Research Of Energy-saving Control On Intelligent Lighting System

China's large population and energy shortage is seriously constrained the development of the national economy.Saving energy is a long-term strategic policy in the national economic development.In order to meet the growing energy needs of the people,how to carry out energy conservation,get the largest energy efficiency,is one of the main issues that all around the world is generally concerned.In this paper,for the energy-saving lighting,we have designed with a double closed-loop intelligent lighting system.On the one hand to ensure that the real-time illumination is stable near the set value,to achieve the first energy saving.On the other hand to optimize energy consumption,we can achieve the second energy conservation.According to the overall design of the lighting system to select the module,and then build intelligent lighting platform to achieve the function of the system.In order to simplify the control complexity,we will group control all lights.So that there are many combinations of light can reach the set value,our aim is to find a combination that minimum the energy consumption.Through the PID control to ensure that the real-time illumination is stable near the set value.In order to meet the energy optimization needs of intelligent lighting system,we use the extremum seeking algorithm to find the minimum energy consumption.In this paper,we focus on the gradient-based extremum seeking algorithm and the newton-based extremum seeking algorithm,analyze their stability,and carry out detailed theoretical proof.Then we apply these two algorithms to the energy-saving control of the intelligent lighting system,the feasibility and accuracy of two algorithms for energy-saving control can be verified by the experiments in intelligent lighting system.The experiment is divided into single closed-loop experiments and double closed-loop experiments.In order to find the corresponding relationship between input and energy when the illumination is stable,the traversal single closed-loop experiment does not include the extremum seeking control algorithm.And find the relative minimum energy consumption to verify the accuracy of the search results in double closed-loop experiments.According to the experimental results,we found that two algorithms can search the minimum energy consumption when the real-time illumination is stable near the set value,so we achieve the purpose of secondary energy conservation.And in the energy saving effect,we found that the newton-based extremum seeking algorithm is superior to the gradient-based extremum seeking algorithm.Therefore,we have analyzed the performance of these two algorithms separately.Because the estimation of inverse matrix of Hessian matrix,the search rate of the newton-based extremum seeking algorithm is faster than the gradient-based extremum seeking algorithm and the search accuracy is also higher than the gradient-based extreme seeking algorithm.