Freeway Traffic Density and On-Ramp Queue Control via ILC Approach

Chi, Ronghu; Li, Mengze; Hou, Zhongsheng; Liu, Xiangpeng; Yu, Zhensheng

doi:10.1155/2014/321934

Cited by 9 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since E k (t) is bounded for any iteration k, the two universal barrier functions as in (11) and (12) are bounded, hence (13) and (14) will always be met during operation. Furthermore, from the design of Ω zL, k , Ω zH, k , and k , in (15), (17), and (16), respectively, we see that, over t ∈ [T 1 , T], + (a 3 − a 1 )e −a 2 k < z e, k < 2 + ′ + e −a 2 k a 3 + e −a 5 k a 4 , which gives a prescribed performance range of the tracking error z e, k over the iteration domain.…”

Section: Satisfaction Of the Constraint Requirementsmentioning

confidence: 99%

“…12 It has been widely used in industrial applications like robotic systems, motion control, hard disk drives, chemical plants, traffic control, high-speed trains, to name just a few. [13][14][15][16][17][18][19] In ILC problems, there exists an infinite iteration domain and a time domain in each iteration that is fixed and finite. ILC controllers can take advantage of the repetitiveness in the system dynamics and/or tasks, so that to improve the tracking performance asymptotically or exponentially over the iteration domain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Jin

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, we present a novel parametric iterative learning control (ILC) algorithm to deal with trajectory tracking problems for a class of nonlinear autonomous agents that are subject to actuator faults. Unlike most of the ILC literature, the desired trajectories in this work can be iteration dependent, and the initial position of the agent in each iteration can be random. Both parametric and nonparametric system unknowns and uncertainties, in particular the control input gain functions that are not fully known, are considered. A new type of universal barrier functions is proposed to guarantee the satisfaction of asymmetric constraint requirements, feasibility of the controller, and prescribed tracking performance. We show that under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive number and zero, respectively, over the iteration domain, beyond a small user‐prescribed initial time interval in each iteration. A numerical simulation is presented in the end to demonstrate the efficacy of the proposed algorithm.

show abstract

Section: Satisfaction Of the Constraint Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Jin

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Extended algorithms of ALINEA such as downstream-measurement-based adaptive ALINEA (AD-ALINEA), upstream-measurement-based adaptive ALINEA (AU-ALINEA) [12], and proportional-integral extension of ALINEA (PI-ALINEA) [13] have recently been proposed. In addition, intelligent control algorithms such as iterative learning control [14][15][16], fuzzy logic control (FLC), neural network control [17], and a reinforcement learning control algorithm [18] are used in local ramp metering. Coordinated ramp metering strategies such as METALINE, FLOW, the Zone algorithm, Helper, and SWARM aim at improving the network-wide traffic efficiency of freeways by making full use of all on-ramps.…”

Section: Introductionmentioning

confidence: 99%

Traffic Model and On‐Ramp Metering Strategy under Foggy Weather Conditions Using T‐S Fuzzy Systems

Sun

Gao

2019

Complexity

View full text Add to dashboard Cite

Foggy weather seriously deteriorates the performance of freeway systems, particularly regarding traffic safety and efficiency. General macroscopic traffic models have difficulty reflecting the characteristics of a freeway under foggy weather conditions. In the present study, a macroscopic traffic model using a correction factor under foggy weather conditions is therefore proposed, which is regulated according to the different levels of visibility and curve radius of the freeway using the Takagi–Sugeno (T-S) model. Based on the proposed traffic model, a local ramp metering strategy with density correction under foggy weather conditions is proposed to improve traffic safety. The proposed local ramp metering strategy regulates the on-ramp flow using the T-S model according to the mainstream density, speed, and visibility. The correction factors are determined based on the parameters of the consequent part in the T-S model, which are optimized using the particle swarm optimization algorithm. The sum of the mean absolute percentage error of the mainstream traffic density and speed is used to evaluate the proposed traffic model. The real-time crash-risk prediction model, which reflects the degree of traffic safety, is used to evaluate the proposed local ramp metering strategy. Simulations using VISSIM and MATLAB show that the proposed traffic model is suitable under foggy weather conditions and that the proposed local ramp metering strategy achieves a better performance in reducing fog-related crashes.

show abstract

“…Recently, Wang and Papageorgiou suggested PI-ALINEA which is a Proportional-Integral extension of ALINEA [5]. In addition, the iterative learning control (ILC) based ramp metering methods have been proposed to keep mainline density at a desired level [6][7][8][9]. Hou et al [6] exploited the pure ILC-based ramp metering approach.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], the modified ILC add-on to ALINEA has been analysed. Chi et al [9] presented a new queue length information fusion based iterative learning control approach for freeway traffic ramp metering. In addition, some advanced control strategies such as fuzzy logic control and neural networks control [10] were developed in local ramp metering.…”

Section: Introductionmentioning

confidence: 99%

Local Freeway Ramp Metering using Self-Adjusted Fuzzy Controller

2018

Teh. vjesn.

View full text Add to dashboard Cite

A self-adjusted fuzzy local ramp metering strategy is proposed to keep the mainline traffic state and the on-ramp queue length at reasonable levels. The fuzzy ramp metering strategy (FRMS) takes the following variables as inputs: error between desired density and measured density, change-in-error and on-ramp queue length. On-ramp metering flow is decided by these variables. It is difficult to construct fuzzy rules for a three-dimension inputs fuzzy controller based on expert knowledge, so the proposed FRMS generates fuzzy control rules by an analytic expression with correction factors. The correction factors reflect the weights upon linguistic variables of inputs and can be regulated according to actual traffic state of mainline and on-ramp. The proposed FRMS not only simplifies the process of rules definition for a multi-dimension fuzzy controller, but also has function of self-adjusted control rules. To examine the proposed FRMS, a freeway stretch in Los Angeles is simulated with distributed models. The proposed FRMS is also compared with an existing T-S FRMS and PI-ALINEA in the simulation experiments which cover different on-ramp inflow scenarios. Simulation results show the proposed FRMS provides improved adaptation to various scenarios and superiority in striking a balance between the mainline and on-ramp performances.

show abstract

Freeway Traffic Density and On-Ramp Queue Control via ILC Approach

Cited by 9 publications

References 17 publications

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Traffic Model and On‐Ramp Metering Strategy under Foggy Weather Conditions Using T‐S Fuzzy Systems

Local Freeway Ramp Metering using Self-Adjusted Fuzzy Controller

Contact Info

Product

Resources

About