This paper proposes an adaptive fault tolerant attitude controller, based on variable structure control, to address tracking control problem of spacecraft in the presence of unknown actuator failure, control input saturation and external disturbances. In contrast to traditional robust fault tolerant attitude controllers, the proposed controller is capable of controlling the transient response of the closed loop system by means of a dedicated parameter. Also this method does not require exact knowledge of the actuator faults and is implemented with uncertain value of fault information. By adjusting a simple parameter dynamically and using Lyapunov direct method and the properties of quaternion representation, the ultimate boundedness of attitude and angular velocity errors in presence of external disturbances is proven. Numerical simulations used to prove the success of proposed controller in achieving high attitude performance in the presence of external disturbances, inexact knowledge of fault information, actuator failures and control input saturation.

Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R² values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices.

Finding a reliable approach to detect bearing faults is crucial, as the most common rotating machine defects occur in its bearings. A convolutional neural network can automatically extract the local features of the mechanical vibration signal and classify the patterns. Nevertheless, these types of networks suffer from the extraction of the global feature of the input signal as they utilize only one scale on their input. This paper presents a method to overcome the above weakness by employing a combination of three parallel convolutional neural networks with different filter lengths. In addition, a bidirectional gated recurrent unit is utilized to extract global features. The CWRU-bearing dataset is used to prove the performance of the proposed method. The results show the high accuracy of the proposed method even in the presence of noise.

This study presents an adaptive sliding mode type‐2 neuro‐fuzzy controller for power control of doubly fed induction generators (DFIGs). DFIG‐based wind turbine system is variable‐speed constant‐frequency wind energy conversion system. In this proposed control scheme, in order to enhance its performance, sliding mode control (SMC) theory is used for online training the parameters of type‐2 fuzzy system membership functions. To regulate the antecedent and consequent part parameters, the SMC adaptive technique is used according to the controller inputs. These inputs are active and reactive power errors and their time derivative, which applied to the structure of T2NF system. The proposed controller employs an interval type‐2 fuzzy system because of the uncertainties of the wind speed and variation in parameters in the wind power conversion system. The simulations carried out for a DFIG‐based 1.5 MW wind turbine. The results of simulation are compared with the classical proportional–integral controller to confirm the effectiveness of this control scheme. This comparison is carried out between the cut‐in and rated region wind speeds. The results of simulation show that the proposed control scheme has better performance to track the peak power and is more robust to machine parameter variations.