Zhihang Jia, Qi Zhou, Tiger Hu Sun

The traditional magnetorheological damper has serious settlement in the process of use, and the amplitude modulation range of damping force and magnetic field utilization also need to be optimized. According to these characteristics, a magnetorheological grease damper with permanent magnet and excitation coil is designed in this paper. Under the action of permanent magnet, the damper ensures that the magnetorheological damper can still provide a certain damping force when there is no current supply. When there is current supply, it can increase the amplitude modulation range of damping force. Through the finite element simulation and mechanical model calculation of the magnetic field of the magnetorheological grease damper, the dynamic performance of the magnetorheological grease damper at different currents is explored.

Vincent A. Akpan, Ioakeim K. Samaras, George D. Hassapis

Industrial control networks play significant roles in industrial distributed networked control systems since it enables all the system components to be interconnected as well as monitor and control the physical equipment in industrial environments [2]. The integration of control and communication in networked control systems (NCSs) has made the design and analysis of NCSs a great theoretical challenge for conventional control theory [11]. A major trend in modern industrial and commercial control systems is to integrate computing, communication and control into different levels of machine/factory operations and information processes [12]. NCSs provide a natural platform for distributed learning control. However, it seems that, apart from the remote-tuning of PID controllers, there is no strong research activity to combine NCS study with adaptive control, learning control, network communications and so on [12]. The transmission time for the data packets introduces network-induced delays to NCSs, which are well known to degrade the performance of the control systems [11]. This paper presents a NCS implemented over on a specific service-oriented-architecture (SOA) computer network based on device profile web services (DPWS) for industrial control applications with emphasis on reduced network-induced delay. The performance of the proposed NCS based on four-level hierarchical structure is compared with other networks by considering its real-time implementation for online neural network-based model identification and a nonlinear model adaptive model predictive control (NAMPC) of a fluidized bed furnace reactor (FBFR). Even though SOA connections offer flexibility and scalability advantages, their large communication overhead makes it difficult to satisfy the real-time requirements of the control algorithm when it is implemented over traditional Ethernet networks. However and contrary to [11], in the proposed NCS over the SOA computer network based on DPWS, every component conforms to a SOA technology while the exchange of messages follows a new format technique which significantly reduces transmission delays and overheads without prohibiting the SOA high level interfacing. Furthermore these components are interconnected with each other by utilizing the switched Ethernet architecture which further reduces the transmission delays. Simulation results for the FBFR pilot plant model identification and control have shown that the proposed computer network allows the satisfaction of the real-time constraints of the considered model-based predictive control of the FBFR pilot plant. The aforementioned results render the proposed computer network suitable for advanced control of industrial processes with time constants similar to those of the FBFR process.

Bukhar Kussainov

In automatic control systems, telecommunications and information systems subjected to impact of random disturbances and measurement inaccuracies, there is the problem of estimating the state vector of observed stochastic system. With the aim to solve the problem the state space system model is described and the problem statement is given. To solve the problem it’s used the discrete Kalman filter (KF) presenting itself the recurrent procedure in the form of the set of the difference vector-matrix equations. In the paper the way of deriving the equations of KF on the basis of the procedure of minimization of the mean-squared error of estimation based on a method of the least squares is considered. Using this procedure the discrete analog of the Wiener-Hopf equation as well as Gaussian and Gaussian-Markov estimates of the state vector of linear stochastic system are received satisfying to a minimum of the mean-squared error in the estimate. On the basis of the received estimates and the discrete equation of Wiener-Hopf the equations of the KF is derived, the theorem of the KF with the minimum mean-squared error is formulated, the sequence of using the equations of KF making up the recursive algorithm of KF for computer program realization is explained.

Md. Turiqul Islam, S. M. Rezaul Karim, Avizit Sutradhar, Shahin Miah

In this article, DC motor supply is controlled using PID controller and fuzzy logic controller. The PID controller requires a mathematical model of the system, while the obscure logic controller is based on experience with rule-based information. The design of a fuzzy logic controller requires a number of design decisions, such as rule-based and physics. FLC has two inputs, one of which is a speed error and the other is a change in speed error. Here are 49 Fuzzy rules designed for the controller of Fuzzy logic. The center of gravity method is used for defasification. The Fuzzy logic controller has used a civilized system that results in Fiji set employees. The PID controller selects its parameters based on trial and error. Method PID and FLC are explore with the help of MATLAB / SIMULINK package program simulation. It has been established that the FLC is more difficult in design to compare with the PID controller, but it has to be more suitable to meet the non-linear characteristics of the DC motor. The results show that fuzzy logic has minimal transient and stable state parameters, indicating that the FLC & PID is more efficient and effective than the controller.

Thisara Ganegoda, H. D. N. S. Priyankara, Nihal S. Senanayake

Soil nitrogen, phosphorus, potassium, and organic matter are the main factors affecting the yields in crop production. It has been a common problem in many developing countries that fertilizers and crop protection chemicals are used with little or no awareness of available soil nutrients. The requirements of these external inputs differ from crop to crop, and with the existing soil conditions. The advice provided by the agricultural extension officers is mainly based on past experiences with no assessments of individual lands. On the other hand, standard laboratory procedures to determine the soil conditions take a long time to prepare the soil samples for testing and obtain the results. When the elements essential for plant nutrition are low in availability or unbalanced, then chemical fertilizers and soil amendments are required to be added to enhance crop yields in right amounts depending crop requirements. Incorrect fertilizer application or unbalanced/inadequate availability of nutrients can lead to depletion of soil nutrient reserves and loss of plant nutrients. Lack of balanced nutrients also encourages excessive uptake of these nutrients supplied in excess, but with no benefit. On the other hand, excessive fertilization is uneconomic, and is a waste of scarce resources. Agriculture extension officers do not have enough details and means to guide the farmers for fertilizer usage to avoid these unfavorable situations in crop production. In this study an instrument was developed for in-situ assessment of soil, P, K and OM which can be used by agriculture field officers. The proposed instrument can analyze the past data in the area and test the soil parameters in the field, and recommend soil P, K and OM requirements. The equipment has the capability to test soil pH and Electrical conductivity in the field using standard Laboratory sensors and to determine Soil, P, K, and OM by processing unique algorithms built from past data. This will help agriculture field officers to recommend suitable fertilizer quantities, hence help reduce the excessive chemical usage and gradual establishment of the chemical balance in the soil.

Amr Mahmoud, Youmna Ismaeil, Mohamed Zohdy

Initially selected system and controller parameters don't often guarantee continued system stability and performance mainly due to the introduction of unexpected system disturbances or unknown system dynamics. In this research we present a novel approach for detecting early failure indicators of non-linear highly chaotic system and accordingly predict the best parameter calibrations to offset such instability using deep machine learning regression model. The approach proposed continuously monitors the system and controller signals. The Re-calibration of the system and controller parameters is triggered according to a set of conditions designed to maintain system stability without compromise to the system speed, intended outcome or required processing power. The deep neural model predicts the parameter values that would best counteract the expected system in-stability. To demonstrate the effectiveness of the proposed approach, it is applied to the non-linear complex combination of Duffing-Van der pol oscillators. The approach is also tested under different scenarios the system and controller parameters are initially chosen incorrectly or the system parameters are changed while running or new system dynamics are introduced while running to measure effectiveness and reaction time.

Sanusi Nuhu, Samaila Ladan, Abubakar Umar Muhammad

This research focused the role to study the effects and ways to control the chemical composition of clinker for better cement production. Cement is a substance produced by grinding a mixture of a clay and limestone and heating to a temperature of 1450°C, in which the chemical transformation occurs inside the kiln to form new compound called clinker. The methodology of this research work are; the 10 samples of clinker were collected on the apron conveyor, 10g of each sample were weighted, milled and pelletized with the aid of pyridine and binding agent. Then the sample were subjected to XRD analyzer machine for determination of mineralogical composition of clinker, the minerals and oxides detected are; C₃S, C₂S, C₃A and C₄AF and CaO, SiO₂, Al₂O₃ and Fe₂O₃. The other cement modulus such as LSF, AM, and SM were calculated using Bogues equation. From results fig.8 shows increases of early strength of cement as a result of increases of AM, fig.10 shows the decrease of early strength of cement as a result of the increase of SM, fig 9 also is the chart of LSF and free lime (FCaO) and the result shows how the free lime increase as the LSF increases, which leads to requires more energy consumption for clinker formation, poor quality clinker, volume expansion and poor strength of cement. Therefore the chemical composition of cement raw materials and clinker are critical to cement plant efficiency and energy consumption. To ensure constant and consistent chemical compositions and quality of cement clinker with lowest possible energy consumption, attention must be paid to kiln feed and clinker chemical compositions.

Abraham C. Lucky, Davies Iyai, Cotterell T. Stanley, Amadi E. Humphrey

In this paper, new results for stability and feedback control of nonlinear systems are proposed. The results are obtained by using the Lyapunov indirect method to approximate the behavior of the uncontrolled nonlinear system’s trajectory near the critical point using Jacobian method and designing state feedback controller for the stabilization of the controlled nonlinear system using the difference in response between the set point and actual output values of the system. Next, the Lyapunov-Razumikhin method is used to determine sufficient conditions for the stabilization of the system. Examples are given with simulation output studies to verify the theoretical analysis and numerical computations using MATLAB.

Rashid M. Ansari, Hasan Imran, Ali S. Hunaidy

This article presents the application of integrating real-time optimization with model-predictive control on a hydrocracking unit on a model case refinery in the Middle East. Real-time optimization (RTO) provides technological excellence that helps to maximize the contribution of the plant to the business profit, provides best-in-class performance, optimizing the plant operation, enhancing safety and reliability. The main objective of RTO implementation on refinery processes was to optimize the operation by applying online rigorous nonlinear closed-loop optimization technology. RTO contributed to optimize key process operating variables by shifting the unit margin toward the optimum, and operation was better placed to challenge targets and operating conditions, driving the plant toward a more profitable operating regime and bringing the higher benefits. The steady-state and kinetic models were developed and used by RTO to improve the yield of high value products by maximizing the economic objective function to enhance the yields of diesel and gasoline. Increasing the feed rate subject to unit constraints and catalyst run length was another objective of RTO implementation. In addition, potential RTO applications have been highlighted in this article for achieving CO₂ emission reduction using two different approaches: improvement of energy efficiency and application of CO₂ capture and conversion technologies. This application will integrate model predictive control (MPC) with RTO with an ultimate aim to maximize an economic objective function to reduce CO₂ emission.

Chiang-Cheng Chiang, Li-Chung Chang

This paper investigates the observer-based robust adaptive fuzzy control problem for a class of uncertain underactuated systems with time delay and dead-zone input. Within this method, the state observer is developed for estimating the unmeasured states in the underactuated system. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and some adaptive laws are introduced to estimate unknown parameters. The dead-zone input which is one of the significant input constraints often exists in many practical industrial control systems. By employing a Lyapunov-Krasovskii functional, it is verified that the proposed controller ensures that all the signals in the closed-loop system are bounded. Simulation results are illustrated to demonstrate the regulation performance of the system output and state estimation by the proposed control method.