Open Issues in Control ofAutomotive R744 Air-ConditioningSystems

In this thesis, one of the current control algorithms for the R744 cycle, which tries tooptimize the performance of the system by two SISO control loops, is compared to acost-effective system with just one actuator. The operation of a key component of thissystem, a two stage orifice expansion valve is examined in a range of typical climateconditions. One alternative control loop for this system, which has been proposed byBehr group, is also scrutinized.The simulation results affirm the preference of using two control-loops instead of oneloop, but refute advantages of the Behr alternate control approach against one-loopcontrol. As far as the economic considerations of the A/C unit are concerned, usinga two-stage orifice expansion valve is desired by the automotive industry, thus basedon the experiment results, an improved logic for control of this system is proposed.In the second part, it is investigated whether the one-actuator control approach isapplicable to a system consisting of two parallel evaporators to allow passengers tocontrol different climate zones. The simulation results show that in the case of usinga two-stage orifice valve for the front evaporator and a fixed expansion valve forthe rear one, a proper distribution of the cooling power between the front and rearcompartment is possible for a broad range of climate conditions.

Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities

Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.