Seref Naci Engin

Determination of the relationship between sewage odour and BOD by neural networks

Sewage treatment works are one of the major sources that cause atmospheric odour pollution. Due to the increase in environmental concerns, there is a growing number of complaints on odour nuisance. In order to determine the boundaries of legal standards, reliable and efficient odour measurement methods need to be defined. An electronic nose was used for the purpose of characterising sewage odours. Samples collected at different locations of a wastewater treatment plant were classified using an Artificial Neural Network (ANN) trained with a back-propagation algorithm. Additionally, the same method was used to determine the relation between sewage sample odours and their related Biochemical Oxygen Demand (BOD) values. The overall results have indicated that ANNs can be used to classify the sewage samples collected from different locations of a wastewater treatment plant. Moreover, the electronic nose output could be used as an indicator in monitoring the biochemical activities of wastewaters.

Design, dynamic modelling and experimental validation of a 2DOF flexible antenna sensor

A two-degree-of-freedom flexible antenna sensor platform was designed to physically simulate the ability of a robotic arm, which rapidly reorients and targets itself towards specific surfaces from different approachable angles. An accurate antenna model involves non-linear expressions that represent the system dynamics. Therefore, a comprehensive study along with experimental work has been carried out in order to achieve accurate system identification and validate the dynamic model. The model developed has proven useful in controlling the antenna tip, minimising the effects of the non-linear flexural dynamics and the Coulomb friction. The system was driven by servo motors. Algebraic controllers were developed for the antenna tip to track the reference trajectory. The platform system used encoders to measure the joint angles and a loadcell sensor to obtain the flexible link tip position. To validate the sensory information, the results obtained by the integrated sensors were compared to that of an external camera system.

Time-series forecasting by means of linear and nonlinear models

The main objective of this paper is two folds. First is to assess some well-known linear and nonlinear techniques comparatively in modeling and forecasting financial time series with trend and seasonal patterns. Then to investigate the effect of pre-processing procedures, such as seasonal adjustment methods, to the improvement of the modeling capability of a nonlinear structure implemented as ANNs in comparison to the classical Box-Jenkins seasonal autoregressive integrated moving average (ARIMA) model, which is widely used as a linear statistical time series forecasting method. Furthermore, the effectiveness of seasonal adjustment procedures, i.e. direct or indirect adjustments, on the forecasting performance is evaluated. The Autocorrelation Function (ACF) plots are used to determine the correlation between lags due to seasonality, and to determine the number of input nodes that is also confirmed by trial-and-errors. The linear and nonlinear models mentioned above are applied to aggregate retail sales data, which carries strong trend and seasonal patterns. Although, the results without any pre-processing were in an acceptable interval, the overall forecasting performance of ANN was not better than that of the classical method. After employing the right seasonal adjustment procedure, ANN has outperformed its linear counterpart in out-of-sample forecasting. Consequently, it is confirmed that the modeling capability of ANN is improved significantly by using a pre-processing procedure. The results obtained from both ARIMA and ANNs based forecasting methodologies are analyzed and compared with Mann-Whitney statistical test.

Modeling of a Coupled Industrial Tank System with ANFIS

Since liquid tank systems are commonly used in industrial applications, system-related requirements results in many modeling and control problems because of their interactive use with other process control elements. Modeling stage is one of the most noteworthy parts in the design of a control system. Although nonlinear tank problems have been widely addressed in classical system dynamics, when designing intelligent control systems, the corresponding model for simulation should reflect the whole characteristics of the real system to be controlled. In this study, a coupled, interacting, nonlinear liquid leveling tank system is modeled using ANFIS (Adaptive-Network-Based Fuzzy Inference System), which will be further used to design and apply a fuzzy-PID control to this system. Firstly, mathematical modeling of the system is established and then, data gathered from this model is employed to create an ANFIS model of the system. Both mathematical and ANFIS model is compared, model consistencies are discussed, and flexibility of ANFIS modeling is shown.

A robust single input adaptive sliding mode fuzzy logic controller for automotive active suspension system

The proposed controller in this paper, which combines the capability of fuzzy logic with the robustness of sliding mode controller, presents prevailing results with its adaptive architecture and proves to overcome the global stability problem of the control of nonlinear systems. Effectiveness of the controller and the performance comparison are demonstrated with chosen control techniques including PID and PD type self-tuning fuzzy controller on a quarter car model which consists of component-wise nonlinearities.

A study on the tip tracking control of a single flexible beam

This sequential paper aims to present studies on modelling and tip tracking control of a flexible single beam. It first outlines the flexible-beam robotic mechanism that was designed and built to be used for the force and torque sensory information-based modelling and control. It then details the vibration suppression controller strategy that is applied to this robotic system. The controller is designed with respect to a simple lumped model describing the dynamics of the system. Here the dynamics of the closed-loop controlled motor is inverted in order to obtain a system with unity dynamics. Further, the flexible-beam dynamics is input state linearized. Finally, a simple external feedback control, which is based on the measurements of beam deflections using a force and torque sensor, is implemented. The complete experimental setup was positioned by two servo-motors controlled by a proportional-integral-derivative controller for each axis. The proposed controllers allow the flexible beam to move continuously in a precise manner, so that it could be treated as an accurate positioning sensor. Simulation and experimental results provided at the end illustrate that the controllers designed and implemented produce a satisfactory control performance and adequate robustness to model uncertainties and system nonlinearities.

A stewart platform as a FBW flight control unit for space vehicles

A variety of flight control units have been put into realization for navigational purposes of spatially moving vehicles, which is mostly manipulated by 2–3 degrees-of-freedom (DOF) joysticks. Since motion in space consists of three translational motions in forward, side and vertical directions and three rotational motions about these axis; with present joystick interfaces, spatial vehicles has to employ more than one navigational control unit to be able to navigate on all required directions. In this study, a 3×3 Stewart-Platform-based FBW (Fly-By-Wire) flight control unit with force feedback is presented which will provide single point manipulation of any space vehicle performing spatial motions along three translational and three rotational axis. Within the frame of this paper, design, capability and the advantages of the novel system is mentioned. Kinematics of the Stewart Platform (SP) mechanism employed and its motion potentials is presented by simulations and workspace of the system is evaluated. Dynamic analysis by Bond-Graph approach will be mentioned. Mechatronic design of the complete structure is discussed and force reflection capability of the system with simulations is pointed out using stiffness control. Finally, the possible future work of the subject is discussed which may include the feasible solutions of the SP in terms of size and safety when implementing inside a cockpit.

e-NOSE response classification of sewage odors by neural networks and fuzzy clustering

Each stage of the sewage treatment process emits odor causing compounds and these compounds may vary from one location in a sewage treatment works to another. In order to determine the boundaries of legal standards, reliable and efficient odor measurement methods need to be defined. An electronic NOSE equipped with 12 different polypyrrole sensors is used for the purpose of characterizing sewage odors. Samples collected at different locations of a WWTP were classified using a fuzzy clustering technique and a neural network trained with a back-propagation algorithm.