This paper designs a high-performance adaptive controller for the uncertain model of hypersonic flight vehicles (HFVs) proceed by faulty and hysteretic actuators. A parameterized HFV model is derived, based on which adaptive tracking controllers for velocity and altitude are designed, sequentially. Compared with other adaptive strategies that mainly focus on the parametric uncertainty and asymptotic tracking, the utilization of prescribed performance control technique can largely improve the transient characteristics of HFVs. A novel handling on angleof- attack (AOA) is proposed with the help of barrier functions. As a result, the magnitude of AOA is limited to match the requirement of the scramjet. Partial loss of effectiveness (PLOE) of actuators is also taken into account, while the backlash hysteresis in aerodynamic control surfaces is accommodated by an adaptive inverse compensation. Corresponding analysis shows that both the limited AOA and the desired performance can be guaranteed despite PLOE of actuators. A simulation study is provided to verify the effectiveness of the proposed controller.

In this paper, a distributed filtering scheme is presented to deal with the soft fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi--Sugeno (T--S) fuzzy models. Randomly occurred packet losses are considered between the WSN and the filter unit. A novel type of IT2 T--S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Meanwhile, a fault reference model is adopted for improving the performance of the fault detection system. Then a new distributed fault detection system is formulated in a type-2 fuzzy model framework. New techniques are utilized to analyse and synthesize of the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established to validate the effectiveness and applicability of the presented distributed fault detection scheme.

In this article, an adaptive antisaturation fixed-time control method with a faster convergence rate is studied for uncertain robotic systems. First, a new segmental sliding variable is constructed to solve the singularity problem brought by the terminal sliding mode control (TSMC) and achieve a faster convergence rate. Second, to approximate and compensate for the model uncertainty and the viscous friction parameter, an adaptive neural network (ANN) is employed. Then, a novel auxiliary system is constructed to mitigate the effects of input saturation. Based on this, a novel nonsingular TSMC algorithm integrated with the ANN and the auxiliary system is designed, so that the trajectory tracking errors of the robotic system can converge within a faster fixed time with actuator saturation. Finally, the superiority and practicability of the present method are verified by comparative experiments.

This article proposes a novel adaptive disturbance observer-based fixed-time backstepping control algorithm for uncertain robotic systems. First, an adaptive sliding mode disturbance observer (ASMDO) is designed to compensate for the lumped disturbances of the robotic system, such as model uncertainty and friction. Second, considering the actuator saturation problem of the robotic system, a new adaptive fixed-time auxiliary system is developed. Then, based on the ASMDO and the adaptive auxiliary system, a novel fixed-time backstepping tracking control algorithm is proposed to achieve faster fixed-time high accuracy tracking control for uncertain robotic systems under actuator saturation. Finally, comparative experiments validate the practicality of the proposed faster fixed-time backstepping control algorithm. Both theoretical analysis and experimental results demonstrate the superiority of the proposed fixed-time backstepping control algorithm.

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.

Both the proportional-integral (PI) controller and the super-twisting algorithm (STA) are attractive alternatives to effectively govern power converters. The PI controller is smooth but is less robust to disturbances, while the STA exhibits good robustness with respect to disturbances but suffers from the negative effect of chattering when some high control gains are required in actual applications. To combine the advantages of both controllers, this paper explores a novel observer-based varying exponent gain STA control scheme for the three-level neutral-point-clamped (NPC) converter. By introducing a time-varying exponent gain into the STA, the proposed novel varying exponent gain STA combines the attractive traits of the PI controller (smooth control input) and the STA (robustness and fast dynamic response) simultaneously. In this sense, the controller exhibits high performance in steady state as well as transient and provides strong robustness with respect to disturbances. In addition to the new controller, a higher-order sliding mode observer is integrated into the control scheme to compensate the disturbances and further enhance the disturbances rejection ability of the system. Finally, the effectiveness of the proposed control method is confirmed by comparing its results with the PI controller and the STA based on a set of experiments.