Mohd Hezri Fazalul Rahiman

Self-tuning fuzzy PID controller for electro-hydraulic cylinder

Hydraulic systems are widely used in industrial applications. This is due to its high speed of response with fast start, stop and speed reversal possible. The torque to inertia ratio is also large with resulting high acceleration capability. The nonlinear properties of hydraulic cylinder make the position tracking control design challenging. This paper presents the development and implementation of self-tuning fuzzy PID controller in controlling the position variation of electro-hydraulic actuator. The hydraulic system mathematical model is approximated using system identification technique. The simulation studies were done using Matlab Simulink environment. The output performance was compared with the design using pole-placement controller. The roots mean squared error for both techniques showed that self-tuning Fuzzy PID produced better result compared to using pole-placement controller.

Modeling of dynamic response of essential oil extraction process

This paper presents a model of dynamic response of essential oil extraction process using system identification approach. A collection of samples was collected from a pilot plant of essential oil extraction process using steam distillation technique. Input signal for the process is pseudo-random binary sequence (PRBS) and the output is temperature. The sample was separated into training and testing data by using interlacing technique. Based on Auto Regressive Exogenous Input (ARX) model validation, the results showed that partial data will produce adequate model to describe the full dynamic of essential oil extraction process.

Model identification and controller design for real-time control of hydraulic cylinder

Hydraulic cylinder has been widely used as an actuator in industrial equipments and processes due to its linear movements, fast response and accurate positioning of heavy load. The nonlinear properties of hydraulic cylinder has challenged researchers to design a suitable controller for position control, motion control, and tracking control. This paper presents model identification and controller design using pole-placement method for real-time control of hydraulic cylinder. The plant mathematical model was approximated using Matlab system identification toolbox from open-loop input-output experimental data. The simulation studies and real-time studies were done using Visual C++ console programming. The simulation and real-time results were compared and they show about similar performances.

Optimized PID control using Nelder-Mead method for electro-hydraulic actuator systems

Despite the application of advanced control technique to improve the performance of electro-hydraulic position control, PID control scheme seems able to produce satisfactory result. PID is preferable in industrial applications because it is simple and robust. The main problem in its application is to tune the parameters to its optimum values. This study will look into an optimization of PID parameters using Nelder-Mead approach for electro-hydraulic position control system. The electro-hydraulic system was represented by an ARX model structure obtained through MATLAB System Identification Toolbox. Second-order and third-order model of the system had been evaluated. Simulation and real-time studies show that ARX211 produced the best response in terms of transient speed and RMSE performance criteria even though the model has the least percentage of best fit.

Essential oil composition of Kaffir lime: Comparative analysis between controlled steam distillation and hydrodistillation extraction process

A controlled steam distillation process was performed to extract essential oil of Kaffir lime. Water temperature in the process was controlled by ON/OFF controller. System performance was quantified based on oil production rate and assessment on the compositions in Kaffir lime leaves and peels respectively. The Kaffir lime essential oil compounds were identified by retention time and percentage area by Gas Chromatography — Mass Spectrophotometry (GC-MS). Results showed the essential oil percentage yield given by modified steam distillation was 1.34% (peels) and 0.43% (leaves) whereas for hydrodistillation yielded 0.16% (peels) and 0.18% (leaves). Based on compositions, oil extracted by controlled steam distillation gain higher percentage of limonene (27.97%) and α/β-pinene (9.82%) compared to hydrodistillation.

Trajectory zero phase error tracking control using comparing coefficients method

This paper presents the studies on trajectory zero phase error tracking control without factorisation of zeros polynomial where the controller parameters are determined using comparing coefficients methods. The controller was applied to two types of third-order non-minimum phase plant. The first plant was having a zero outside and far from the unity circle. Another plant was having a zero outside and near to the unity circle. Simulation and experimental results will be presented to discuss its tracking performance.

Selection of training data for modeling essential oil extraction system using NNARX structure

In this work, the suitable training data for modeling the steam distillation essential oil extraction system is presented. The data were collected from the self-refilling distillation column using RTD sensor with associated signal conditioning circuit. The control signal is on/off The heating system implementing 1.5 kW electrical immersion heater. The power switching is performed using zero-crossing solid-state-relay. The input signals are the PRBS with different probabilities. There are 3 situations of data to be investigated. Since the system is highly-nonlinear, it is expected that the training data that covers the full operating condition will be the optimum training data. These data are separated into training and testing data by interlacing technique, which make the total number of data 6. For each data, the model order selection is based on ARX structure and MDL information criterion. These data are cross-validated between each others and the validation results are presented and concluded. The performance indexes are the percentages of R2 , adjusted-R2 and NMSE.

Assessment of NNARX structure as a global model for self-refilling steam distillation essential oil extraction system

This paper investigates the performance of neural network autoregressive with exogenous input (NNARX) model structure and evaluates the training data that provide robust model on fresh data set. The system under test is a self-refilling steam distillation essential oil extraction system. Two PRBS signals with different probability band were tested at different operating points and conditions. A total of three data sets will be used to evaluate the model. NNARX model was estimated by means of prediction error method with Levenberg-Marquardt algorithm. It is expected that the training data that covers the full operating condition will be the optimum training data. All data are separated into training and testing data by interlacing technique. For each data, the model order selection is based on ARX structure and MDL information criterion. These data are cross-validated between each other and the validation results are presented and concluded. The model performance is based on the R2, adjusted-R2, RMSE and NMSE. The histogram is also used to evaluate the distribution of the one-step-ahead residuals. Overall results have shown that the NNARX model trained with data of full operating condition is the most robust when it is validated on a fresh data set.

Modeling of steam distillation pot with ARX model

This paper present a autoregressive with exogenous input (ARX) was applied in modelling of steam distillation pot system. The system of steam distillation pot are design with special specification interm of material and shape additional with induction heating system that provide a new technologies in extraction of essential oil to fullfill demand for this industry. The performance of ARX was investigates and the training data that provide robust model and fresh data set was evaluate. ARX model was estimated by means of prediction error method and training function of Levenberg-Marquardt algorithm. Data was collected with probability band 0.2 of PRBS input signals. By ARX model structure, the input output data are model. The performance of the model based on coefficient of determination (R2), mean square error(MSE) and loss function of one-step-ahead and residuals. In overall, results shows that ARX have capability in capturing dynamic of the system.

Analysis of weight decay regularisation in NNARX nonlinear identification

This paper presents the analysis of weight decay regularisation, which is one of artificial neural network generalisation categories, in modelling nonlinear behaviour of steam temperature in distillation essential oil extraction system. The modelling is based on the neural network autoregressive with exogenous input structure. During the network training, the optimisation of the network weights has been carried out by minimisation the error through the Levenberg-Marquardt algorithm (LMA). In the weight decay regularisation network training, the LMA has been modified. Several results on unregularised and regularised trainings have been presented, compared and concluded. The results showed that the optimal weights are obtained with the moderate regularisation of the network training.