This paper presents an intelligent method of tuning a flat controller for trajectory tracking of a single link flexible manipulator. The tip position of the manipulator was determined as the flat output. Using the flat output, a feedback control law is designed to stabilize the tip of the end effector and track the reference trajectories. The gains obtained for the flat controller is optimized ...

In this paper, we will proposed a hybrid method to generate fuzzy membership function automatically. Particle Swarm Optimization (PSO) is used as optimized algorithm, supplement the performance of fuzzy system. The PSO is able to generate an optimal set of parameter for the membership functions automatic adjustment. Fuzzy control system that automatically backs up a truck to a specified point o...

This paper provides an overview of the SWARM-BOTS project, a robotic project sponsored by the Future and Emerging Technologies program of the European Commission. The paper illustrates the goals of the project, the robot prototype and the 3D simulator we built. It also reports on the results of experimental work in which distributed adaptive controllers are used to control a group of real, or s...

This paper presents a new optimization model – EPSO, Evolutionary Particle Swarm Optimization, inspired in both Evolutionary Algorithms and in Particle Swarm Optimization algorithms. The fundamentals of the method are described, and an application to the problem of Loss minimization and Voltage control is presented, with very good results.

In this paper we present the Behaviosite Paradigm, a new approach to coordination and control of distributed agents in a multiagent system, inspired by biological parasites with behavior manipulation properties. Behaviosites are code modules that “infect” a system, attaching themselves to agents and altering the sensory activity and actions of those agents. These behavioral changes can be used ...

The paper proposes two Nonlinear Model Predictive Control schemes that uncover a synergistic relationship between on-line receding horizon style computation and Particle Swarm Optimization, thus benefiting from both the performance advantages of on-line computation and the desirable properties of Particle Swarm Optimization. After developing these techniques for the unconstrained nonlinear opti...

This Paper presents a comparative study of Genetic Algorithm method (GA) and Particle swarm optimization (PSO) method to determine the optimal proportional-integral-derivative (PID) controller parameters, for load frequency control in a single area power system. Comparing with conventional Proportional–Integral (PI) method and the proposed PSO the performance of the controller is improved for t...

In this paper, a new algorithm to optimize the state feedback linearization matrix of two-link PenduBot control system. Mature method of comparison, the best of the simulation results of the particle swarm optimization algorithm can make the object more stable. Obviously, the particle swarm algorithm is introduced to the angular displacement and speed of smaller each link, less overshoot and a ...

in this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationope...

In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization...