A Data-Driven Iterative Feedback Tuning Approach of ALINEA for Freeway Traffic Ramp Metering With PARAMICS Simulations

Chi, Ronghu; Hou, Zhongsheng; Jin, Shangtai; Wang, Danwei; Hao, Jiangen

doi:10.1109/tii.2013.2238548

Cited by 47 publications

(25 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned above, the device utilises a novel type of pneumatic powered actuator, and the speed and range of patient's recovery can lead to significant changes to the model of the rehabilitation robot, all of which indicate that a model-based method is not suitable, and a robust model-free method will be a prime candidate. Iterative feedback tuning (IFT) is a model-free data-driven learning method [15]. Further, IFT is very much suitable for repeated rehabilitation trajectories due to the requirement of specially designed experiments.…”

Section: Introductionmentioning

confidence: 99%

“…One shortcoming of these methods if applying them to our system is that the dynamic modelling of pneumatic muscles and robot behaviour would bring heavy computation burden to the control system, and it is also difficult to model the patient's arbitrary activities. One advantage of IFT technique is its ability to learn from repeating scenarios and optimise the controller parameters without knowledge of the actual system, which guarantees the inherent robustness of such a method [15,17,28]. To obtain a more robust control system, the design criterion or objective function of IFT must be taken into account.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

Meng

Xie

Quan

et al. 2017

IEEE/ASME Trans. Mechatron.

113

View full text Add to dashboard Cite

Abstract-Robot-assisted rehabilitation offers benefits such as repetitive, intensive and task specific training as compared to traditional manual manipulation performed by physiotherapists. In this paper, a robust iterative feedback tuning (IFT) technique for repetitive training control of a compliant parallel ankle rehabilitation robot is presented. The robot employs four parallel intrinsically compliant pneumatic muscle actuators that mimic skeletal muscles for ankle's motion training. A multiple degreesof-freedom normalised IFT technique is proposed to increase the controller robustness by obtaining an optimal value for the weighting factor and offering a method with learning capacity to achieve an optimum of the controller parameters. Experiments with human participants were conducted to investigate the robustness as well as to validate the performance of the proposed IFT technique. Results show that the normalised IFT scheme will achieve a better and better tracking performance during the robot repetitive control and provides more robustness to the system by adapting to various situations in robotic rehabilitation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

Meng

Xie

Quan

et al. 2017

IEEE/ASME Trans. Mechatron.

113

View full text Add to dashboard Cite

show abstract

“…For example, as a popular local responsive feedback ramp metering strategy, ALINEA is verified to be effective in throughput maximization, congestion alleviation, and risk reduction in both field test and simulation. Recent years, researchers pay their attention to advanced tuning approaches for feedback gains and operational parameters in ALINEA [23], [24], integration of ALINEA with Iterative Learning Control (ILC) [25], and the derivatives of ALINEA, e.g. PI-ALINEA [26].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Optimal Control for Time-Efficient Highway Traffic Management

Liu

Dai

et al. 2018

2018 Annual American Control Conference (ACC)

View full text Add to dashboard Cite

This article examines the hybrid traffic control problem to minimize total travel time (TTT) of a highway network through traffic management infrastructures, including dynamic speed limit signs, ramp metering, and information board.We first build the traffic flow model based on the Moskowitz function for each highway link to predict traffic status within a control horizon. The traffic density is predicted based on the flow dynamic model and corrected periodically by measured traffic flow data. The minimum TTT traffic control problem is then formulated as a mixed-integer quadratic programming problem with quadratic constraints. Numerical simulation of a real world highway network is provided to demonstrate significant reduction of TTT and alleviation of traffic congestion compared to results obtained from ALINEA and PI-ALINEA methods. Abstract-This article examines the hybrid traffic control problem to minimize total travel time (TTT) of a highway network through traffic management infrastructures, including dynamic speed limit signs, ramp metering, and information board.We first build the traffic flow model based on the Moskowitz function for each highway link to predict traffic status within a control horizon. The traffic density is predicted based on the flow dynamic model and corrected periodically by measured traffic flow data. The minimum TTT traffic control problem is then formulated as a mixed-integer quadratic programming problem with quadratic constraints. Numerical simulation of a real world highway network is provided to demonstrate significant reduction of TTT and alleviation of traffic congestion compared to results obtained from ALINEA and PI-ALINEA methods.

show abstract

“…The performance of the these strategies relies heavily on the existence of an accurate and reliable mathematical model of the process [6], [7]. Though the first principle model is preferred for process modeling, the process complexity and the lack of extensive understanding of the process greatly limit its application in process industry [8]. Alternatively, system identification based on informative process data has drawn considerable attentions of both academic researchers and industrial practitioners [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian Inference for FIR Models With Randomly Missing Measurements

Yang

Yin

2017

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

This paper is concerned with parameter and output estimation for industrial processes described by finite impulse response (FIR) model in presence of randomly missing output measurements. The statistical models for describing the estimation problems are given and the prior distributions over unknown parameters and variables are constructed. The estimation problems with incomplete data set are formulated in the variational Bayesian framework and the problems of randomly missing measurements, overfitting, and high sensitivity of parameter estimate to noise are handled simultaneously. The iterative formulas to estimate the posterior distributions of missing output data and unknown parameters based on available process data are derived. The simulation example and the hybrid tank system experiment are performed to demonstrate the effectiveness of the proposed method.

show abstract

A Data-Driven Iterative Feedback Tuning Approach of ALINEA for Freeway Traffic Ramp Metering With PARAMICS Simulations

Cited by 47 publications

References 23 publications

Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

Hybrid Optimal Control for Time-Efficient Highway Traffic Management

Variational Bayesian Inference for FIR Models With Randomly Missing Measurements

Contact Info

Product

Resources

About