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The conventional methods of observer poles placement in sensor fault detection usually adopt the trial-and-error methods. These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. Therefore, this paper proposes a fuzzy-based observer tuning method to optimize and adapt the selection of poles locations to determine the optimal gains of the observer, and it is experimentally applied to a composite sensor fault detection. Fuzzy logic is a promising method that could overcome the trial-and-error method challenges by introducing better adaptation and system robustness. The proposed observer structure includes adaptive tuning corresponding to an unknown input. Utilizing self-tuning for the observer correction stage, the gain is going to be updated online using the proposed fuzzy adaptive poles placement (FAPP) system. This paper validated the system simulation by implementing fault detection algorithms by using a real-time embedded observer-based system. The experimental results demonstrate the effectiveness of the proposed fuzzy-based observer schemes at detecting sensor faults in the Brushless DC (BLDC) motors, with significantly better performance than conventional counterparts' methodologies. The experiments indicate that the average estimation error is 0.146, which less by 43.8% than was obtained for high levels of noise and disturbances compared with the traditional Luenberger observer approach.

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In this paper, an adaptive high-accurate observer-based fault detection approach for industrial applications is proposed. The proposed fault detection algorithm employs a fuzzy logic-based approach with the objective of finding the appropriate observer gains that could cope with the different working conditions. The flexibility and adaptability represent the main objectives of the proposed observer. This work is interested in proposing an observer with fuzzy variable gains for a general nonlinear system. Furthermore, a linear model has been built to facilitate the accomplishment of the fault detection of the industrial servo system by using the proposed observer. In order to evaluate the proposed approach, eleven realistic sensor fault scenarios are created under varying conditions: fault parameters (e.g., multiple fault profiles, location, and magnitudes), unknown inputs (e.g., disturbers and sensor noises) for performance testing. Also, a scoring algorithm has been implemented, to evaluate the classification ability of the algorithm and the early fault detection ability. The experimental results demonstrate the effectiveness of the proposed observer approach in detecting sensor faults in the industrial servo systems, with 88.8% classification accuracy. Furthermore, the obtained results confirm the proposed algorithm superiority when compared to classical Luenberger observer with constant gains.

This paper proposes a newly adaptive single-neuron proportional integral derivative (SNPID) controller that uses fuzzy logic as an adaptive system. The main problem of the classical controller is lacking the required robustness against disturbers, measurement noise in industrial applications. The new formula of the proposed controller helps in fixing this problem based on the fuzzy logic technique. In addition, the genetic algorithm (GA) is used to optimize parameters of the SNPID controller. Because of the high demands on the availability and efficiency of electrical power production, the design of robust load-frequency controller is becoming increasingly important due to its potential in increasing the reliability, maintainability and safety of power systems. So, the proposed controller has been applied for load-frequency control (LFC) of a single-area power system. The effectiveness of the proposed SNPID controller has been compared with the conventional controllers. The simulation results show that the proposed controller approach provides better damping of oscillations with a smaller settling time. This confirms its superiority against its counterparts. In addition, the results show the robustness of the proposed controller against the parametric variation of the system.