Dynamic Simulation of Quadruple Tank System

The problem of estimating state of dynamical system from only input and output measurement remain always an important field in the system theory. In fact, observers play a key roles during monitoring of process, a there are shown an essential component in many control application such as output regulation. Ahhough the theories and applications for linear systems are well developed, development of observers for nonlinear system still provides an open area for research. Quadruple tank system and its mathematical model with typical parameters value will be collected from the reflected real system of quadruple tank. Dynamics simulation will be performed and through the MATLAB® enhancement. Various input changes take part. The result of the simulation will be analyzed and reported

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

Dynamic Simulation of Quadruple Tank System

The problem of estimating state of dynamical system from only input and output measurement remain always an important field in the system theory. In fact, observers play a key roles during monitoring of process, a there are shown an essential component in many control application such as output regulation. Ahhough the theories and applications for linear systems are well developed, development of observers for nonlinear system still provides an open area for research. Quadruple tank system and its mathematical model with typical parameters value will be collected from the reflected real system of quadruple tank. Dynamics simulation will be performed and through the MATLAB® enhancement. Various input changes take part. The result of the simulation will be analyzed and reported

Nonlinear predictive restricted structure control

This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system.This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Advanced PID Control Optimisation and System Identification for Multivariable Glass Furnace Processes by Genetic Algorithms

This thesis focuses on the development and analysis of general methods for the design of optimal discrete PID control strategies for multivariable glass furnace processes, where standard genetic algorithms (SGAs) are applied to optimise specially formulated objective functions. Furthermore, a strong emphasis is given on the realistic model parameters identi cation method, which is illustrated to be applicable to a wide range of higher order model parameters identi cation problems. A complete, realistic and continuous excess oxygen model with nonlinearity effect was developed and the model parameters were identified. The developed excess oxygen model consisted of three sub-models to characterise the real plant response. The developed excess oxygen model was evaluated and compared with real plant dynamic response data, which illustrated the high degree of accuracy of the developed model. A new technique named predetermined time constant approximation was proposed to make an assumption on the initial value of a predetermined time constant, whose motive is to facilitate the SGAs to explore and exploit an optimal value for higher order of continuous model's parameters identi cation. Also, the proposed predetermined time constant approximation technique demonstrated that the population diversity is well sustained while exploring the feasible search region and exploiting to an optimal value. In general, the proposed method improves the SGAs convergence rate towards the global optimum and illustrated the effectiveness. An automatic tuning of decentralised discrete PID controllers for multivariable processes, based on SGAs, was proposed. The main improvement of the proposed technique is the ability to enhance the control robustness and to optimise discrete PID parameters by compensating the loop interaction of a multivariable process. This is attained by adding the individually optimised objective function of glass temperature and excess oxygen processes as one objective function, to include the total effect of the loop interaction by applying step inputs on both set points, temperature and excess oxygen, at two different time periods in one simulation. The effectiveness of the proposed tuning technique was supported by a number of simulation results using two other SGAs conventional tuning techniques with 1st and 2nd order control oriented models. It was illustrated that, in all cases, the resulting discrete PID control parameters completely satisfied all performance specifications. A new technique to minimise the fuel consumption for glass furnace processes while sustaining the glass temperature is proposed. This proposed technique is achieved by reducing the excess oxygen within the optimum thermal efficiency region within 1.7% to 3.2%, which is approximately equal to about 10% to 20% of excess air. Therefore, by reducing the excess oxygen set point within the optimum region, 2.45% to 2%, the fuel consumption is minimised from 0:002942kg/sec to 0:002868kg/sec while the thermal efficiency of the glass temperature is sustained at the desired set point (1550K). In addition, a reduction in excess oxygen within methane combustion guidelines will assure that undesirable emissions are in control throughout the combustion process. The efficiencies of the proposed technique were supported by a number of simulation results applying the three SGAs controller tuning techniques. It was illustrated that, in all cases, the fraction of excess oxygen reduction results in a great minimisation of fuel consumption over long plant operating periods