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The steel industry involves energy-intensive processes such as combustion processes whose accurate modelling via first principles is both challenging and unlikely to lead to accurate models let alone cast time-varying dynamics and describe the inevitable wear and tear. In this paper we address the main objective which is the reduction of energy consumption and emissions along with the enhancement...
Ozone is an important disinfectant in drinking water treatment. The ideal ozone dosage should be a good trade-off between the requirement of disinfection and the restriction of bromate formation. Additionally, the ozone dosing process should cope with the changes of raw water quality and maintain the treated water stable. However, ozone dosing process is very difficult to be controlled because of...
This paper present modeling, simulation and control of hybrid system who integrating logic, dynamics and constraints. System. The Hybrid Automaton is used to modelling a transition system with continuous dynamics. The framework it consist of a finite set of location and transition for the specification of discrete dynamics or states called control mode. Each control mode is indexed set of continuous...
Internet of Vehicles, an emerging industry, is being gradually popularized and applied. This paper is to propose a new construction method to neural network ensembles that based on the key technology to multi-sensor data fusion of Internet of Vehicles system, which is an effective solution to the problems such as the information collection about vehicle traffic. Training a group of neural networks...
In this work, implementation issues related to multivariable Generalized Predictive Control (GPC) for processes with multiple time delays are analyzed. Due to specific properties of those processes, the resulting control system has to account for the existing delays between control variables changes and their effect on controlled outputs. In the case of Model Predictive Control (MPC) techniques, such...
This work considers the problem of economic model predictive control (EMPC) of electric arc furnaces (EAF), subject to the limited availability of process measurements and noise. The key issues addressed are: (1) the multi-rate sampling of process variables; and (2) the requirement of optimized operation that achieves desired product specifications and also minimizes the operating costs. To this end,...
Availability of an accurate and robust dynamic model is essential for implementing the model dependent process control. When first principles based modeling becomes difficult, tedious and/or costly, a dynamic model in the black-box form is obtained (process identification) by using the measured input-output process data. Such a dynamic model frequently contains a number of time delayed inputs and...
Many of the industrial processes are difficult to model because of their complex behavior, influent characteristics and operational conditions. Control of robot manipulator for industrial applications is considered as one of the challenging tasks. In this paper, Model Predictive Controller (MPC), a class of advanced control technique is proposed in order to control the motion of the revolute joints...
The implementation of the Extended Prediction Self-Adaptive Controller is presented in this paper. It employs LabVIEWTM graphical programming of industrial equipment and it is suitable for controlling fast processes. Three different systems are used for implementing the control algorithm. The research regarding the controller design using graphical programming demonstrates that a single advanced control...
The primary focus of this paper is the identification of second-order Volterra models using input sequences that offer the following three advantages: (i) the are "plant friendly;" (ii) they simplify the required computations; and (iii) they can emphasize certain model parameters over others. To provide a quantitative basis for discussing the first of these advantages, this paper defines...
This paper describes the application of Model Based Predictive Control (MBPC) to the temperature regulation of the superconducting magnets for the future Large Hadron Collider (LHC), the next particle accelerator being built at the European Center for Particle Physics (CERN). This controller has the capability of having different sampling times for the prediction and the controlled variable. Through...
This paper presents the application of nonlinear predictive control to the distributed collector field of a solar desalination plant. The main purpose of the controller is to manipulate the water flow rate to maintain an outlet-inlet temperature gradient in the collectors in spite of disturbances and water flow rate constraints. The controller uses a deadtime compensation structure to account for...
The recently developed methods of multi-parametric model predictive control (mp-MPC) for hybrid systems provide an interesting opportunity for solving a class of nonlinear control problems. With this approach, the nonlinear process is approximated by a piecewise affine (PWA) hybrid model, containing a set of local linear dynamics. Compared to linear model based MPC, a performance improvement is expected...
This article presents an application of a control strategy developed by the authors to a nonlinear model. The control strategy is based on a sliding mode controller that uses a generalized predictive controller for the reaching mode part. The proposed Predictive Sliding Mode Controller is developed for a First-Order-Plus-Deadtime model that represents a good approximation to many processes. The Predictive...
A trajectory tracking strategy is presented for processes described by a parabolic nonlinear distributed parameter model. A model-based predictive control approach, combined with an internal model control structure and a state estimation method is extended to this kind of process. On-line requirements such as computational time and constraint satisfaction are outlined and discussed. This method is...
The control of coke fractionation tower has been an issue in both the Chemical engineering and process control field. It requires significant effort in practice to control the parameters in the coke fractionation tower. The performance of the conventional control design and tuning is found to be poor because of the non-linearity in the system. Real-time implementation showed that classical proportional-integral-derivative...
This paper focus on performance analysis of Model Predictive Controller (MPC) due to the presence of constraint in its algorithm. The controllers are tested in Small-Medium Industry Steam Distillation Plant (SMISD) for regulating the steam temperature at the desired level. The performance of the controller are evaluated based on percentage overshoot, settling time and rise time. The simulation and...
Human action formation primarily concerns automatic brain processes that are responsive to a salient stimulus. Nevertheless, the importance of studying the control of these actions to obtain more flexible and self-regulated behaviours under the intervention of top-down related processes has been noted. In this paper a top-down guided action formation based on automatic pathways with the cognitive...
Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable...
In this paper, two novel supervisory method for Multiple Models Predictive Control (MMPC) algorithm presented. Enhanced decision-making unit in MMPC is designed based on practical applicability notions and new fundamental control concepts. The goal is performance increasing of regulation and disturbance rejection. Presented algorithms are evaluated in simulation study on nonlinear pH neutralization...
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