Greenhouse Air Temperature Control Using the Particle Swarm Optimisation Algorithm
نویسندگان
چکیده
The particle swarm optimisation algorithm is proposed as a new method to design a model based predictive controller subject to restrictions. Its performance is compared with the one obtained by using a genetic algorithm for the environmental temperature control of a greenhouse. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-point tracking and minimisation of the control effort over a prediction horizon of one hour with a one-minute sampling period. Copyright © 2002 IFAC
منابع مشابه
A New Solution for the Cyclic Multiple-Part Type Three-Machine Robotic Cell Problem based on the Particle Swarm Meta-heuristic
In this paper, we develop a new mathematical model for a cyclic multiple-part type threemachine robotic cell problem. In this robotic cell a robot is used for material handling. The objective is finding a part sequence to minimize the cycle time (i.e.; maximize the throughput) with assumption of known robot movement. The developed model is based on Petri nets and provides a new method to calcul...
متن کاملOptimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm
The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and ...
متن کاملAn Energy Efficient Control Strategy for Induction Machines Based on Advanced Particle Swarm Optimisation Algorithms
This paper proposes an energy efficient control strategy for an induction machine (IM) based on two advanced particle swarm optimisation (PSO) algorithms. Two advanced PSO algorithms, known as the dynamic particle swarm optimisation (Dynamic PSO) and the chaos particle swarm optimisation (Chaos PSO) algorithms modify the algorithm parameters to improve the performance of the standard PSO algori...
متن کاملLearning of B-spline Neural Network Using New Particle Swarm Approaches
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 ...
متن کاملPerceptive Particle Swarm Optimisation
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...
متن کامل