In this article, an adaptive fuzzy finite-time fault-tolerant control (FTC) scheme for uncertain non-linear systems under sensor faults is proposed. Compared with the existing methods, the considered system contains unknown time-varying fault parameters, uncertain non-linear functions, and can guarantee the performance of the system in finite time. The coupling between fault parameters and actual states is solved by the fault parameters separation method. The fuzzy logic system (FLS) is used to approximate the unknown functions, and combining the backstepping technology an adaptive fault-tolerant controller is designed. The finite-time stability of the closed-loop system is proved by the Lyapunov theory. At last, the numerical simulation and the real physical system simulation verified the effectiveness of the proposed scheme.

This paper studies the fault-tolerant control problem of uncertain doubly-fed wind turbine generation systems with sensor faults. Considering the uncertainty of the system, a fault-tolerant control strategy based on a T-S fuzzy observer is proposed. The fuzzy observer is established based on the T-S fuzzy model of the uncertain nonlinear system. According to the comparison and analysis of residual between the state estimation of the fuzzy observer output and the measured value of the real sensor, a fault detection and isolation (FDI) based on T-S fuzzy observer is designed. Then by using a Parallel Distributed Compensation (PDC) method we design the robust fuzzy controller. Finally, the necessary and sufficient conditions for the stability of the closed-loop system are proved by quoting Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control method.

Precision irrigation in fuzzy control system based on crop water stress acoustic monitoring was designed in the paper.Moreover,conventional double fuzzy control model was found to carry out two work mode,normal irrigation and precision irrigation,which aims to both ensure normal growth of crops and achieve effective water-saving.Since five input signals affect output in different degree,three layer BP neural network was employed to compute weight.Based on conventional fuzzy model,self learning fuzzy model for crop growth was built to tackle with normal irrigation and precision irrigation.In order to testify the control strategy,an experiment platform including virtual instruments was founded.It shows that the system can effectively adjust to water and control valve speed under signals in AE sensor and environmental information for the corp growth to some extent,and not only save water in the normal irrigation,but also realize safety and efficiency in precision irrigation

The quality of aquaculture waters is directly related to water management and aquaculture efficiency, which puts forward higher requirements for water quality evaluation. Based on the consideration of the influence of temporal and spatial changes on the water quality, this paper proposed an improved fuzzy comprehensive evaluation method for aquaculture water quality evaluation. Specifically, constructing a new membership function in the first place, and then selecting dissolved oxygen, pH, temperature and ammonia nitrogen content as water quality indexes for aquaculture, after that, collecting 60 sets of water quality index for different seasons in the past three years, finally, evaluating the water quality of Yangjiabo Aquaculture Base. Meanwhile, comparing it with the evaluation results of the single factor evaluation method and the traditional fuzzy evaluation method. The results show that the water quality of the Yangjiabo Aquaculture Base is at the worst level in winter, and the water quality has improved significantly in spring, summer and autumn. Compared with the other two method, the improved method can comprehensively reflect the changes in water quality with time and space, which is more practical, and so it can be considered to provide a scientific basis for efficient aquaculture and water quality classification.

This article addresses an adaptive fuzzy practical fixed-time tracking control for nonlinear systems with unknown actuator constraints and uncertainty functions. First, fuzzy logic systems (FLSs) are used to identify uncertain functions.Then, by utilizing FLSs, backstepping technique, and finite-time stability theory, an adaptive fuzzy practical fixed-time control is proposed to obtain satisfactory tracking performance even when the actuator faults. The theoretical analysis verified that the closed-loop systems is practical fixed-time stable under the proposed control strategy, the tracking error converges to a small neighborhood of the origin in a fixed time, and the convergence time is independent of the state conditions. Finally, both numerical simulation and physical example demonstrates the effectiveness of the proposed control strategy.