Traffic signal self-adaptive iterative learning control method for attenuation memory de-counterfeiting control

Abstract

The invention relates to a traffic signal self-adaptive iterative learning control method for attenuation memory de-counterfeiting control. The method combines attenuation memory de-counterfeiting control with iterative learning control so as to overcome the technical defects of the traditional signal control scheme. Open-loop and closed-loop PD type are adopted for iterative learning control by taking the repetitive characteristic of traffic flow as an entry point, therefore, in order to solve the problem that the influence of traffic flow historical data on the current real-time state is continuously reduced as time goes on, an attenuation memory function is added to a pseudo-removal control performance index function, dynamic change of a closed-loop learning law is realized, and the accuracy of iterative learning is improved, so that the traffic efficiency of a road network is improved.

Description

technical field [0001] The invention relates to the technical field of urban traffic signal control, in particular to a traffic signal self-adaptive iterative learning control method for attenuation memory and false control. Background technique [0002] With the continuous progress and development of social science and technology, the scope of people's life and travel continues to expand, and the improvement of material life prompts people to pursue higher-quality spiritual life. However, these changes will be realized based on the urban transportation system, which makes urban planners Focus on transportation infrastructure. However, under the new development situation, urban traffic also has many new problems. The cross-mixing of various urban traffic problems has brought serious challenges to traffic management departments, such as traffic congestion, environmental pollution, how to coordinate various travel modes, and the use of new energy. Transportation applications,...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to overcome the technical deficiencies of traditional signal control schemes, and propose open-closed-loop PD-type iterative learning control based on the repetitive characteristics of traffic flow. Due to real-time changes in traffic flow, the system structure will change to a certain extent, and the fixed closed-loop The learning law cannot achieve the best iterative effect, and considering that the influence of historical traffic flow data on the current real-time state will continue to decrease over time, it is proposed to add a decay memory function to the performance index function of the de-aliasing control to realize the closed-loop learning law. Dynamic changes to improve the accuracy of iterative learning, thereby improving the traffic efficiency of the road network

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