Interactions in swarm robotic search are explored for intelligence emergence based on Extended Particle Swarm Optimization (EPSO) model. For this end, the best combination of proper properties in typical versions of PSO is transferred to swarm robotic search. Synchronous / asynchronous communication modes and respective control strategies under conditions of parallel distributed control are com...

Conventional particle swarm optimisation relies on exchanging information through social interaction among individuals. However for real-world problems involving control of physical agents (i.e., robot control), such detailed social interaction is not always possible. In this study, we propose the Perceptive Particle Swarm Optimisation algorithm, in which both social interaction and environment...

In this paper, we integrate, implement, and validate formation flying algorithms for large number of agents using probabilistic swarm guidance with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each time step in a statistically independent manner while the swarm c...

We present an active control scheme of a kinetic model of swarming. It has been shown previously that the global control scheme for the model, presented in [1], gives rise to spontaneous collective organization of agents into a unified coherent swarm, via a long-range attractive and short-range repulsive potential. We extend these results by presenting control laws whereby a single swarm is bro...

Controlling large swarms of robotic agents has many challenges including, but not limited to, computational complexity due to the number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm’s configuration. This work generalizes the swarm state using Random Finite Set (RFS) theory and solves the control problem using model predictive control which...

Robot swarms modeled after hub-based colonies, such as ants and bees, potentially offer fault-tolerant capabilities at very favorable cost margins. However, relatively little is known about how to harness the potential of these swarms through command-and-control systems. In this paper, we study how to merge operator input with the underlying swarm behavior to maintain the fault-tolerant attribu...

In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuz...

one of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. in this paper, the decoupled sliding mode control technique (dsmc) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. furthermore, a new multi-objective particle swarm o...

oil production optimization is one of the main targets of reservoir management. smart well technology gives the ability of real time oil production optimization. although this technology has many advantages; optimum adjustment or sizing of corresponding valves is still an issue to be solved. in this research, optimum port sizing of inflow control devices (icds) which are passive control valves ...

Many cooperative control problems ranging from formation following, to rendezvous to flocking can be expressed as consensus problems. The ability of an operator to influence the development of consensus within a swarm therefore provides a basic test of the quality of human-swarm interaction (HSI). Two plausible approaches are : Directdictate a desired value to swarm members or Indirectcontrol o...