In this paper, the command filter-based adaptive fuzzy finite-time output feedback control (FOFC) is investigated for the Electro-hydraulic servo system. For the uncertainties in the system, we utilize the fuzzy logic systems (FLSs) to estimate these uncertain nonlinearities and fuzzy state observer is established to approximate the unmeasurable hydraulic cylinder stem speed and the internal cylinder force. Then, a command filter-based finite time output feedback control is proposed to achieve high tracking precision, where the tracking errors can be regulated into a small neighborhood around the equilibrium proved by the Lyapunov finite-time stability theory. Moreover, a command filter is introduced to avoid the explosion of complexity in the backstepping procedure, where a compensation mechanism is developed to compensate for filter errors. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control.

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

This article proposes a saturation-tolerant prescribed control (SPC) for a class of multiinput and multioutput (MIMO) nonlinear systems simultaneously considering user-specified performance, unmeasurable system states, and actuator faults. To simplify the control design and decrease the conservatism, tunnel prescribed performance (TPP) is proposed not only with concise form but also smaller overshoot performance. By introducing non-negative modified signals into TPP as saturation-tolerant prescribed performance (SPP), we propose SPC to guarantee tracking errors not to violate SPP constraints despite the existence of saturation and actuator faults. Namely, SPP possesses the ability of enlarging or recovering the performance boundaries flexibly when saturations occur or disappear with the help of these non-negative signals. A novel auxiliary system is then constructed for these signals, which bridges the associations between input saturation errors and performance constraints. Considering nonlinearities and uncertainties in systems, a fuzzy state observer is utilized to approximate the unmeasurable system states under saturations and unknown actuator faults. Dynamic surface control is employed to avoid tedious computations incurred by the backstepping procedures. Furthermore, the closed-loop state errors are guaranteed to a small neighborhood around the equilibrium in finite time and evolved within SPP constraints although input saturations and actuator faults occur. Finally, comparative simulations are presented to demonstrate the feasibility and effectiveness of the proposed control scheme.

We in this paper investigate the Saturation-tolerant Prescribed Control (SPC) for multi-input and multi-output (MIMO) nonlinear systems with unknown control directions and actuator faults. We propose concise Tunnel Prescribed Performance (TPP) with the control design independent of initial conditions and smaller overshoots achieved due to its tight feasible region. A novel auxiliary system, to tactfully establish a feedback mechanism between input saturation and prescribed performance, is constructed. By introducing the generated nonnegative modifications into TPP, the resulted Saturation-tolerant Prescribed Performance (SPP) is capable of flexibly degrading performance constraints in the case of saturation; and recovering back to the user-specified performance in the case without saturation. Furthermore, the proposed control scheme guarantees not only finite-time convergence, but also SPP-constrained tracking performance despite the input saturation and uncertainties. Finally, comparative results are provided to demonstrate the distinctive merit of the proposed SPC more than the traditional Prescribed Performance Control (PPC).

In this article, saturation-tolerant prescribed control (SPC) is investigated for a class of multiinput–multioutput (MIMO) nonlinear systems. The key challenge lies in how to guarantee both input and performance constraints simultaneously for nonlinear systems especially under external disturbance and unknown control directions. We propose concise finite-time tunnel prescribed performance (FTPP) for better tracking performance, which features tight allowable set and user-specified settling time. To comprehensively tackle the conflict between the above two constraints, an auxiliary system is designed to explore their interconnections instead of neglecting their contradictions. By introducing its generated signals into FTPP, the obtained saturation-tolerant prescribed performance (SPP) has the ability to degrade or recover the performance boundaries in the light of different saturation conditions. Consequently, the developed SPC, together with nonlinear disturbance observer (NDO), can effectively improve the robustness and reduce the conservatism against external disturbances, input, and performance constraints. Finally, comparative simulations are presented to showcase these theoretical findings.

This paper addresses the nite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. And the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Further, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in nite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.