abstract: this paper presents dynamic modelling and control of a linear prismatic series of elastic actuator. the capability of generating large torques is why this actuator is increasingly used in human-assistive robotic systems. due to having human in the loop, the actuator requires precise control. a fractional pid controller known for its improved performance is used for the control, due to...

A new methodology for Emergent System Identification is proposed in this paper. The new method applies the self-organizing Group Method of Data Handling (GMDH) functional networks, Particle Swarm Optimization (PSO), and Genetic Programming (GP) that is effective in identifying complex dynamic systems. The focus of the paper will be on how Particle Swarm Optimization (PSO) is applied within Grou...

Particle Swarm Optimization (PSO) is an innovative and competitive optimization technique for numerical optimization with real-parameter representation. This paper examines the working mechanism of PSO in a principled manner with forma analysis and investigates the applicability of PSO on the Quadratic Assignment Problem (QAP). Particularly, the derived PSO operator for QAP is empirically studi...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

Evolutionary Algorithms (EAs) can be used in order to design Particle Swarm Optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of designing PSO algorithms, we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the swarm’s behaviour. The rules that hav...

In nature, the organisms have a limited lifespan and they grow older with time. Aging is an essential process which leads to the maintenance of species diversity in environment. Every group of species is lead by a leader. As the lifespan of every organism is limited, at a certain point of its life time, the organism deteriorates and become inefficient to lead its group. In this situation, a new...

According to researches, activation was a minor strategy of improving Particle Swarm Algorithm. For proofing the possibility of activation strategy could be a main strategy for improving Particle Swarm Algorithm, this article adopt the concept of mutation in Genetic Algorithm to improve Particle Swarm Optimization. The experimental results show three advantages for the activation strategies. Fi...

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by in...

This paper presents a comparative analysis of continuously stirred tank reactor (CSTR) control based on adaptive control and optimal tuning of PID control based on particle swarm optimization. In the design of adaptive control, Model reference adaptive control (MRAC) scheme is used, in which the adaptation law have been developed by MIT rule & Lyapunov’s rule. In PSO control parameters of PID c...

In order to have clarity in the satellite images we have used Particle Swarm Optimization technique. When incorporated with traditional clustering algorithms, problems such as local optima and sensitivity to initialization, are reduced, thus exploring a greater area using global search. This segmented image is further classified using Kappa coefficient. Keywords— Particle Swarm Optimization(PSO...