Iterative dynamic linearization and self-learning control method for expressway traffic system
A traffic system and learning control technology, applied in the field of iterative dynamic linearization and self-learning control, can solve the problems of transient response performance degradation, target tracking trajectory repetition, etc.
Active Publication Date: 2020-07-28
QINGDAO UNIV OF SCI & TECH
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[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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[0058] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0059] Such as figure 1 As shown, the expressway traffic system includes a single-lane expressway, and each section has an on-ramp and an off-ramp. Its space-discrete traffic flow model is shown in formulas (1)-(4) below.
[0060]
[0061] q i (t) = ρ i (t)v i (t), (2)
[0062]
[0063]
[0064] Among them, h is the sampling time interval; t refers to the tth moment, t∈{0,1,∧,T}; i∈{1,∧,I N} refers to the i-th section of the expressway; I N is the total number of segments; τ, v, k, l, m are constant parameters; ρ i (t) represents the traffic flow density of the i-th section of the expressway at the t-th moment; v i (t) represents the average speed of the i-th section of the expressway at the t-th moment; q i (t) represents the traffic flow of the i-th section of the expressway at the t-th moment; r i (t) represents the traffic flow ra...
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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 expressways 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. In order to better exploit the performance of expressways, on-ramps are a commonly used strategy. The purpose of on-ramp control is to adjust the traffic volume entering the expressway main road at its on-ramp, to ensure that the expected (or optimal) traffic flow is maintained on the downstream arterial expressway, and to maximize the expressway capacity. In practice, at the entrance ra...
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IPC IPC(8): G08G1/01
CPCG08G1/0125G08G1/0145
Inventor 池荣虎林娜姚文龙
Owner QINGDAO UNIV OF SCI & TECH