Temperature controller optimization by computational intelligence

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several meta heuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency

Temperature controller optimization by computational intelligence

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several meta heuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency

Vortex lattices in strong type-II superconducting two-dimensional strips

We show how to calculate semi-analytically the dense vortex state in strong type-II superconducting nanostructures. For the specific case of a strip, we find vortex lattice solutions which also incorporate surface superconductivity. We calculate the energy cost to displace individual vortex rows parallel to the surfaces and find that this energy oscillates with the magnetic field. Remarkably, we also find that, at a critical field $H^*$ below $H_{c2}$, this ''shear'' energy becomes strictly zero for the surface rows due to an unexpected mismatch with the bulk lattice.Comment: Title, abstract, and some text paragraphs have been rewritte

First-Order Melting and Dynamics of Flux Lines in a Model for YBa2_2Cu3_3O7−δ_{7-\delta}

We have studied the statics and dynamics of flux lines in a model for YBCO, using both Monte Carlo simulations and Langevin dynamics. For a clean system, both approaches yield the same melting curve, which is found to be weakly first order with a heat of fusion of about $0.02 k_BT_m$ per vortex pancake at a field of $50 {\rm kG}.$ The time averaged magnetic field distribution experienced by a fixed spin is found to undergo a qualitative change at freezing, in agreement with NMR and $\mu {\rm SR}$ experiments. Melting in the clean system is accompanied by a proliferation of free disclinations which show a clear B-dependent 3D-2D crossover from long disclination lines parallel to the c-axis at low fields, to 2D ``pancake'' disclinations at higher fields. Strong point pins produce a logarithmical $\ln t$ relaxation which results from slow annealing out of disclinations in disordered samples.Comment: 31 pages, latex, revtex, 12 figures available upon request, No major changes to the original text, but some errors in the axes scale for Figures 6 and 7 were corrected(new figures available upon request), to be published in Physical Review B, July 199