Introduction to control engineering

This book is intended to serve as a text book for a first course in control system engineering in higher institutes and universities. The text has been written through the experience of the authors in teaching this subject. Control systems are found in a broad range of applications within various engineering disciplines namely electrical, mechanical, chemical or aerospace engineerings. This book emphasizes particularly on the principle, design and analysis of feedback control system. The contents have been written to be suitable for all branches of engineering

User friendly system for the visually impaired in learning Al-Quran

This study presents a method to enable the visually impaired Muslim to learn and read the Al-Quran using Braille Display with software help. The system reads the database which contains all verses of Al-Quran and user will need to select the verse and ayah to read. Besides that, this system can be used in a class to teach visually impaired students to learn Al-Quran. Every word or character typed by the instructor in the main Braille Panel will be transmitted to the sub Braille Panel that is connected to the main Braille Panel. The selected verse of Al-Quran and ayah will also generate an index before being transmitted to the Braille Panel. The index will be transmitted to the Braille Display for people to touch and read the display. A user friendly Graphical User Interface (GUI) will be used to fulfill the ergonomics for the visually impaired user's physical capabilities. Several approaches are used to design and implement the interface for the visually impaired like speech or sound output and Braille display. The Braille codes can be displayed using the Braille panel. The design interface and structure of the system for the visually impaired users in learning Al-Quran is presented

Effect of Penalty Function Parameter in Objective Function of System Identification

The evaluation of an objective function for a particular model allows one to determine the optimality of a model structure with the aim of selecting an adequate model in system identification. Recently, an objective function was introduced that, besides evaluating predictive accuracy, includes a logarithmic penalty function to achieve a suitable balance between the former model’s characteristics and model parsimony. However, the parameter value in the penalty function was made arbitrarily. This paper presents a study on the effect of the penalty function parameter in model structure selection in system identification on a number of simulated models. The search was done using genetic algorithms. A representation of the sensitivity of the penalty function parameter value in model structure selection is given, along with a proposed mathematical function that defines it. A recommendation is made regarding how a suitable penalty function parameter value can be determined

Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model

In this paper, major properties of an adaptive fuzzy model as a system identifier when trained by the back-propagation algorithm are discussed. The standard rule-based fuzzy models were used to identify discrete-time nonlinear dynamic systems. The method of selection of the input variables, the number of rules, and the learning rate are briefly discussed. Three methods for choosing the initial parameter of the fuzzy model are considered, namely the on-line, the off-line, and the random initial parameters. The implementation and the computational aspects of the training algorithm are also highlighted. Three examples of discrete-time nonlinear systems are used in the simulation study to show the effects of user selected conditions on the identification process. The results of the identification procedure show that they approximate the dynamic plants quite well. The correlation based model validity tests are used to validate the identified fuzzy model

Daily streamflow forecasting using simplified rule-based fuzzy logic system

In this study, a simplified fuzzy logic system with uniform partitions in the input space is proposed for forecasting the dailystreamflow of four river systems in Malaysia. The proposed simplified fuzzy logic system was calibrated (trained) using backpropagation(BP) and recursive prediction error (RPE) algorithms. For each catchment, the calibration data set consisted ofthree consecutive years of daily rainfall and streamflow records. Verifications of the calibrated models were done using the data set of the following year. The performances of the simplified fuzzy logic system and the normal fuzzy logic system are compared,with each model having the same number of adjustable parameters. The results are also compared with the auto-regressive with exogenous input model. This study has shown that the proposed RPE algorithm performed better than the more popular BPalgorithm. The results show that all the simplified fuzzy logic system models registered better performance measures for the calibration data sets. However, variable results were obtained for the predictions of the verification data sets

Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification

Abstract-System identification is a method of determining a mathematical relation between variables and terms of a process based on observed input-output data. Model structure selection is one of the important steps in a system identification process. Evolutionary computation (EC) is known to be an effective search and optimization method and in this paper EC is proposed as a model structure selection algorithm. Since EC, like genetic algorithm, relies on randomness and probabilities, it is cumbersome when constraints are present in the search. In this regard, EC requires the incorporation of additional evaluation functions, hence, additional computation time. A deterministic mutation-based algorithm is introduced to overcome this problem. Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods

Implementation of evolutionary optimization techniques in tuning PID parameters for tremor patient active assistive writing device

Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are methods of the Evolutionary Optimization techniques and autonomously tuning method used in this study to tune the parameters of the Proportional-integral-derivative (PID) controller. PID controllers need to be tuned appropriately to establish the good performance of the Active Assistive Writing Device (AAWD). This AAWD device is used to help patients who face difficulties due to hand trembling while writing. The actual hand tremor data while writing was measured by attaching an accelerometer to the device. Based on the simulation results, it has been found that applying PID controller and GA optimization techniques to the assistive device has an enormous potential in helping tremor patients improve their quality of handwriting