Goals reasoning and management of pilot is a key issue to monitor pilot's behavior and intention. Traditional modeling methods are based on scenarios or situations, such methods will cause the,covering problem due...Goals reasoning and management of pilot is a key issue to monitor pilot's behavior and intention. Traditional modeling methods are based on scenarios or situations, such methods will cause the,covering problem due to redundancy and are incapable of depicting interactions among various goals and plans of pilot. Petri net integrated with belief, desire and intention (BDI) theory (BDI Petri net) is designed to solve this problem. Focusing on the BDI theory, goal states of agent are discussed firstly. Belief, desire and intention are modeled by places and transitions based on the Petri net theory. In order to simplify the network, colored token is introduced to depict various states of belief, and the hierarchy transition is applied to model the intention, together with tokens' flow derrionstrating the interaction among various goals and relationship among belief, desire and intention. A search and rescue mission is used to validate the proposed method and the result indicates that the model can be used to monitor goals and behaviors of pilots.展开更多
PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to tak...PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.展开更多
文摘Goals reasoning and management of pilot is a key issue to monitor pilot's behavior and intention. Traditional modeling methods are based on scenarios or situations, such methods will cause the,covering problem due to redundancy and are incapable of depicting interactions among various goals and plans of pilot. Petri net integrated with belief, desire and intention (BDI) theory (BDI Petri net) is designed to solve this problem. Focusing on the BDI theory, goal states of agent are discussed firstly. Belief, desire and intention are modeled by places and transitions based on the Petri net theory. In order to simplify the network, colored token is introduced to depict various states of belief, and the hierarchy transition is applied to model the intention, together with tokens' flow derrionstrating the interaction among various goals and relationship among belief, desire and intention. A search and rescue mission is used to validate the proposed method and the result indicates that the model can be used to monitor goals and behaviors of pilots.
文摘PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.
