Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is pro...Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is proposed to ensure cooking temperature uniformity. The control strategy introduces expert experiences to adjust the single neuron gain K, while a single neuron PID self-learning and adaptive ability, as well as cascade advantage can be combined to realize the real-time and fast temperature difference control. In the Simulink, the s-function of this control strategy is used to carry out a dynamic simulation experiment with temperature difference characteristics and verify the robustness and response to model mismatch. Compared to conventional temperature difference-flow PID cascade control and single neuron PID cascade control, this control strategy has better robustness and stronger adaptability. The results of real-time control on the THJSK-1 experiment platform indicate this control strategy is feasible.展开更多
In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To ana...In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.展开更多
文摘Considering the temperature difference of displacement cooking characterized by severe non-linearity, large time delay, and real-time control, a cascade PID adaptive control strategy composed of a single neuron is proposed to ensure cooking temperature uniformity. The control strategy introduces expert experiences to adjust the single neuron gain K, while a single neuron PID self-learning and adaptive ability, as well as cascade advantage can be combined to realize the real-time and fast temperature difference control. In the Simulink, the s-function of this control strategy is used to carry out a dynamic simulation experiment with temperature difference characteristics and verify the robustness and response to model mismatch. Compared to conventional temperature difference-flow PID cascade control and single neuron PID cascade control, this control strategy has better robustness and stronger adaptability. The results of real-time control on the THJSK-1 experiment platform indicate this control strategy is feasible.
基金supported by the National Natural Science Foundation of China(Nos.51204185 and 41674030)Natural Youth Science Foundation of Jiangsu Province,China(No.BK20140185)+1 种基金China Postdoctoral Science Foundation(No.2016M601909)the Fundamental Research Funds for the Central Universities(No.2014QNA44)
文摘In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.
