Mehdi Shahbazian

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This paper presents the state estimation problem for nonlinear industrial systems using asynchronous measurements to simulate the circumstances of real case studies. The well-known conventional Kalman filters give the optimal solution but require synchronous measurements, an accurate system model and exact stochastical noise characteristics. Thus, the(More)
Flow rates of oil, gas and water are most important parameters of oil production that is detected by Multiphase Flow Meters (MFM). Conventional MFM collects data on long-term, because of the radioactive source is used for detection and in unmanned location used due to being away from wells. In this work, a new method based on feed-forward artificial neural(More)
Wavelet neural network based on sampling theory has been found to have a good performance in function approximation. In this paper, this type of wavelet neural network is applied to modeling and control of a nonlinear dynamic system and some methods are employed to optimize the structure of wavelet neural network to prevent a large number of nodes. The(More)
Conventional PID controllers are still used in industry due to their simplicity in structure, and ability to eliminate steady state error in operating point. However, it is difficult to achieve a desired tracking control performance since for highly non-linear processes. In order to improve the set point tracking performance under aforementioned issues, a(More)
This paper presents a modified fuzzy control for speed control of induction motor (IM). At first, the PI controller is investigated for speed control of Induction Motor, and then fuzzy logic controller performance is simulated. Induction Motor performance is checked through the simulation studies in MATLAB/SIMULINK environment. Hybridization of fuzzy logic(More)
A multiple fault tolerant measurement system based on nonlinear dynamic models, special searching algorithm, principle components decomposition and Q test is developed. The proposed system uses a model-based estimator to deliver symptoms. The symptoms are then analyzed in a statistical unit in order to detect the faults and isolate the faulty sensors.(More)
In the process monitoring procedure, Data-driven (statistical) methods usually rely on the process measurements. In most industrial process this measurements has a multi-scale substance in time and frequency. Therefore the statistical methods which are proper for one scale may not be able to detect events at several scales. A Multi-Scale Partial Least(More)
In this paper, the particle swarm optimization (PSO) is proposed to train fuzzy wavelet neural network (FWNN) for process system identification. The structure of FWNN is based on the fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the Identification accuracy and general capability of the FWNN system, an(More)
In this work, a fuzzy logic controller has been applied to control the top and bottom compositions in a binary distillation column. To improve the performance of fuzzy logic controller, Cuckoo optimization algorithm is proposed to optimize its parameters. Compared to ordinary fuzzy logic controller and PID controller, the proposed optimized fuzzy logic(More)
This paper discusses a model predictive control approach to hybrid systems with continuous and discrete inputs. The algorithm, which takes into account a model of a hybrid system, described as Hybrid Automaton. However, to avoid computational complexity and computation time, the nonlinear optimization problem is solved by evolutionary algorithms (EA) such(More)