Gülay Öke Günel

An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.

Generalized self-tuning regulator based on online support vector regression

This paper introduces a novel generalized self-tuning regulator based on online support vector regression (OSVR) for nonlinear systems. The main idea is to approximate the parameters of an adaptive controller by optimizing the regression margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, and the parameters of the adaptive controller are optimized so as to minimize the tracking error which leads simultaneously to the optimization of the closed-loop margin. The overall architecture consists of an online SVR which computes a forward model of the system, an adaptive controller with tunable parameters and an adaptation mechanism realized by separate online SVRs to estimate each tunable controller parameter. The proposed architecture is implemented with adaptive proportional–integral–derivative (PID) and adaptive fuzzy PID in the controller block. The performance of the generalized self-tuning regulator mechanism has been examined via simulations performed on a bioreactor benchmark system, and the results show that the generalized adaptive controller and OSVR model attain good control and modeling performances.

Improving human-robot interaction based on joint attention

The current study proposes a novel cognitive architecture for a computational model of the limbic system, inspired by human brain activity, which improves interactions between a humanoid robot and preschool children using joint attention during turn-taking gameplay. Using human-robot interaction (HRI), this framework may be useful for ameliorating problems related to attracting and maintaining attention levels of children suffering from attention deficit hyperactivity disorder (ADHD). In the proposed framework, computational models including the amygdala, hypothalamus, hippocampus, and basal ganglia are used to simulate a range of cognitive processes such as emotional responses, episodic memory formation, and selection of appropriate behavioral responses. In the currently proposed model limbic system, we applied reinforcement and unsupervised learning-based adaptation processes to a dynamic neural field model, resulting in a system that was capable of observing and controlling the physical and cognitive processes of a humanoid robot. Several interaction scenarios were tested to evaluate the performance of the model. Finally, we compared the results of our methodology with a neural mass model.

A Novel Adaptive NARMA-L2 Controller Based on Online Support Vector Regression for Nonlinear Systems

In this study, a novel nonlinear autoregressive moving average (NARMA)-L2 controller based on online support vector regression (SVR) is proposed. The main idea is to obtain a SVR based NARMA-L2 model of a nonlinear single input single output system (SISO) by decomposing a single SVR which estimates the nonlinear autoregressive with exogenous inputs (NARX) model of the system. Consequently, using the obtained SVR-NARMA-L2 submodels, a NARMA-L2 controller is designed. The performance of the proposed SVR based NARMA-L2 controller has been evaluated by simulations carried out on a bioreactor system, and the results show that the SVR based NARMA-L2 model and controller attain good modelling and control performances. Robustness of the controller in the case of system parameter uncertainty and measurement noise have also been examined.

Controlling 3-DOF helicopter via fuzzy PID controller

In this paper, the real time tracking performance of the fuzzy PID controller has been evaluated on nonlinear 3-DOF helicopter system. Quanser 3-DOF helicopter system has been utilized as experimental setup. Three separate fuzzy PID controller have been employed so as to control three state of the system. The dynamics of the system has been altered via counterweight in order to examine the performances of the both controller for parametric uncertainty in system parameter. The results reveal that the fuzzy PID controller leads to a good tracking performance and has better parametric uncertainty rejection capability than LQR.

A case study in Istanbul E-80: Intelligent speed change at the highway for reducing gas emission

Greenhouses gases (GHGs) are a major problem in the world since they cause warming of the atmosphere and climate change which lead to global warming. Gasoline from transportation systems is the main source of GHGs. Frequent acceleration processes in vehicles cause unnecessary fuel consumption. If unnecessary acceleration is prevented, emission of GHGs will be reduced. In this research, unnecessary acceleration is decreased by changing line speed limit with an intelligent method at the highway. We propose an intelligent speed change (ISC) algorithm, which estimates the number of vehicles waiting in the traffic and calculates a speed limit to decrease unnecessary accelerations. A case study is done to reduce the gas emission at Istanbul E-80 highway with Istanbul Technical University City Transportation Simulation (ITUCiTSim) program. Test section is selected as Şekerpinar-Selimpasa at one direction. An Intelligent Highway is constructed by placing sensors at access road connections. Traffic on the road is modelled by using the main principles of queueing theory. Information that is acquired by sensors is used in determining new speed limit in an intelligent way. It is demonstrated by simulations that the proposed method leads to a decrease in unnecessary accelerations, which results in a subsequent decrease in emitted GHGs.

Rehabilitation applications using brain inspired cognitive architecture for humanoid robots

In this paper, interaction between humans and robots and for rehabilitation applications in social areas is investigated. People suffering from some disorders require better nursing service for interacting socially. Additionally, utilizing human-robot interaction (HRI), this proposed architecture may be a suitable resolution for problems related to optimizing the focusing and sustaining attention states of children with Attention Deficit Hyperactive Disorder (ADHD) and Autism Spectrum Disorder (ASD). In the near future, it is expected that humanoid robots will have more interactive skills in social regions.

Design of an Adaptive Support Vector Regressor Controller for a Spherical Tank System

In this study, an adaptive support vector regressor (SVR) controller which has previously been proposed [1] is applied to control the liquid level in a spherical tank system. The variations in the cross sectional area of the tank depending on the liquid level is the main cause of nonlinearity in system. The parameters of the controller are optimized depending on the future behaviour of the system which is approximated via a seperate online SVR model of the system. In order to adjust controller parameters, the “closed-loop margin” which is calculated using the tracking error has been optimized. The performance of the proposed method has been examined by simulations carried out on a nonlinear spherical tank system, and the results reveal that the SVR controller together with SVR model leads to good tracking performance with small modeling, transient state and steady state errors.