scholarly journals Iterative Learning Control with Forgetting Factor for Urban Road Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Tianyi Lan ◽  
Fei Yan ◽  
Hui Lin
Keyword(s):  
Traffic Congestion ◽  
Iterative Learning Control ◽  
Road Network ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Forgetting Factor ◽  
Urban Road Network ◽  
Traffic Condition ◽  
Urban Road

In order to improve the traffic condition, a novel iterative learning control (ILC) algorithm with forgetting factor for urban road network is proposed by using the repeat characteristics of traffic flow in this paper. Rigorous analysis shows that the proposed ILC algorithm can guarantee the asymptotic convergence. Through iterative learning control of the traffic signals, the number of vehicles on each road in the network can gradually approach the desired level, thereby preventing oversaturation and traffic congestion. The introduced forgetting factor can effectively adjust the control input according to the states of the system and filter along the direction of the iteration. The results show that the forgetting factor has an important effect on the robustness of the system. The theoretical analysis and experimental simulations are given to verify the validity of the proposed method.

scholarly journals Widespread Traffic Congestion Prediction for Urban Road Network Based on Synergetic Theory

2014 ◽  
Vol 2 (4) ◽  
pp. 366-371 ◽  
Author(s):  
Liangliang Zhang ◽  
Yuanhua Jia ◽  
Zhonghai Niu ◽  
Cheng Liao
Keyword(s):  
Predictive Models ◽  
Traffic Congestion ◽  
Road Network ◽  
Urban Road Network ◽  
Urban Road ◽  
Self Organized ◽  
Congestion Prediction ◽  
Self Organized Criticality ◽  
Synergetic Theory

AbstractThe traffic congestion often occurs in urban road network. When one of the sections becomes congested, it will lead to a series of congestions in other sections. The traffic congestion spreads rapidly until part of road network becomes congestion ultimately. In this case, the paper investigates the mechanism of the traffic congestion in urban road network and points out that subsystems of the traffic congestion always perform completive and cooperative functions in the process of traffic congestion. The process behaves in a manner of self-organized criticality, which can be forecasted. The paper also establishes synergetic predictive models based on self-organized criticality of the synergetic theory. Finally, the paper takes Beijing road network as an example to forecast the widespread traffic congestion. The result shows that the established models are accuracy, and the traffic congestion is featured of self-organized criticality.

scholarly journals Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Yun-Shan Wei ◽  
Qing-Yuan Xu
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Initial State ◽  
Tracking Errors ◽  
Discrete Time Systems ◽  
P Type ◽  
Time Systems

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

Iterative Learning Control for a Class of Modeling Undesirable Single Degree of Freedom System

2014 ◽  
Vol 538 ◽  
pp. 379-382
Author(s):  
Wei Zhou ◽  
Bao Bin Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Degree Of Freedom ◽  
Control Input ◽  
Convergence Properties ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Simulation Results

A class of modeling undesirable single degree of freedom system is studied by using iterative learning control. The proposed iterative learning algorithm constantly updates the control input according to output error until the desired output occurred. So the system with designed controller can achieve perfect accuracy. We have proved convergence properties in iteration domain and simulation results demonstrate the effectiveness of the presented method.

scholarly journals A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Prediction Algorithm ◽  
Control Law ◽  
Control Input

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

scholarly journals Iterative Learning Control of a Nonlinear Aeroelastic System despite Gust Load

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xing-zhi Xu ◽  
Ya-kui Gao ◽  
Wei-guo Zhang
Keyword(s):  
Control Strategy ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Surface ◽  
Stream Velocity ◽  
Control Input ◽  
Parameter Uncertainties ◽  
Composite Energy

The development of a control strategy appropriate for the suppression of aeroelastic vibration of a two-dimensional nonlinear wing section based on iterative learning control (ILC) theory is described. Structural stiffness in pitch degree of freedom is represented by nonlinear polynomials. The uncontrolled aeroelastic model exhibits limit cycle oscillations beyond a critical value of the free-stream velocity. Using a single trailing-edge control surface as the control input, a ILC law under alignment condition is developed to ensure convergence of state tracking error. A novel Barrier Lyapunov Function (BLF) is incorporated in the proposed Barrier Composite Energy Function (BCEF) approach. Numerical simulation results clearly demonstrate the effectiveness of the control strategy toward suppressing aeroelastic vibration in the presence of parameter uncertainties and triangular, sinusoidal, and graded gust loads.

PD-Type ILC Algorithm Research with Forgetting Factor for a Class of Linear Systems with Multiple Time Delays

2012 ◽  
Vol 220-223 ◽  
pp. 1125-1130 ◽  
Author(s):  
Hong Yang ◽  
Sheng Ming Li
Keyword(s):  
Linear Systems ◽  
Iterative Learning Control ◽  
Time Delays ◽  
Learning Control ◽  
Iterative Learning ◽  
Multiple Time ◽  
Forgetting Factor ◽  
Convergence Conditions ◽  
Multiple Time Delays ◽  
Study Results

Based on iterative learning control (ILC) algorithm with forgetting factor, the thought that the forgetting factor is a function of iteration numbers is proposed in this paper, which has simplified the convergence conditions. And the convergence analysis is given. Then, the study results of this paper are applied to a class of linear systems with multiple time delays and simulation results show that, under the improvements of the convergence conditions and the reasonable choice of forgetting factor function, the PD-type iterative learning control algorithm with forgetting factor applied to the linear systems with multiple time delays in this paper has effectiveness and superiority.

Traffic Congestion Identification and Analysis of Urban Road Network Based on Granular Computing

2014 ◽  
Vol 641-642 ◽  
pp. 916-922 ◽  
Author(s):  
Chen Yao ◽  
Jiu Chun Gu ◽  
Qi Yang
Keyword(s):  
Rough Set ◽  
Traffic Congestion ◽  
Evaluation System ◽  
Road Network ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Quotient Space ◽  
Urban Road Network ◽  
Evaluation Indexes ◽  
Urban Road

This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associating with traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μ is larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.

scholarly journals Chaotic Synchronizing Systems with Zero Time Delay and Free Couple via Iterative Learning Control

2017 ◽  
Author(s):  
Chun-Kai Cheng ◽  
Paul C.-P. Chao
Keyword(s):  
Time Delay ◽  
Iterative Learning Control ◽  
Mathematical Theory ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Control Input ◽  
Feed Forward Control ◽  
Response Systems ◽  
Error Dynamics

This research not only dedicated a less restrictive method of iteration-varying function for a learning control law to design a controller but also synchronize two nonlinear systems with free time-delay. In addition, the mathematical theory of system synchronization has proved rigorously and the theory verified through an example to demonstrate the behavior of each parameter in the theory. The design of a controller using the iterative learning control law is significant for robotic tracking. The controller in this research generates a feed-forward control input using the error dynamics among the drive-response systems. The error dynamics satisfies the Lyapunov function and the combination of output errors, which respectively represented relative estimated differences of the drive-response systems. The iterative learning control rule serves the function of a filter adding previous control error after the end of each iteration. The numerical example of a synchronous system is given a Lorenz system for driving and another with the iterative learning control law for response under different initial condition. The results verify and demonstrate the proposed mathematical theory. The simulation exhibits consistency in the behavior of each parameter to match mathematical theory.

scholarly journals Performance Of Hybrid Learning Control With Input Shaping For Input Tracking And Vibration Suppression Of A Flexible Manipulator

2012 ◽  
Author(s):  
M. Z. Md. Zain ◽  
M. O. Tokhi ◽  
Z. Mohamed
Keyword(s):  
Iterative Learning Control ◽  
Vibration Suppression ◽  
Hybrid Learning ◽  
Learning Control ◽  
Iterative Learning ◽  
Body Motion ◽  
Flexible Manipulator ◽  
Input Shaping ◽  
Control Input ◽  
Control Scheme

Objektif kertas kerja ini ialah untuk mengkaji keberkesanan gabungan pengawal pembelajaran berulang cerdik dan teknik pembentuk masukan bagi penjejakan masukan dan pengurangan getaran pada hujung suatu pengolah fleksibel. Model dinamik sistem tersebut diterbitkan menggunakan kaedah unsur terhingga. Pada permulaan, pengawal kadaran–kebezaan (PD) menggunakan sudut dan halaju hub direka bentuk untuk kawalan pergerakan badan tegar sistem. Kemudian, pengawal pembelajaran berulang dengan algoritma genetik dan pengawal suap hadapan berasaskan teknik pembentuk masukan ditambahkan untuk kawalan getaran sistem. Keputusan simulasi dalam domain masa dan frekuensi diberikan. Keberkesanan pengawal yang direka bentuk ini dikaji berasaskan penjejakan masukan dan kadar pengurangan getaran sistem. Keberkesanan pengawal ini untuk sistem pengolah fleksibel berbagai beban juga dikaji. Kata kunci: Pengolah fleksibel, algoritma genetik, kawalan cerdik, kawalan pembelajaran berulang, pembentukan masukan The objective of the work reported in this paper is to investigate the performance of an intelligent hybrid iterative learning control scheme with input shaping for input tracking and end–point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using finite element method. Initially, a collocated proportional–derivative (PD) controller utilizing hub–angle and hub–velocity feedback is developed for control of rigid–body motion of the system. This is then extended to incorporate iterative learning control with genetic algorithm (GA) to optimize the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in time and frequency domains. The performance of hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied. Key words: Flexible manipulator, genetic algorithms, intelligent control, iterative learning control, input shaping

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