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The permanent magnet synchronous motor (PMSM) is a highly efficient energy saving machine. Due to its simple structural characteristics, good heat radiation capability, and high efficiency, PMSMs are gradually replacing AC induction motors in many industrial applications. The PMSM has a nonlinear system and lies on parameters that differ over time with complex high-class dynamics. To achieve the excessive performance operation of a PMSM, it essentially needs a speed controller for providing accurate speed tracking, slight overshoot, and robust disturbance repulsion. Therefore, this article provides an overview of different robust control techniques for PMSMs and reviews the implementation of a speed controller. In view of the uncertainty factors, such as parameter perturbation and load disturbance, the H∞ robust control strategy is mainly reviewed based on the traditional control techniques, i.e., robust H∞ sliding mode controller (SMC), and H∞ robust current controller based on Hamilton–Jacobi Inequality (HJI) theory. Based on comparative analysis, this review simplifies the development trend of different control technologies used for a PMSM speed regulation system.

This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.

Recently, formation flying of multiple unmanned aerial vehicles (UAVs) found numerous applications in various areas such as surveillance, industrial automation and disaster management. The accuracy and reliability for performing group tasks by multiple UAVs is highly dependent on the applied control strategy. The formation and trajectories of multiple UAVs are governed by two separate controllers, namely formation and trajectory tracking controllers respectively. In presence of environmental effects, disturbances due to wind and parametric uncertainties, the controller design process is a challenging task. This article proposes a robust adaptive formation and trajectory tacking control of multiple quad-rotor UAVs using super twisting sliding mode control method. In the proposed design, Lyapunov function-based adaptive disturbance estimators are used to compensate for the effects of external disturbances and parametric uncertainties. The stability of the proposed controllers is guaranteed using Lyapunov theorems. Two variants of the control schemes, namely fixed gain super twisting SMC (STSMC) and adaptive super twisting SMC (ASTSMC) are tested using numerical simulations performed in MATLAB/Simulink. From the results presented, it is verified that in presence of disturbances, the proposed ASTSMC controller exhibits enhanced robustness as compared to the fixed gain STSMC.

This paper addresses fixed-time trajectory tracking for a dual-arm free-floating space robot (FFSR) with the large initial errors and bounded uncertainty. A wrist-based trajectory planning method is improved by fixed-time stability to fast eliminate the error caused by singularity. Then, a novel low-chattering and global-nonsingular fixed-time terminal sliding mode control strategy is studied by state approaching angle and switching sliding mode; the practical fixed-reachable Lyapunov stability analysis is presented for a mechanical control system. In the end, the proposed trajectory planning method and controller are combined to improve the tracking accuracy of end-effector to the nanoscale. Simulation results validate the effectiveness of the proposed methodologies.

This work proposes a hierarchical nonlinear control scheme for quadrotor to track 3D trajectory subject to payload variation and fast time-varying wind gust disturbance. In terms of dynamics model, the 6 DOF dynamics model with parametric and nonparametric uncertainties is built up. Wind gust and propeller momentum drag model are implemented to quantify the wind impact (force and moment disturbances) on quadrotor. In terms of control design, adaptive robust controller is developed for dynamic subsystem to deal with moment disturbance and estimate the system parameters. Global sliding mode controller is implemented for kinematic subsystem to generate adequate desired attitude angles for tracking the planned 3D trajectory. Simulations and experiments under various conditions are carried out for verification, and the results indicate the effectiveness, adaptiveness and robustness of the control strategy.

Fabricated by high elastic materials, soft actuators provide a prominent solution for soft rehabilitation gloves, soft graspers and locomotion robots. However, the control of soft actuators is a grant challenge due to dynamic modeling error and unavailable system states. This paper proposes an observer-based continuous adaptive sliding mode controller for soft actuators in the presence of system uncertainties without knowledge of its upper bound in prior. By exploiting a novel nonsingular fast terminal sliding mode (NFTSM) surface and a high-order sliding mode (HOSM) observer, the proposed control scheme features adaptive-tuning gains, continuity, singularity-free, stronger robustness and higher tracking accuracy. The stability of the proposed controller is analyzed by the Lyapunov method. Corresponding comparative simulations and experiments of a soft pneumatic network actuator verify the effectiveness and related features of the proposed controller.

This study concerns with robust tracking control problem for the longitudinal model of airbreathing hypersonic vehicles (AHVs). The AHVs include serious non-linearities, strong couplings, parametric perturbations and mismatched disturbances, which results in great difficultly in the controller design. By using back-stepping method and non-linear disturbance observer technique, a novel composite controller is proposed, which can guarantee system outputs asymptotically track their reference signals. A new idea is that disturbance estimations are introduced into virtual control law in each step to compensate the mismatched disturbances. As compared with other robust flight control methods for AHVs, the developed method exhibits not only excellent robustness and disturbance rejection performance but also the property of nominal performance recovery. Finally, the effectiveness of the proposed method is demonstrated by simulation results.

Articulating crane (AC), a widely used crane, plays an essential role in various industrial activities. Owing to its strong nonlinearity and uncertainty, its tracking control remains challenging, particularly for precise dynamic tracking control. This paper proposes an adaptive diffeomorphism-constraint-based control (ADCBC) for a nonlinear AC to robustly achieve trajectory tracking while guaranteeing desired dynamic control performance (DDCP), considering (possibly rapid and irregular) time-variant uncertainty with unknown bounds. A user-definable hard-limiting function was used to guarantee the DDCP, including the requirement for steady-state tracking error and dynamic convergence speed. The desired trajectories and DDCP were formulated as equality and inequality servo constraints, respectively. A diffeomorphism approach was adopted to incorporate inequality servo constraints into equality servo constraints, yielding new equality servo constraints. Thus, the control task was converted to enable the transformed AC to follow the new equality servo constraints and was completed by a constraint-based control (CBC) scheme, where an adaptive law was established for the estimation of online uncertainty bounds to compensate for uncertainty. No approximations or linearizations were invoked. The effectiveness and robustness of the proposed ADCBC were confirmed through rigorous proofs and simulation results. To the best of our knowledge, this is the first endeavor in tracking control while guaranteeing the DDCP for uncertain AC-like systems.

A novel trajectory tracking control problem based on constraint modeling and analysis is addressed by the way of constraint-following control for the unmanned tracked vehicle in this paper. The unmanned tracked vehicle system contains time-varying uncertainty which is possibly swift but bounded, and the bound is possibly unknown. First, the coupled dynamics model of unmanned tracked vehicle is established. By taking into account the kinematic characteristics, it makes the motion control of unmanned tracked vehicles more precise. Meanwhile, a 3D virtual prototype model is established for the unmanned tracked vehicle. Second, for the control objective of trajectory tracking, the related problem is converted into a constraint-following problem, and an adaptive robust controller is therefore proposed based on this for the controlled unmanned tracked vehicle system to satisfy the trajectory tracking constraint. Finally, it is proved that the controlled unmanned tracked vehicle system can achieve accurate trajectory tracking with the proposed adaptive robust control, even under the interference of complex time-varying uncertainties. Modeling accurate dynamics and trajectory tracking constraints for unmanned tracked vehicles while designing an adaptive robust controller to realize accurate motion control for unmanned tracked vehicles even under strong external disturbances are the main contributions of this paper.