Robust Adaptive Controls of a Vehicle Seat Suspension System

This work proposes two novel adaptive fuzzy controllers and applies them to vibration control of a vehicle seat suspension system subjected to severe road profiles. The first adaptive controller is designed by considering prescribed performance of the sliding surface and combined with adaptation laws so that robust stability is guaranteed in the presence of external disturbances. As for the second adaptive controller, both the H-infinity controller and sliding mode controller are combined using inversely fuzzified values of the fuzzy model. In order to evaluate control performances of the proposed two adaptive controllers, a semi-active vehicle suspension system installed with a magneto-rheological (MR) damper is adopted. After determining control gains, two controllers are applied to the system and vibration control performances such as displacement at the driver’s position are evaluated and presented in time domain. In this work, to demonstrate the control robustness two severe road profiles of regular bump and random step wave are imposed as external disturbances. It is shown that both adaptive controllers can enhance ride comfort of the driver by reducing the displacement and acceleration at the seat position. This excellent performance is achieved from each benefit of each adaptive controller; accurate tracking performance of the first controller and fast convergence time of the second controller

On transitioning from type-1 to interval type-2 fuzzy logic systems

Capturing the uncertainty arising from system noise has been a core feature of fuzzy logic systems (FLSs) for many years. This paper builds on previous work and explores the methodological transition of type-l (Tl) to interval type-2 fuzzy sets (IT2 FSs) for given "levels" of uncertainty. Specifically, we propose to transition from Tl to IT2 FLSs through varying the size of the Footprint Of Uncertainty (FOU) of their respective FSs while maintaining the original FS shape (e.g., triangular) and keeping the size of the FOU over the FS as constant as possible. The latter is important as it enables the systematic relating of FOU size to levels of uncertainty and vice versa, while the former enables an intuitive comparison between the Tl and T2 FSs. The effectiveness of the proposed method is demonstrated through a series of experiments using the well-known Mackey-Glass (MG) time series prediction problem. The results are compared with the results of the IT2 FS creation method introduced in [1] which follows a similar methodology as the proposed approach but does not maintain the membership function (MF) shape

Global Research Performance on the Design and Applications of Type-2 Fuzzy Logic Systems: A Bibliometric Analysis

There has been a significant contribution to scientific literature in the design and applications of Type-2 fuzzy logic systems (T2FLS). The T2FLSs found applications in many aspects of our daily lives, such as engineering, pure science, medicine and social sciences. The online web of science was searched to identify the 100 most frequently cited papers published on the design and application of T2FLS from 1980 to 2016. The articles were analyzed based on authorship, source title, country of origin, institution, document type, web of science category, and year of publication. The correlation between the average citation per year (ACY) and the total citation (TC) was analyzed. It was found that there is a strong relationship between the ACY and TC (r = 0.91643, P<0.01), based on the papers consider in this research.  The “Type -2 fuzzy sets made simple” authored by Mendel and John (2002), published in IEEE Transactions on Fuzzy Systems received the highest TC as well as the ACY. The future trend in this research domain was also analyzed. The present analysis may serve as a guide for selecting qualitative literature especially to the beginners in the field of T2FLS

Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets

In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both interpolation and extrapolation with multiple multi-antecedent rules. However, the difficult problem of defining the precise-valued membership functions required in the representation of fuzzy rules, or of the observations, restricts its applications. Fortunately, this problem can be alleviated through the use of type-2 fuzzy sets, owing to the fact that the membership functions of such fuzzy sets are themselves fuzzy, providing a more flexible means of modelling. This paper therefore, extends the existing T-FRI approach using interval type-2 fuzzy sets, which covers the original T-FRI as its specific instance. The effectiveness of this extension is demonstrated by experimental investigations and, also, by a practical application in comparison to the state-of-the-art alternative approach developed using rough-fuzzy setspublishersversionPeer reviewe

Perbandingan Performa Logika Fuzzy Tipe-1 Dan Logika Fuzzy Tipe-2 Pada Sistem Pasteurisasi Susu Berbasis Mikrokontroler

Milk is a perishable food product, to extend its shelf life, a heating technique called pasteurization can be applied. The purpose of pasteurization is to kill pathogenic bacteria that can be harmful to human health and minimize the growth of other spoilage microorganisms. This study aims to design a milk pasteurization system that can regulate temperature stably in the HTST pasteurization method using type-1 fuzzy logic and type-2 fuzzy logic. Type-2 fuzzy logic is a further development of type-1 fuzzy logic, with an additional dimension of membership function, allowing type-2 fuzzy logic systems to represent more flexible fuzzy sets and better represent uncertainty than type-1 fuzzy logic. Two tests were conducted to compare the performance of the two systems, one with no disturbance (noise) and other with disturbance. The result showed that in the test with no disturbance, type-2 fuzzy logic performed better than type-1 fuzzy logic in terms of maximum overshoot, while type-1 fuzzy logic performed better in terms of rise time. However, in the test with disturbance, type-2 fuzzy logic outperformed type-1 fuzzy logic at achieving rise time and settling time and was able to maintain or approach the temperature setpoint for a longer period than type-1 fuzzy logic.Susu merupakan bahan makanan yang mudah rusak, untuk memperpanjang ketahanan susu dapat dilakukan teknik pemanasan yang disebut dengan pasteurisasi. Proses pasteurisasi tersebut bertujuan untuk membunuh bakteri patogen yang dapat membahayakan kesehatan manusia dan meminimalkan perkembangan mikroorganisme pembusuk lainnya. Penelitian ini bertujuan untuk merancang sistem pasteurisasi susu yang mampu mengatur suhu secara stabil pada proses pasteurisasi susu metode High Temperature Short Time (HTST) menggunakan logika fuzzy tipe-1 dan logika fuzzy tipe-2.  Logika fuzzy tipe-2 merupakan pengembangan lebih lanjut dari logika fuzzy tipe-1, dengan adanya tambahan dimensi dari fungsi keanggotaannya, memungkinkan sistem logika fuzzy tipe-2 dapat merepresentasikan himpunan fuzzy yang lebih fleksibel dan dapat merepresentasikan ketidakpastian lebih baik daripada logika fuzzy tipe-1. Dua pengujian dilakukan untuk membandingkan performansi kedua sistem tersebut yaitu pada kondisi tanpa adanya gangguan (noise) dan kondisi dengan adanya gangguan. Hasilnya menunjukkan bahwa pada pengujian tanpa adanya gangguan, logika fuzzy tipe-2 unggul dalam hal maximum overshoot, sedangkan logika fuzzy tipe-1 unggul dalam rise time. Namun, dalam pengujian dengan adanya gangguan, logika fuzzy tipe-2 lebih baik dalam mencapai rise time dan settling time, serta mampu menjaga suhu tetap maupun mendekati setpoint lebih lama dibandingkan dengan logika fuzzy tipe-1

EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time-delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters

Nie-Tan Method and its Improved Version: A Counterexample

Context: The bottleneck on interval type-2 fuzzy logic systems is the output processing when using Centroid Type-Reduction + Defuzzification (CTR+D method). Nie and Tan proposed an approximation to CTR+D (NT method). Recently, Mendel and Liu improved the NT method (INT method). Numerical examples (due to Mendel and Liu) exhibit the NT and INT methods as good approximations to CTR+D.Method: Normalization to the unit interval of membership function domains (examples and counterexample) and variables involved in the calculations for the three methods. Examples (due to Mendel and Liu) taken from the literature. Counterexample with piecewise linear membership functions. Comparison by means of error and percentage relative error.Results: NT vs. CTR+D: Our counterexample showed an error of 0.1014 and a percentage relative error of 30.53%. This is respectively 23 and 32 times higher than the worst case obtained in the examples. INT vs. CTR+D: Our counterexample showed an error of 0.0725 and a percentage relative error of 21.83%. This is respectively 363 and 546 times higher than the worst case obtained in the examples.Conclusions: NT and INT methods are not necessarily good approximations to the CTR+D method

Study on sensible beginning divided-search enhanced Karnik-Mendel algorithms for centroid type-reduction of general type-2 fuzzy logic systems

General type-2 fuzzy logic systems (GT2 FLSs) on the basis of alpha-plane representation of GT2 fuzzy sets (FSs) have attracted considerable attention in recent years. For the kernel type-reduction (TR) block of GT2 FLSs, the enhanced Karnik-Mendel (EKM) algorithm is the most popular approach. This paper proposes the sensible beginning divided-search EKM (SBDEKM) algorithms for completing the centroid TR of GT2 FLSs. Computer simulations are provided to show the performances of the SBDEKM algorithms. Compared with EKM algorithms and sensible beginning EKM (SBEKM) algorithms, the SBDEKM algorithms have almost the same accuracies and better computational efficiency