480 research outputs found
Multivariable predictive PID control for quadruple tank
In this paper multivariable predictive PID controller has been implemented on a multi-inputs multi-outputs control problem i.e., quadruple tank system, in comparison with a simple multiloop PI controller. One of the salient feature of this system is an adjustable transmission zero which can be adjust to operate in both minimum and non-minimum phase configuration, through the flow distribution to upper and lower tanks in quadruple tank system. Stability and performance analysis has also been carried out for this highly interactive two input two output system, both in minimum and non-minimum phases. Simulations of control system revealed that better performance are obtained in predictive PID design
NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES
The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved.
Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun
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Nonlinear model predictive control strategy based on soft computing approaches and real time implementation on a coupled-tank system
In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algorithm (GA) and an artificial neural network (ANN) to design both a robust single nonlinear dynamic ANN model derived from an experimental data driven system identification approach and a nonlinear model predictive control (NMPC) strategy. SA is employed to give an initial weight for the training of the ANN model structure while a gradient descent based Levenberg–Marquardt Algorithm (LMA) approach is used to optimise the ANN weights. The designed NMPC strategy is optimised using a stochastic GA optimisation method and is tested first in simulation and then implemented in real time practical experiment on a highly nonlinear single input single output (SISO) coupled tank system (CTS). An excellent control performance is reported over the conventional proportional-integral-derivative (PID) controller and results show the effectiveness of the approach under disturbances. The nonlinear neural network model proved very reliable in different operating regions. The SISO system can be upgraded to multi-input multi-output (MIMO) system while the whole NMPC approach can easily be adapted to other industrial processes
Application of DCS for Level Control in Nonlinear System using Optimization and Robust Algorithms
This proposed work deals with the real-time implementation of a PI level controller for a nonlinear interacting multi-input multi-output (MIMO) system using YOKOGAWA CENTUM CS 3000 DCS. Some intricate algorithms were chosen to tune the PI controller, presuming the effect of disturbances in a nonlinear interacting MIMO system. Three algorithms; a classical evolution algorithm, genetic algorithm (GA); a metaheuristic optimization algorithm, particle swarm optimization algorithm (PSO); and a robust algorithm, quantitative feedback theory (QFT) were chosen to tune thecontroller offline optimally. These controllers were then implemented in the process using distributed control systems (DCS), and the simulation results resulting from the three algorithms were compared with the experimental results. The impact of the tuning algorithms in the controller performance was studied in real-time
Fractional transformation-based decentralized robust control of a coupled-tank system for industrial applications
Petrochemical and dairy industries, waste management, and paper manufacturing fall
under the category of process industries where flow and liquid control are essential. Even when
liquids are mixed or chemically treated in interconnected tanks, the fluid and flow should constantly
be observed and controlled, especially when dealing with nonlinearity and imperfect plant models.
In this study, we propose a nonlinear dynamic multiple-input multiple-output (MIMO) plant model.
This model is then transformed through linearization, a technique frequently utilized in the analysis
and modeling of fractional processes, and decoupling for decentralized fixed-structure H-infinity
robust control design. Simulation tests based on MATLAB and SIMULINK are subsequently executed.
Numerous assessments are conducted to evaluate tracking performance, external disturbance re jection, and plant parameter fluctuations to gauge the effectiveness of the proposed model. The
objective of this work is to provide a framework that anticipates potential outcomes, paving the way
for implementing a reliable controller synthesis for MIMO-connected tanks in real-world scenarios.This research was partially funded by FONDECYT grant number 1200525 (V.L.) from
the National Agency for Research and Development (ANID) of the Chilean government under the
Ministry of Science, Technology, Knowledge, and Innovation; and by Portuguese funds through the
CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020
and UIDP/00013/2020 (C.C.)
Multivariable model predictive control of a pilot plant using Honeywell profit suite
This thesis documents the first implementation of Profit Suite into Murdoch University’s Pilot Plant. This Pilot Plant is a small scale model of the Bayer Alumina Process. Profit Suite is a Honeywell application that uses Model Predictive Control (MPC) for Multivariable Control (MVC). The major project objective was to connect Profit Suite to the exiting Experion PKS control system then compare multivariable model predictive control to the existing PI control scheme. The project objectives were achieved. Multivariable controllers were built that controlled temperatures and levels in both halves of the plant.
The OPC connections between Profit Suite and Experion were completed and documented, as well as the procedures used to build and commission Profit Controllers in the Pilot Plant. Multivariable level controllers were designed using accurate models that performed well. These MVCs performed better than PI control in that they managed all tank levels and recycle streams throughout the plant. Linear objective functions were used to optimize flows and levels with success.
Baseline testing of the PI Controllers showed they were better than the MVCs for temperature control. The steam pressure disturbance had no effect on temperatures controlled by fast executing Experion PI controllers. Models found for steam pressure caused MVCs to overcompensate for this temperature disturbance. An MVC built that could manipulate steam valve positions to control temperature performed poorly compared to PI control. Multivariable temperature control was significantly improved when all pumps and steam valves were used as Manipulated Variables by the MVC. Models between water flow rates and temperatures enabled the MVC to use additional pump MVs to counteract the steam pressure disturbance.
There was no existing instrumentation to measure steam flowrates from each valve. This required Profit Suite to connect to the OP point of the PI Controllers to directly manipulate valve position for temperature control. Temperature control by cascaded PI steam flow control is recommended to improve the performance of multivariable temperature control. The installation of steam flow transmitters will enable the set point of a PI flow controller to be used as an MV by Profit Control. Fundamental models between steam flowrates and tank temperatures could then be acquired for multivariable control
Plantwide Control and Simulation of Sulfur-Iodine Thermochemical Cycle Process for Hydrogen Production
A PWC structure has developed for an industrial scale SITC plant. Based on the performance evaluation, it has been shown that the SITC plant developed via the proposed modified SOC structure can produce satisfactory performance – smooth and reliable operation. The SITC plant is capable of achieving a thermal efficiency of 69%, which is the highest attainable value so far. It is worth noting that the proposed SITC design is viable on the grounds of economic and controllability
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