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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.

In this article, novel results on the maximality of discrete fractional Green's functions are established and corresponding explicit Lyapunov inequalities for delta fractional systems, with applications to chaos analysis and robust control design, are derived. For the proposed Riemann-Liouville fractional difference system with the delta boundary conditions, explicit expressions for the maximum values of the associated Green's function over its domain are obtained. These results lead to a refined Lyapunov delta-type inequality establishing a necessary condition for the existence of nontrivial solutions, where the lower bound explicitly depends on the maximum values of the fractional order and the Green's function. Furthermore, it is demonstrated that violation of this inequality implies the existence of nontrivial solutions and can induce chaotic behavior in fractional difference systems. For control applications, robust stability conditions for uncertain fractional systems are established and stabilizing state feedback controllers is designed. Finally, the numerical examples validate the emergence of chaos under inequality violation and confirm the control design's efficacy for robust stability.

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.

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.

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.

The surgical dual-arm collaborative robots require extremely high precision. However, time-varying uncertainty and inequality constraints pose challenges to the control design of such dual-arm collaborative robot systems. To address these difficulties, this paper proposed an adaptive robust control strategy based on a diffeomorphism technique. Firstly, in the case of unknown uncertainty bounds, the uncertainty within the system are described using fuzzy set theory. Secondly, for the inequality constraints, a diffeomorphism technique is employed to reconstruct the dynamic equations and servo constraint equations, thereby integrating the equality constraints and the inequality constraints into the system’s constraint-following problem. Subsequently, an adaptive robust control strategy incorporating a leakage adaptive law is proposed. Furthermore, in order to balance the relationship between system performance and control cost, a fuzzy performance index for parameter optimization is proposed. Finally, the effectiveness of the proposed method is verified through numerical simulation.

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.