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...

This paper introduces a novel fuzzy adaptive optimization strategy (FAOPSO) for the particle swarm algorithm. Initially, to avoid falling into local optimums, the information of multioptimum distribution state is introduced into the particle swarm movement programming. However, in this kind of multi-optimum static programming mode (MSPPSO), the programming proportion factor of multi-optimum can...

In recent years Evolutionary Computation has its growth to extent. Amidst various Evolutionary computation approaches, Genetic Algorithms and Particle swarm optimisation are used in optimisation problems. The two approaches find a solution to a given objective function employing different procedures and computational techniques; as a result their performance can be evaluated and compared. The p...

Swarm intelligence deals with natural and arti cial systems composed of many individuals that coordinate using decentralized control and self-organization [1, 2, 5]. The main focus of swarm intelligence research is on the collective behaviour that results from local interactions of individuals with each other and with their environment. There are many examples of natural systems that are studie...

A cognitive collaborative multi-agent control architecture that addresses real-world control problems for swarms of mobile robots is proposed. The swarm's emergent behaviour is obtained by using a distributed Particle Swarm Optimization inspired algorithm. A swarm-user interface is also presented and offers a way for a human operator to interact with and guide the robotic swarm without limiting...

This paper deals with the design of optimal backstepping controller, by using the chaotic particle swarm optimization (CPSO) algorithm to control of chaos in Lur’e like chaotic system. The backstepping method consists of parameters which could have positive values. The parameters are usually chosen optional by trial and error method. The controlled system provides different behaviors for differ...

In this paper we described the hardware architecture of an inexpensive, heterogeneous, mobile robot swarm, designed and developed at RISC lab, University of Bridgeport. Each UB robot swarm is equipped with sensors, actuators, control and communication units, power supply, and interconnection mechanism. This article also describes the essential features and design of a dynamic power distribution...

New approaches of particle swarm optimisation algorithm based on Gaussian and Cauchy distributions to adjust the control points of B-spline neural networks are proposed. B-spline networks are trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, particle swarm optimisation  ...

This paper presents a convergence analysis of particle swarm optimization system by treating it as a discrete-time linear time-variant system firstly. And then, based on the results of system convergence conditions, dynamic optimal control of a deterministic PSO system for parameters optimization is studied by using dynamic programming; and an approximate dynamic programming algorithm – swarm-b...

Previous work has demonstrated the feasibility of using process-oriented programming to implement simple subsumption architectures for robot control. However, the utility and scalability of process-based subsumption architectures for more complex tasks and those involving multiple robots has not been proven. We report our experience of applying these techniques to the implementation of a standa...