Session Program


  • 10 July 2017
  • 08:00AM - 10:00AM
  • Room: Normanna
  • Chair: Massimo Esposito

Fuzzy Logic Control and Cognitive Systems-I

Abstract - Cultural adaptation, i.e., the matching of a robot's behaviours to the cultural norms and preferences of its user, is a well known key requirement for the success of any assistive application. However, culture-dependent robot behaviours are often implicitly set by designers, thus not allowing for an easy and automatic adaptation to different cultures. This paper presents a method for the design of culture-aware robots, that can automatically adapt their behaviour to conform to a given culture. We propose a mapping from cultural factors to related parameters of robot behaviours which relies on linguistic variables to encode heterogeneous cultural factors in a uniform formalism, and on fuzzy rules to encode qualitative relations among multiple variables. We illustrate the approach in two practical case studies.
Abstract - This paper proposes an experimental comparison between human imprecise calculation and fuzzy arithmetic: an empirical study has been conducted to collect real intervals resulting from products and additions with imprecise operands from participants. Fuzzy intervals are elicited from these data and fuzzy arithmetic is applied to the collected imprecise operands. Comparisons show that the fuzzy product and addition differ from the way human beings perform these operations. Moreover, they show that the participants, rather than taking into account the imprecisions in the calculations, realise exact calculation and in the end approximate the exact result.
Abstract - Health, Safety, Environment and Ergonomic (HSEE) are important factors in any organization. An organization always have to assess its compliance in these factors to the required benchmarks and take proactive actions to improve them if required. In this paper, we propose a Fuzzy Cognitive Map-Bayesian network (BN) model to identify the KPIs in HSEE measurement and management. Fuzzy Cognitive Map (FCM) method is used for constructing graphical model of BN to ascertain the relationships between the inputs and the impact that they will have on the quantified HSEE. After the KPIs are identified, Noisy-OR method and EM techniques are used to ascertain the conditional probability between the inputs to quantify the HSEE value. Using this, we find out the most influential input factor on HSEE quantification, which can then be managed for improving an organization's compliance to HSEE. Leveraging the power of Bayesian network in modeling HSEE and augmenting it with FCM is the main contribution of this research work which opens this line of research.
Abstract - This paper studies the problems of stability analysis and stabilization for a class of nonlinear systems represented by T-S fuzzy models with time-varying delay. Based on a reinforced Lyapunov-Krasovskii functional, a new delay-dependent criterion for ensuring the asymptotic stability of the concerned fuzzy systems has been derived in terms of linear matrix inequalities (LMI). Then, the state feedback control design is derived to achieve the stabilization. The efficiency and merits of the proposed approach are shown through several numerical examples.
Abstract - In this paper, a novel sliding mode control technique is proposed which benefits from recursive least square adaptation laws. Since this method has high resistance against uncertainty and may result in a desirable transient response, this method is one of the most commonly used nonlinear control methods. In this method, the uncertain dynamics of system is stabilized by applying a discontinuous control signal. The principle of this type of controller is to force the states of the system towards sliding manifold and maintain the states of this stable manifold which defines the desired behavior of the system. It is possible to model dynamic behavior of physical systems in terms of fuzzy systems. In order to tune the parameters of fuzzy system, a cost function based on sliding mode is proposed. The solution to this cost function results in optimal adaptation laws whose stability analysis is done using an appropriate Lyapunov function. The proposed adaptive sliding mode fuzzy controller is simulated on an inverted pendulum to test its efficacy and performance in control of a benchmark system.
Abstract - This paper develops conditions for sensor fault detection and isolation of nonlinear descriptor systems. The proposed methodology is based on a bank of observers, thus a novel approach is proposed to design Takagi-Sugeno observers in descriptor form. Traditionally, for descriptor systems, the designing conditions employ an augmented state vector whose elements are the state and its derivative. The proposed approach overcomes previous results in the literature by means of a novel augmented estimated vector, therefore conditions in terms linear matrix inequalities are directly obtained. The effectiveness of the given methodology is illustrated through a numerical example.