disturbance to improve robustness. A data-driven radial-basis function neural network is proposed to approximate the nonlinear dynamic caused by parameter uncertainties and periodic-changing disturbance by deploying real-time and historical data. The stability analysis is based on Lyapunov's control theory. Experimental results verify the effectiveness and advantages of the proposed control scheme.