System identification: theory for the user
System identification: theory for the user
Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Model-based predictive control of greenhouse climate for reducing energy and water consumption
Computers and Electronics in Agriculture
Greenhouse climate modelling and robust control
Computers and Electronics in Agriculture
Artificial Intelligence techniques: An introduction to their use for modelling environmental systems
Mathematics and Computers in Simulation
Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem
Transportation Science
Expert Systems with Applications: An International Journal
Finite cut-based approximation of fuzzy sets and its evolutionary optimization
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Particle swarm optimization for gantry control: a teaching experiment
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Computers and Electronics in Agriculture
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The particle swarm optimisation algorithm is proposed as a new method to design a model-based predictive greenhouse air temperature controller subject to restrictions. Its performance is compared with the ones obtained by using genetic and sequential quadratic programming algorithms to solve the constrained optimisation air temperature control problem. 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 1h with 1-min sampling period, for a greenhouse located in the north of Portugal. Since the controller must be able to predict the greenhouse environmental conditions over the specified time interval, it is necessary to use mathematical models that describe the greenhouse climate, as well as to predict the outside weather. These requirements are met by using auto regressive models with exogenous inputs and time series auto-regressive models to simulate the inside and outside climate conditions, respectively. These models have time variant parameters and so, recursive identification techniques are applied to estimate their values in real-time. The models employ data from the climate inside and outside the greenhouse, as well as from the control inputs. Simulations with the proposed methodology to design the model-based predictive air temperature controller are presented. The results indicate a better efficiency of the particle swarm optimisation algorithm as compared with the efficiencies obtained with a genetic algorithm and a sequential quadratic programming method.