Učinkovito upravljanje brzinom induktivnog motora korištenjem metode adaptivnog upravljanja s referentnim modelom zasnovane na RBF-u

This paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that artificial intelligence based control methods were much more successful in the nonlinear system control applications. In this work, it has been developed an intelligent controller for induction motor speed control with combination of radial basis function type neural network (RBF) and model reference adaptive control (MRAC) strategy. RBF is utilized to adaptively compensate the unknown nonlinearity in the control system. The indirect field-oriented control (IFOC) technique and space vector pulse width modulation (SVPWM) methods which are widespread used in high performance induction motor drives has been preferred for drive method. In order to demonstrate the reliability of the control technique, the proposed adaptive controller has been tested under different operating conditions and compared performance of conventional PI controller. The results show that the proposed controller has got a clear superiority to the conventional linear controllers.Ovaj rad prikazuje adaptivni regulator s referentnim modelom zasnovan na neuronskoj mreži za induktivne motore. Ponašanje tradicionalnih regulatora s povratnom vezom pokazalo se nedovoljno dobrom za upravljanje brzinom induktivnih motora zbog nelineatnosti strukture sustava, promjene okolišnih uvjeta, i efekta ulaznih poremećaja. Uspješno upravljanje brzinom induktivnog motora zahtjeva nealinearne upravljačke sustave. S druge strane, posljednjih godina pokazano je kako su upravljačke metode zasnovane na umjetnoj inteligenciji bitno uspješnije u primjenama upravljanja nelinearnim sustavima. U ovome radu razvijen je inteligentni regulator za upravljanje brzinom induktivnog motora s kombinacijom radijalne neuronske mreže (RBF) i strategije adaptivnog regulatora s referentnim modelom (MRAC). RBF je realiziran kako bi adaptivno kompenzirao nepoznatu nelinearnost u sustavu upravljanja. Tehnika indirektnog vektorskog upravljanja (IFOC) i metoda prostorno vektorske širinsko impulsne modulacije koje su široko korištene za induktivne motore visokih performansi preferirani su kao metode u ovome radu. Kako bi se prikazala pouzdanost tehnike upravljanja, predloženi adaptivni regulator ispitan je u različitih uvjetima rada i uspoređeno je vladanje s obzirom na konvencionalni PI regulator. Rezultati pokazuju kako predloženi regulator očito pokazuje bolje vladanje od konvencionalnih linearnih regulatora

Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting

Flood, which is the most common natural disaster that occurs worldwide, causes massive casualties and damages to people and environment respectively. Hence, flood prediction is integral to minimise the damage and loss of life, while simultaneously aiding the government authorities and even the private sector in making accurate decisions when faced with incoming flood. Therefore, this present study had imputed the missing hydrological data using five imputation methods, namely Neural Network (NN), Moving Median (MM), Iterative Algorithm (IA), Nonlinear Iterative Partial Least Square (NIPALS), and Combined Correlation with Inversed Distance (CCID) imputation methods. Next, a newly developed hybrid deep learning (DL) algorithm is proposed to predict the daily water level in selected rivers that flow through Kelantan. The proposed model was then compared with two benchmark models, namely single Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN). The outcomes revealed that the MM imputation method resulted in higher accuracy with the lowest Root Mean Square Error (RMSE) for all rainfall and streamflow stations, in comparison to the other imputation methods. The experimental results portrayed that the proposed model achieved the best prediction accuracy in all performance measurements. The Mean Arctangent Absolute Percentage Error (MAAPE) results for all rivers ranged at 1-12%, which signified higher accuracy. Essentially, the proposed model may facilitate the government authorities and private sector to predict and plan better when dealing with the occurrence of flood