Research on First Order Delays System Automation

Mohammad Reza Avazpour,Farzin Piltan,Mohammad Hadi Mazloom,Amirzubir Sahamijoo,Hootan Ghiasi,Nasri B. Sulaiman 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.4

Many of industrial plant require high performance and linear operation; higher density position and/or incremental PID can be used to integrate large amounts of control methodology in a single methodology. This work, proposes a developed method to design PID controller (PID) with optimal-tunable gains method using PC-based method. Many industrial processes can be represented by a first order model. The time delay occurs when a sensor or an actuator are used with a physical separation. The method used to design a PID is to design it as Proportional – derivative controller (PDC) and proportional – integral controller (PIC) connected in parallel through a summer. PIC is designed by accumulating the output of PDC. This method contributes to avoid writing a huge number of fuzzy rules and to reduce the memory considerations in digital design.

Robust Auto-Intelligent Sliding Accuracy for High Sensitive Surgical Joints

Mohammad Hadi Mazloom,Farzin Piltan,Amirzubir Sahamijoo,Mohammad Reza Avazpour,Hootan Ghiasi,Nasri B. Sulaiman 보안공학연구지원센터 2016 International Journal of Bio-Science and Bio-Techn Vol.8 No.1

The objective of this paper is to design and coordinate controllers that will enhance transient stability of three dimensions motor subject to large disturbances. Two specific classes of controllers have been investigated, the first one is a type of disturbance signals added to the excitation systems of the generating units. To address a wide range of operating conditions, a nonlinear control design technique, called highly nonlinear computed torque control, is used. While these two types of controllers improve the dynamic performance significantly, a coordination of these controllers is even more promising. Results show that the proposed control technique provides better stability than conventional computed torque fixed gain controllers.

Intelligent Trajectory Tracking Control of Robot-Assisted Surgery

Mohammad Reza Avazpour,Farzin Piltan,Hooton Ghiasi,Mohammad Hadi Mazloom,Amirzubir Sahamijoo 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.5

Robotic surgery, computer-assisted surgery, and robotically-assisted surgery are terms for technological developments that use robotic systems to aid in surgical procedures. Robotically-assisted surgery was developed to overcome the limitations of minimally-invasive surgery and to enhance the capabilities of surgeons performing open surgery. In the case of robotically-assisted minimally-invasive surgery, instead of directly moving the instruments, the surgeon uses one of two methods to control the instruments; either a direct telemanipulator or through computer control. A telemanipulator is a remote manipulator that allows the surgeon to perform the normal movements associated with the surgery whilst the robotic arms carry out those movements using end-effectors and manipulators to perform the actual surgery on the patient. In computer-controlled systems the surgeon uses a computer to control the robotic arms and its end-effectors, though these systems can also still use telemanipulators for their input. One advantage of using the computerised method is that the surgeon does not have to be present, but can be anywhere in the world, leading to the possibility for remote surgery. The multi degrees of freedom actuator is an important joint, which has attracted worldwide developing interests for its medical, industry and aerospace applications. This paper addresses the problem of trajectory tracking of three dimensions joint in the presence of model uncertainties and external disturbances. An adaptive fuzzy sliding mode controller (AFLSMC) is proposed to steer a three dimension joint along a desired trajectory precisely. First, the dynamics model of a three dimension joint is formulated and the trajectory tracking problem is described. Second, a sliding mode controller (SMC) is designed to track a time-varying trajectory. The fuzzy logic system (FLS) is employed to approximate the uncertain model of the three dimension joint, with the tracking error and its derivatives and the commanded trajectory and its derivatives as FLS inputs and the approximation of the uncertain model as FLS output. And a fuzzy logic system is also adopted to attenuate the chattering results from the SMC. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller is proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the AFLSMC attenuates the chattering effectively and has better performance against the SMC.

Design Sensor-less PID Filter Controller for First Order Delays System

Hootan Ghiasi,Farzin Piltan,Mohammad Reza Avazpour,Mohammad Hadi Mazloom,Amirzubir Sahamijoo,Nasri B. Sulaiman 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.4

The dynamics of a first order delay system is highly nonlinear, time variant, uncertain and coupling effects. The main objectives to control of first order delay system are time response and acceleration measurements. The problem of acceleration measurements can be reduced, based on design sensor-less Proportional-Integral-Derivative (PID) filter controller in this research. Assuming unstructured uncertainties and structure uncertainties can be defined into one term and considered as an uncertainty and external disturbance, the problem of computation burden and large number of parameters can be solved to some extent. To solve the uncertainties acceleration measurements play an important role. In order to design sensor-less PID filter controller, an accurate PD surface and the derivative of PD surface plays important role. To design an accurate PD surface, stable and tuning surface slope is needed to form the structure of main PID controller. In this algorithm, the derivative of PD surface computes the second derivation of error. Regarding to this method, the challenge of system uncertainties and time response have been solved based on sensor-less acceleration linear filter controller. As this point if s = K1e + e + K2Σe is chosen as desired surface, if the dynamic of first order delay is derived to surface then the linearization can be realized. Because, when the system dynamic is on the surface is used the derivative of surface S = K1e + e + K2e is equal to the zero that is a decoupled and linearized closed-loop systems dynamics. Linearization and decoupling by the above method can be obtained in spite of the quality of the first order delay dynamic model.

Trajectory Tracking Control of Multi Degrees of Freedom Joints: Robust Fuzzy Logic-Based Sliding Mode Approach

S.Yauldegar,Hootan Ghiasi,Mohammad Hadi Mazloom,Amirzubir Sahamijoo,Mohammad Reza Avazpour,Farzin Piltan 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.12

The multi degrees of freedom actuator is an important joint, which has attracted worldwide developing interests for its medical, industry and aerospace applications. This paper addresses the problem of trajectory tracking of three dimensions joint in the presence of model uncertainties and external disturbances. An adaptive fuzzy sliding mode controller (AFLSMC) is proposed to steer a three dimension joint along a desired trajectory precisely. First, the dynamics model of a three dimension joint is formulated and the trajectory tracking problem is described. Second, a sliding mode controller (SMC) is designed to track a time-varying trajectory. The fuzzy logic system (FLS) is employed to approximate the uncertain model of the three dimension joint, with the tracking error and its derivatives and the commanded trajectory and its derivatives as FLS inputs and the approximation of the uncertain model as FLS output. And a fuzzy logic system is also adopted to attenuate the chattering results from the SMC. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller is proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the AFLSMC attenuates the chattering effectively and has better performance against the SMC.

Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study

Ghiasi, Ramin,Ghasemi, Mohammad Reza Techno-Press 2018 Smart Structures and Systems, An International Jou Vol.22 No.5

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

Immune response and antioxidant status of broilers as influenced by oxidized vegetable oil and pomegranate peel

( Mohammad Ghasemi-Sadabadi ),( Yahya Ebrahimnezhad ),( Naser Maheri-Sis ),( Jamshid Ghiasi Ghalehkandi ),( Abdolahad Shaddel-Teli ) 한국축산학회 2021 한국축산학회지 Vol.63 No.5

The experiment was designed as a 3 × 3 × 2 factorial arrangement of treatments, including (i) pomegranate peel (zero, 4%, and 8 percent), (ii) oxidized soybean oil (zero, 2%, and 4 percent), and (iii) alpha-tocopherol (zero and 200 mg/kg). Supplementation of 8% pomegranate peel in diets significantly decreased the growth performance of broiler chickens. The supplementation of 4% oxidized oil in diets significantly reduced body weight gain and Feed intake whole experimental period (p < 0.05). The results showed that supplementation of 4% pomegranate peel in the diet was associated with low aspartate transaminase (AST), alanine transaminase, and malondialdehyde (MDA). However, 4% pomegranate peel increased the total antioxidant capacity (TAC) and superoxide dismutase (SOD) and glutathione peroxidase (GPx) activities. The supplemental 4% oxidized oil increased the serum AST, alanine aminotransferase (ALT), and MDA concentrations. TAC, SOD, and Catalase (CAT) activities were affected by 4% oxidized oil and alpha-tocopherol. The use of oxidized oil and vitamin E decreased MDA concentration. The serum glucose and globulin concentrations were significantly lower in the 8% pomegranate peel. The results showed that supplementation with 4% pomegranate peel in diets reduced serum low-density lipoprotein (LDL). The inclusion of 4% oxidized oil in diets reduced serum glucose and increased the blood lipid concentration such as triglyceride, cholesterol and LDL. Vitamin E supplementation reduced the serum cholesterol and LDL concentrations. The use of 8% pomegranate peel reduced red blood cell (RBC), hemoglobin, and packed cell value (PCV). The results indicated that supplementation with 8% pomegranate peel and 4% oxidized oil in diets decreased the immunoglobulin concentration in broilers. In addition, it was found that the inclusion of 4% pomegranate peel in diets resulted in higher IgG, IgM and total immunoglobulin. Pomegranate peel supplementation significantly decreased meat MDA concentration. Supplementation of 4% oxidized oil increased MDA of meat (p < 0.05). Vitamin E supplementation (200 mg/kg) significantly decreased MDA of meat (p < 0.05). Consequently, the results of this experiment showed that supplementation with 4% pomegranate peel had beneficial effects on broiler chickens. It was also found that feeding 2% oxidized oil in diets had no adverse effect on broilers.

An intelligent health monitoring method for processing data collected from the sensor network of structure

Ramin Ghiasi,Mohammad Reza Ghasemi 국제구조공학회 2018 Steel and Composite Structures, An International J Vol.29 No.6

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, Nystrőm method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study

Ramin Ghiasi,Mohammad Reza Ghasemi 국제구조공학회 2018 Smart Structures and Systems, An International Jou Vol.22 No.5

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three well-known optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

Optimum feature selection for SHM of benchmark structures using efficient AI mechanism

Ramin Ghiasi,Mohammad Reza Ghasemi,H.T. Chan 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.4

Structural Health Monitoring (SHM) is rapidly developing as a multi-disciplinary technology solution for condition assessment and performance evaluation of civil infrastructures. It consists of three parts: data collection, data processing (feature extraction/selection), and decision-making (feature classification). In this research, for effectively reducing a dimension of SHM data, various methods are proposed such as advanced feature extraction, feature subset selection using optimization algorithm, and effective surrogate model based on artificial intelligence methods. These frameworks enhance the capability of the SHM process to tackle with uncertainties and big data problem. To reach such goals, a framework based on three main blocks are proposed here: feature extraction block using wavelet pocket relative energy (WPRE), feature selection block using improved version of binary harmony search algorithm and finally feature classification block using wavelet weighted least square support vector machine (WWLS-SVM). The capability of the proposed framework is compared with various well known methods for each block. Results will be presented using metrics of precision, recall, accuracy and feature-reduction. Furthermore, to show the robustness of the proposed methods, six well-known benchmark datasets of SHM domain are studied. The results validate the suitability of the proposed methods in providing data reduction and accelerating damage detection process.