Iterative dynamic linearization and self-learning control method of express way traffic system

Abstract

The invention relates to an iterative dynamic linearization and self-learning control method of an express way traffic system, and belongs to the technical field of express way traffic control. The method comprises the following steps that (1) a spatial discrete traffic model of the express way traffic system is established; (2) the spatial discrete traffic flow model is expressed in the form a general nonlinear discrete time system; (3) a general nonlinear discrete time model is converted into a dynamic linear data model; and (4) learning control and parameter update rules of the dynamic linear data model are established. The provided LDM-AILC method can be used to process a nonlinear system needless of a known linear parameter structure, and serves as a data driving control method, and design and analysis of a controller are only dependent on I / O data. In addition, under the condition that a random initial state and a tracking object of iterative change can be repeated in a non-strict way, the provided LDM-AILC method can achieve a complete tracking performance.

Description

technical field [0001] The invention relates to the technical field of expressway traffic control, in particular to an iterative dynamic linearization and self-learning control method of an expressway traffic system. Background technique [0002] Expressway traffic control is an important field in traffic engineering and intelligent transportation systems. Frequent congestion on highways during rush hour worsens traffic conditions. The most common causes of expressway congestion include: traffic demand greater than design capacity, traffic accidents, road works and weather conditions. On-ramps are a common strategy for better performance on expressways. The purpose of on-ramp control is to regulate the traffic volume entering the main expressway at its on-ramp, to ensure that the desired (or optimal) traffic flow is maintained on the downstream arterial expressway, and to maximize the expressway capacity. In practice, at the on-ramp, the traffic monitoring device and the ...

AI Technical Summary

Problems solved by technology

[0005] It should be noted that the above-mentioned ILC methods for on-ramp control are linear iterative learning algorithms designed based on compressive mapping and fixed-point theory, which will have two major limitations in practical applications

The first limitation is that, since the convergence of the tracking error is obtained based on the λ-norm, sometimes the transient response performance of the system output along the iterative axis becomes worse

The second limitation is that identical initial states and identical reference trajectories must match for full tracking

However, object tracking trajectories must be strictly repetitive

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