Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique

Multivariable System Identification of a Continuous Binary Distillation Column

Distillation is a process that is commonly used in industries for separation purpose. A distillation column is a multivariable system which shows nonlinear dynamic behavior due to its nonlinear vapor-liquid equilibrium. In order to gain better product quality and lower energy consumption of the distillation column, an effective model based control system is needed to allow the process to be operated over a certain operating range. In control engineering, System Identification is considered as a well suited approach for developing an approximate model for the nonlinear system. In this study, System Identification technique is applied to predict the top and bottom product composition by focusing the temperature of the distillation column. The process in the column is based on the distillation of a binary mixture of Isopropyl Alcohol and Acetone. The experimental data obtained from the distillation column was used for estimation and validation of simulated models. During analysis, different types of linear and nonlinear models were developed and are compared to predict the best model which can be effectively used for designing the control system of the distillation column. Among the linear models such as; Autoregressive with Exogenous Input (ARX), Autoregressive Moving Average with Exogenous inputs (ARMAX), Linear State Space (LSS) model and Continuous Process Model were developed and compared with each other. The results of this comparison reveals that the perf01mance of LSS model is efficient and hence it was further used to improve the modeling approach and compared with other nonlinear models. A Nonlinear State Space (NSS) model was developed by the combination of LSS and Neural Network (NN) and is compared solely with NN and ANFIS identification model. The simulation results show that the developed NSS model is well capable of defining the dynan1ics of the plant based on the best fit criteria and residual performance. In addition to this, NSS model predicted the best statistical measurement of the nonlinear system. This approach is helpful for designing the efficient control system for online separation process of the plant

Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations

River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values

A non-linear approach to modelling and control of electrically stimulated skeletal muscle

This thesis is concerned with the development and analysis of a non-linear approach to modelling and control of the contraction of electrically stimulated skeletal muscle. For muscle which has lost nervous control, artificial electrical stimulation can be used as a technique aimed at providing muscular contraction and producing a functionally useful movement. This is generally referred to as Functional Electrical Stimulation (FES) and is used in different application areas such as the rehabilitation of paralysed patient and in cardiac assistance where skeletal muscle can be used to support a failing heart. For both these FES applications a model of the muscle is essential to develop algorithms for the controlled stimulation. For the identification of muscle models, real data are available from experiments with rabbit muscle. Data for contraction with constant muscle length were collected from two muscle with very different characteristics. An empirical modelling approach is developed which is suitable for both muscles. The approach is based on a decomposition of the operating space into smaller sub-regions which are then described by local models of simple, possibly linear structure. The local models are blended together by a scheduler, and the resulting non-linear model is called a Local Model Network (LMN). It is shown how a priori knowledge about the system can be used directly when identifying Local Model Networks. Aspects of the structure selection are discussed and algorithms for the identification of the model parameters are presented. Tools of the analysis of Local Model Networks have been developed and are used to validate the models. The problem of designing a controller based on the LMN structure is discussed. The structure of Local Controller Networks is introduced. These can be derived directly from Local Model Networks. Design techniques for input-output and for state feedback controllers, based on pole placement, are presented. Aspects of the generation of optimal stimulation patterns (which are defined as stimulation patterns which deliver the smallest number of pulses to obtain a desired contraction) are discussed, and various techniques to generate them are presented. In particular, it is shown how a control structure can be used to generate optimal stimulation patterns. A Local Controller Network is used as the controller with a design based on a non-linear LMN model of muscle. Experimental data from a non-linear heat transfer process have been collected and are used to demonstrate the basic modelling and control principles throughout the first part of the thesis

Study on identification of nonlinear systems using Quasi-ARX models

制度:新 ; 報告番号:甲3660号 ; 学位の種類:博士(工学) ; 授与年月日:2012/9/15 ; 早大学位記番号:新6026Waseda Universit

Precision Control of a Sensorless Brushless Direct Current Motor System

Sensorless control strategies were first suggested well over a decade ago with the aim of reducing the size, weight and unit cost of electrically actuated servo systems. The resulting algorithms have been successfully applied to the induction and synchronous motor families in applications where control of armature speeds above approximately one hundred revolutions per minute is desired. However, sensorless position control remains problematic. This thesis provides an in depth investigation into sensorless motor control strategies for high precision motion control applications. Specifically, methods of achieving control of position and very low speed thresholds are investigated. The developed grey box identification techniques are shown to perform better than their traditional white or black box counterparts. Further, fuzzy model based sliding mode control is implemented and results demonstrate its improved robustness to certain classes of disturbance. Attempts to reject uncertainty within the developed models using the sliding mode are discussed. Novel controllers, which enhance the performance of the sliding mode are presented. Finally, algorithms that achieve control without a primary feedback sensor are successfully demonstrated. Sensorless position control is achieved with resolutions equivalent to those of existing stepper motor technology. The successful control of armature speeds below sixty revolutions per minute is achieved and problems typically associated with motor starting are circumvented.Research Instruments Ltd

A Promising Wavelet Decomposition –NNARX Model to Predict Flood: Application to Kelantan River Flood

Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2