Session Program


  • 11 July 2017
  • 08:00AM - 10:00AM
  • Room: Normanna
  • Chairs: Tufan Kumbasar and Hao Ying
Abstract - In this paper, we will present the gain analysis of an Internal Type-2 (IT2) Fuzzy Logic Controller (FLC) that employs the Nie-Tan method and validated our theoretical results on the control of realistic Electric Vehicle (EV) model. In this context, we firstly will present the analytical derivation of the employed IT2-FLC structure and its output in closed form. We will then investigate the gain variations with respect to the Footprint of Uncertainty (FOU) design parameter of the IT2-FLC. We will define aggressive and smooth control regions based on the gain of IT2 FLCs in comparison with its type-1 counterpart. We will also present the FOU parameter settings that obtain aggressive or smooth control actions based on derived controller gains. We will extend these gain analysis into controller design to achieve desired control action. We will present the simulation studies in which aggressive and smooth IT2-FLCs are compared and evaluated on the EV model for different control performance measures. The results will show that the presented gain analysis provides better understanding about the effect of the FOU parameter and an initiative way to tune IT2-FLC for control system applications.
Abstract - In this paper, we will present the novel application of Type-2 (T2) fuzzy logic to solve a real-time pursuit-evasion game problem with the spherical droids Sphero 2.0 and BB8 (products of the Sphero company). The game scenario is constructed as the evader droid BB8 is controlled by a human user while the pursuer droid Sphero 2.0 is navigated through the game environment via the proposed T2 fuzzy pursuing system. The proposed T2 fuzzy pursuing system structure is composed of vision based localization, the error signal generator, T2 fuzzy strategy planner and the control system. The T2 fuzzy strategy planner is the key structure of the pursuing system since it generates the reference trajectories to be followed by the pursuer droid Sphero 2.0. In this paper, we have transformed design guidelines presented for T2 fuzzy logic controllers into two pursuing strategies for the first time in literature. The performances of the proposed T2 fuzzy strategies have been examined by providing comparative experimental results performed in the real-world game environment against a human user. We believe that this pioneer application of the T2 fuzzy logic in pursuit- evasion games will be an important step for a wider deployment in the research area of real world games.
Abstract - In this study, we will present the novel application of Type-2 (T2) fuzzy logic to the popular video game called Lunar Lander. The proposed T2 fuzzy moon landing system structure is composed of the error signal generator and the T2 fuzzy logic control structure which give the opportunity to transform the moon landing problem of the spaceship as a multivariable tracking control problem. The landing problem of the game can be seen as one of the classical multivariable control problems including uncertainties due to the randomization process occurring the game environment. Thus, we will employ T2 fuzzy logic controllers since they are capable of handling a high level of uncertainties. Then, by optimizing the T2 fuzzy moon landing system via the particle swarm optimization, we will show that the resulting T2 fuzzy moon landing system resulted with an adequate control and game performance in the presence of the uncertainties, disturbances and nonlinear system dynamics in comparison with its type-1 and conventional counterparts. We believe that the results of this paper will be an important step for a wider deployment of T2 fuzzy logic in the research area of computer games.
Abstract - This paper presents the application of Interval Type-2 Subsethood Neural Fuzzy Inference System (IT2SuNFIS) [1] in the area of control of a chemical plant and function approximation. In this model, a subsethood method between the inputs and hidden rule layer neurons determines the similarity between interval type-2 fuzzy set (IT2 FS) inputs and IT2 FS antecedents. The inputs to the system are fuzzified using IT2 FS with Gaussian primary membership function (GPMF) having identical mean but different variance. The signal aggregation of type-2 based activation is performed using product operator. This neuro-fuzzy system trains in differential evolution (DE) framework. Different DE learning strategies have been used for this purpose. During the training, different networks are generated and trained using DE methodology. The system is tested on the control of a chemical plant. Comparisons with other type-1 and type-2 neuro-fuzzy models verify the excellent control of the proposed methodology for the control of the chemical plant. The system is also tested on Hang function approximation problem. It is observed that the system performs better than other models reported in literature in terms of lesser number of free parameters; the result accuracy is similar to other models.
Abstract - This paper presents a novel interval type-2 TSK nominal-fuzzy-model-based sliding mode controller (IT2-TSK-NFMSMC) for flexible air-breathing hypersonic vehicle (FAHV) in order to stress robustness of the control system in dealing with data-driven based fuzzy modelling deviations, system uncertainty and disturbances. We adopt backstepping structure decomposing FAHV model into 5 control subsystems and design controllers, respectively. More specifically, two subsystems are designed with integral sliding mode model controllers. Another three subsystems which directly coupling with flexible mode disturbances are designed with IT2-TSK-NFMSMCs by the following steps: 1) interval type-2 TSK nominal-fuzzy-models (IT2-TSK-NFM) are generated automatically by using type-2 fuzzy self-organizing methods from experiment datasets; 2) nominal model sliding mode controllers are designed based on the IT2-TSK-NFM, respectively; 3) notch filters are adopted in order to decrease the disturbance effects from the flexible modes; 4) sliding mode compensation controllers are designed through Lyapunov synthesis in order to compensate differences between IT2-TSK-NFM and real models of the FAHV. Several scenarios are studied and the simulation results validate the robustness of the proposed controllers when there exist internal flexible vibration and external system disturbances.
Abstract - The control performance of type-2 fuzzy logic controller (IT2-FLCs) is heavily dependent on the choice of antecedent and consequent sets. However, there are no clear guidelines on how to choose suitable FOU shape to achieve the desired control requirements. This paper aims to explore how differences in FOU shapes affect the control performance by analysing three different types of antecedent fuzzy sets. They are the triangular top wide, triangular bottom wide and the trapezoidal fuzzy sets. Analytical structures of these controllers are derived. The analytical structures of the triangular bottom wide and trapezoidal controllers show more common features than triangular top wide controller. Based on the characteristics of the analytical structure, it may be hypothesised that the control performances of IT2-FLCs that use triangular bottom wide and trapezoidal antecedent IT2 fuzzy sets would be more similar than an IT2-FLC constructed by triangular top wide antecedent IT2 fuzzy sets. The hypothesis is then verified by simulation results.