K Das Sharma

Bio: K Das Sharma is an academic researcher from West Bengal University of Technology. The author has contributed to research in topics: Algorithm design & Adaptive control. The author has an hindex of 1, co-authored 1 publications receiving 54 citations.

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

Abstract: 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.

Feature Selection With Harmony Search

Abstract: Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.

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

Abstract: 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.