Purpose: This paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.Design/methodology/approach: The proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.Findings: The performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.Practical implications: For the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.Originality/value: The proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

This paper proposes a novel technique using fuzzy logic controller (FLC) in circulating mode to improve the performance of the International Thermonuclear Experimental Reactor (ITER) poloidal field (PF) converter systems. The accurate mathematical modeling of the complex system is challenging using conventional proportional integral and derivative (PID) controller. The novel approach using FLC overcomes the complexity of the modeling by human expert’s knowledge and experience. The FLC is considered for ITER PF converter system and compared with a conventional PI controller. The dynamic behavior and transient response of the PF converter system in the circulating mode are verified by analysis and simulation. The analysis results determine that the FLC can achieve better operation performance.

The performance of power semiconductor devices near fusion reactor may be changed by the fast neutron irradiation in nuclear fusion. In order to investigate the reliability of thyristors used in Toroidal Field Fast Discharge Unit (TFFDU), two thyristors KPD5000-52 with almost the same parameters were selected to be irradiated by 14.9 MeV neutrons in the range from $\rm 0.403\times 10^{5} n/(cm^{2}\times s) to 1.38\times 10^5 n/(cm^2 \times s)$ for 28 hours. Metal of thyristor was activated to 3 mSv/h after irradiation, and it decreased to 360 nSv/h after 40 days. The electrical parameters of thyristor such as the forward /reverse leakage current and on-state voltage were measured under normal junction temperature (25°C) and high junction temperature (125°C) respectively. The irradiated thyristors cannot be fully conducted until the gate trigger strength is enhanced to 10 A. Moreover, these devices turn off automatically with a small delay after the gate signal is removed. According to the irradiation theory, this paper also presents the decrease of carrier lifetime, carrier concentration and mobility, which lead to thyristor failure.

The elimination of position sensors allows sensorless control to minimizing the size, cost, and additional layers of the permanent magnet synchronous motor (PMSM). In this work, a new flux estimator with an active disturbance rejection controller (ADRC) drift-elimination capability is developed in order to accomplish precise flux estimation. The ESO integrated into the ADRC is capable of detecting and compensating each of the inner model’s fluctuation along with external loads disturbances, enabling a control system with a more effective dynamic response and robust regulations. As a result, the drift issue is greatly mitigated, and the stator flux is accurately predicted throughout a broad range of speeds. The proposed flux observer is validated via sensorless speed vector control of a 15 kW IPMSM motor.

This paper presents a novel technique using a shunt active power filter (SAPF) control strategy to reduce the harmonics generated by the power converter. A three-phase SAPF using the Takagi Sugeno (TS) fuzzy technique is used to reduce current distortion. The proposed TS fuzzy controller is a suitable candidate using SAPF techniques. In order to realize an acceptable distortion limit, TS fuzzy control uses SAPF to realize the performance of the system. The simulation results validate the proposed fuzzy control strategy by a Matlab/Simulink model.