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Fish farms are prone to disease outbreaks and stress due to high-density rearing conditions in tanks and sea cages, adversely affecting growth, reproduction, and metabolism. To understand the molecular mechanisms affected in the gonads of breeder fish after an immune challenge, we investigated the metabolome and the transcriptome profiles in zebrafish testes after inducing an immune response. After 48 h of the immune challenge, ultra-high-performance liquid chromatography (LC-MS) and transcriptomic analysis by RNA-seq (Illumina) resulted in 20 different released metabolites and 80 differentially expressed genes. Among these, glutamine and succinic acid were the most abundant metabolites released and 27,5% of the genes belong to either the immune or reproduction systems. Pathway analysis based on metabolomic and transcriptomic crosstalk identified cad and iars genes that act simultaneously with succinate metabolite. This study deciphers interactions between reproduction and immune systems and provides a basis to improve protocols in generating more resistant broodstock

This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications.

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault-and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Proportional integral derivative (PID) regulators are the most practical control schemes for industrial wind turbines. The key to PID design is the determination of the control parameter gains, which motivated our attempts to construct an adaptive PID control for wind turbines allowing auto-tuning of the gains without the need for trial and error processes. By equipping a novel PID-based fault-tolerant controller with a Nussbaum-type function, a robust adaptive and fault-tolerant control scheme is developed for wind turbines. Compared with available methods, the proposed controller has advantages such as, ability for dealing with complete nonlinear dynamics of wind turbines including model uncertainty, ability to ensure system stability by using an adaptive self-tuning gain algorithm, and robustness against wind speed variation. Furthermore, it has the ability to accommodate unexpected actuator faults and the accommodation of an unknown control direction. However, the salient feature of the proposed controller lies in its simple structure and inexpensive online computational demands while dealing with the nonlinear dynamics of wind turbines and unknown disturbances. It is shown that the proposed pitch angle controller remains continuous and smooth and all the closed-loop system signals are guaranteed to be uniformly ultimately bounded. Theoretical analysis and numerical simulations are presented to confirm the effectiveness of the proposed control strategy.

© 2017 IEEE. This paper presents a neural adaptive fault tolerant control design of wind turbines in partial load operation. The controller is designed to be robust against actuator faults as well as noise, while keeping the wind turbine generating as much power as possible. The wind speed variation is considered as an external disturbance, and an adaptive radial basis function neural network is utilized to estimate aerodynamic torque. Estimation of a fault size and establishment of a desired trajectory are adopted in the design. Using the proposed method, the reliability of wind power generation is increased so as to track the optimum power point under faulty conditions, close to the fault free case. Uniformly ultimately boundedness of the closed-loop system is achieved using Lyapunov synthesis. The designed controller is verified via numerical simulations, showing comparison with an industrial reference controller, using predefined criteria.

© 2017 IEEE. The safe operation of wind turbines is a vital criterion which should be considered in the controller design to avoid rotor over speeding and hazardous operation. In this regard, the main focus of this paper is to keep the wind turbine operating within given limits by considering constrained control concepts using the barrier Lyapunov function. The nonlinear model of a benchmark wind turbine is considered and the wind speed variation is assumed to be unmeasurable disturbance and consequently, utilising the radial basis function neural network, an aerodynamic torque estimator is designed. The proposed controller is evaluated via numerical simulations and its performance is compared to a practical industrial wind turbine controller and a quadratic Lyapunov-based controller.

Tujuan penelitian ini adalah mengembangkan media pembelajaran berbasis moodle untuk perkuliahan Fisika Dasar. Penelitian dilakukan dengan model pengembangan 4D selama dua tahun. Tahun pertama, penelitian terfokus pada pengembangan platform moodle melalui fase define, design, dan develop. Tahun kedua dilakukan penyempurnaan dengan revalidasi produk dan diseminasi. Pengembangan desain platform moodle dilakukan secara offline dan online menggunakan software moodle versi 2.0. Pengembangan secara offline dilakukan untuk mengatur story board, page navigation, profile setting, administration settings, dan management course setting. Pengembangan secara online dilakukan agar pembelajaran fisika dasar dapat diakses di manapun sehingga mahasiswa dapat belajar lebih mudah dan fleksibel. Hasil pengembanga divalidasikan kepada pakar pembelajaran dan diujicobakan kepada mahasiswa. Pengumpulan data respon dilakukan lewat angket dan tes dan hasilnya dianalisis dengn statistik deskriptif. Hasil validasi pakar untuk silabus, SAP, dan kelayakan ketiganya masuk kategori sangat baik/layak sehingga dapat dinyatakan layak untuk digunakan untuk mendukung proses pembelajaran. mahasiswa juga memberikan respon yang positif terhadap media yang dikembangkan.

The operational wind turbine efficiency in the power maximization regions and reliability improvement to reduce the produced power cost can both be enhanced by using an appropriate controller to cope with the highly nonlinear behavior of wind turbines in the presence of wind speed variation and actuator faults. In this regard, a nonlinear controller is proposed to make the wind turbine operate effectively despite some of the actuator faults, similar to the fault-free case. The considered actuator faults are pitch and generator actuator biases, as well as pitch actuator dynamic change, including pump wear, hydraulic leak, and high air content in the oil. Also, the wind speed is assumed to be an unmeasurable disturbance, and, accordingly, when using the neural network scheme, the unknown desired trajectory is reconstructed, so that the captured power is maximized. The proposed controller is shown to be able to keep the wind turbine tracking the reconstructed desired trajectory with sufficient accuracy. By using the Lyapunov analysis, the boundedness of the closed-loop system with the proposed controller is proven. The designed controller is verified via numerical simulations. The effectiveness of the proposed controller is evaluated in comparison with industrial constant gain controller results.

Power generation in modern and industrial wind turbines can be improved by careful choice and analysis of operational control strategies. In this paper a new controller scheme is proposed in partial load operation of the wind turbine where the pitch angle is kept constant at zero and the generator torque is adjusted utilizing the fuzzy inference method. Fuzzy rules were defined with respect to the response of the wind turbine to reference gains such that the output power tracks the ideal power curve as close as possible without any significant increase of stress on the main shaft and drive train. A variety of membership shape functions were considered to show the resulting effect on the extracted energy and the drive train stress. Accordingly, through numerous simulations, it can be seen that the total harvested energy is increased. The fuzzy controller was evaluated based on a nonlinear model of the wind turbine using real wind speed applied to the model as a disturbance, to consider the practicality of the proposed controller.

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

© 2017 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.In this paper, the fault detection of a latent fault in a sensor for a Markov jump system is studied. It is equivalent to detecting a change point in a coefficient vector of a measurement equation in the state space representation of a system. Indeed, the fault detection procedure is evaluated as detecting this change point and the time that the change point has occurred. To this end, first, the recursive least square (RLS) filter is proposed and under Yao's Prior setting, the Bayesian fault detection algorithm is proposed. The Smith-Gelfand re-sampling method is applied to approximate the posterior distribution. The performance of the Bayesian method is studied under the null and alternative hypotheses. The delay in diagnosis of the fault is measured. To study the effect of the fault time point in the performance of the Bayesian method, the sensitivity analysis is studied. The probability of the fault is studied and the Martingale approach is used to obtain the lower and upper bounds for this probability. The fault detection in integrated systems is studied and a Kalman filter, as a parallel filter, is considered to estimate the state and the effect of the unknown coefficient jump on state estimation is also studied.