Anjan Rakshit

A Hybrid Approach for Design of Stable Adaptive Fuzzy Controllers Employing Lyapunov Theory and Particle Swarm Optimization

This paper proposes a new approach for designing stable adaptive fuzzy controllers, which employs a hybridization of a conventional Lyapunov-theory-based approach and a particle swarm optimization (PSO) based stochastic optimization approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and can simultaneously provide satisfactory performance. The design methodology for the controller simultaneously utilizes the good features of PSO (capable of providing good global search capability, required to provide a high degree of automation) and Lyapunov-based tuning (providing fast adaptation utilizing a local search method). Three different variants of the hybrid controller are proposed in this paper. These variants are implemented for benchmark simulation case studies and real-life experimentation, and their results demonstrate the usefulness of the proposed approach.

Stability analysis for continuous system with additive time-varying delays: A less conservative result

This paper presents a less conservative result for stability analysis of continuous-time systems with additive delays by constructing a new Lyapunov-Krasovskii functional and utilizing free matrix variables in approximating certain integral quadratic terms in obtaining the stability condition in terms of linear matrix inequalities. Numerical example is provided to show the effectiveness of the proposed method compared to some recent results.

Design of a Hybrid Stable Adaptive Fuzzy Controller Employing Lyapunov Theory and Harmony Search Algorithm

This brief proposes hybrid stable adaptive fuzzy controller design procedures utilizing the conventional Lyapunov theory and, the relatively newly devised harmony search (HS) algorithm-based stochastic approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory performance with a high degree of automation in the design process. Two different variants of the hybrid controller are proposed in this work. These variants are implemented for a benchmark simulation case study and real-life experimentation. The results obtained demonstrate the usefulness of the proposed approach.

A PSO–Lyapunov Hybrid Stable Adaptive Fuzzy Tracking Control Approach for Vision-Based Robot Navigation

This paper proposes a novel methodology for autonomous mobile robot navigation utilizing the concept of tracking control. Vision-based path planning and subsequent tracking are performed by utilizing proposed stable adaptive state feedback fuzzy tracking controllers designed using the Lyapunov theory and particle-swarm-optimization (PSO)-based hybrid approaches. The objective is to design two self-adaptive fuzzy controllers, for -direction and -direction movements, optimizing both its structures and free parameters, such that the designed controllers can guarantee desired stability and, simultaneously, can provide satisfactory tracking performance for the vision-based navigation of mobile robot. The design methodology for the controllers simultaneously utilizes the global search capability of PSO and Lyapunov-theory-based local search method, thus providing a high degree of automation. Two different variants of hybrid approaches have been employed in this work. The proposed schemes have been implemented in both simulation and experimentations with a real robot, and the results demonstrate the usefulness of the proposed concept.

Influential rule search scheme (IRSS) - a new fuzzy pattern classifier

Automatic generation of fuzzy rule base and membership functions from an input-output data set, for reliable construction of an adaptive fuzzy inference system, has become an important area of research interest. We propose a new robust, fast acting adaptive fuzzy pattern classification scheme, named influential rule search scheme (IRSS). In IRSS, rules which are most influential in contributing to the error produced by the adaptive fuzzy system are identified at the end of each epoch and subsequently modified for satisfactory performance. This fuzzy rule base adjustment scheme is accompanied by an output membership function adaptation scheme for fine tuning the fuzzy system architecture. This iterative method has shown a relatively high speed of convergence. Performance of the proposed IRSS is compared with other existing pattern classification schemes by implementing it for Fishers iris data problem and Wisconsin breast cancer data problems.

Development and study of an automatic AC bridge for impedance measurement

An automatic ac bridge method employing the principle of balancing by means of a stochastic gradient search algorithm is presented. Among the various gradient search techniques, the Widrow-Hoff least mean square (LMS) technique has been chosen as it involves a low computational burden. The LMS algorithm has been used by the authors for the operation of an ac bridge operating with continuous variables. The relevant operation has been verified by simulation; it can also measure negative impedance. A relation has been established between parameters to be used in the LMS algorithm for discrete and continuous versions of the bridge. The balance convergence time for a relatively simple form of the LMS bridge, namely, the R-R type bridge, has been theoretically established and experimentally verified both by simulation and real-time implementation. The frequency response of the LMS algorithm has also been determined.

An application of an LMS adaptive algorithm for a digital AC bridge

This paper describes a new microprocessor controlled ac bridge method using an LMS adaptive algorithm. The main advantages of the method include: (i) the algorithm is simple to implement because no complex computations are actually involved, and (ii) slowly time varying impedances may also be measured or monitored. A realtime implementation of the bridge has been made and test results obtained have been incorporated in the paper.

Development of a real-life EKF based SLAM system for mobile robots employing vision sensing

Developing real-life solutions for implementation of the simultaneous localization and mapping (SLAM) algorithm for mobile robots has been well regarded as a complex problem for quite some time now. Our present work demonstrates a successful real implementation of extended Kalman filter (EKF) based SLAM algorithm for indoor environments, utilizing two web-cam based stereo-vision sensing mechanism. The vision-sensing mechanism is a successful development of a real algorithm for image feature identification in frames grabbed from continuously running videos on two cameras, tracking of these identified features in subsequent frames and incorporation of these landmarks in the map created, utilizing a 3D distance calculation module. The system has been successfully test-run in laboratory environments where the robot is commanded to navigate through some specified waypoints and create a map of its surrounding environment. Our experimentations showed that the estimated positions of the landmarks identified in the map created closely tallies with the actual positions of these landmarks in real-life.

A Real-Time Palm Dorsa Subcutaneous Vein Pattern Recognition System Using Collaborative Representation-Based Classification

This paper describes the development of a real-time system for the recognition of a real human subject using the palm dorsa subcutaneous vein pattern (PDSVP) as a physiological biometric feature. The system has been developed using a low-cost, single board computer, called the Raspberry Pi Model B, in conjunction with an infrared sensitive camera, called the Raspberry Pi No Infrared camera, and other components. The camera is sensitive to near infrared (NIR) radiations and this acquisition property has been used to acquire the pattern of vascular structure present in the subcutaneous layer of the dorsum of the human palm. Moreover, an automatic two-axis pan-tilt mechanism has been developed on which the camera is mounted. This is a completely novel mechanism that has been developed so that the data acquisition is independent of the position where the palm dorsum is positioned, as an automatic palm dorsum self-locating strategy is developed using the two-axis pan-tilt mechanism. Now, the NIR images of the PDSVP acquired, in the aforementioned methodology, do not represent the vein pattern with appreciable clarity and discernibility. Therefore, each image acquired undergoes few steps of image preprocessing, to extract the vein pattern, before they are subjected to testing conditions or they are incorporated into the training database. The recognition strategy has been developed using the collaborative representation-based classification. In this paper, we have emphasized upon the most severe case of small sample size, which is single sample per person-based training data set creation. The proposed method is tested on a well-structured database, of NIR images of the PDSVP, JU-NIR-V1: NIR Vein Database, developed in the Electrical Instrumentation and Measurement Laboratory, Electrical Engineering Department, Jadavpur University, Kolkata, India. Subsequently, through extensive experimentation it has been proven that the proposed strategy attains substantially high and stable recognition rate. Moreover, the performance of the recognition strategy is highly robust even in the presence of artifacts, such as angular displacement and scaling, that corrupt the NIR images acquired during data acquisition.

State feedback stabilization of uncertain linear time‐delay systems: A nonlinear matrix inequality approach

A nonlinear matrix inequality is derived as a stabilizability condition of linear uncertain time-delay systems. This inequality is seen as a less conservative one as well as efficient for numerical computation than the existing results as seen when solving by cone-complementary algorithm. Copyright