GM-learn: an iterative learning algorithm for CMOS gate matrix layout

1990 ◽  
Vol 137 (4) ◽  
pp. 301 ◽  
Author(s):  
Sao-Jie Chen ◽  
Yu Hen Hu
Keyword(s):  
Learning Algorithm ◽  
Iterative Learning ◽  
Gate Matrix

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

Analysis of an Adaptive Iterative Learning Algorithm for Freeway Ramp Flow Imputation

2011 ◽  
Author(s):  
Ajith Muralidharan ◽  
Roberto Horowitz
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Iterative Learning ◽  
Equivalent Model ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Effective Demand ◽  
Freeway Traffic ◽  
Flow Profiles ◽  
Cell Transmission

We present an adaptive iterative learning based flow imputation algorithm, to estimate missing flow profiles in on ramps and off ramps using a freeway traffic flow model. We use the Link-Node Cell transmission model to describe the traffic state evolution in freeways, with on ramp demand profiles and off ramp split ratios (which are derived from flows) as inputs. The model based imputation algorithm estimates the missing flow profiles that match observed freeway mainline detector data. It is carried out in two steps: (1) adaptive iterative learning of an “effective demand” parameter, which is a function of ramp demands and off ramp flows/ split ratios; (2) estimation of on ramp demands/ off ramp split ratios from the effective demand profile using a linear program. This paper concentrates on the design and analysis of the adaptive iterative learning algorithm. The adaptive iterative learning algorithm is based on a multi-mode (piecewise non-linear) equivalent model of the Link-Node Cell transmission model. The parameter learning update procedure is decentralized, with different update equations depending on the local a-priori state estimate and demand estimate. We present a detailed convergence analysis of our approach and finally demonstrate some examples of its application.

scholarly journals Iterative learning of human partner’s desired trajectory for proactive human–robot collaboration

2020 ◽  
Vol 4 (2) ◽  
pp. 229-242
Author(s):  
Jingkang Xia ◽  
Deqing Huang ◽  
Yanan Li ◽  
Na Qin
Keyword(s):  
Learning Algorithm ◽  
Interaction Force ◽  
Iterative Learning ◽  
Reference Trajectory ◽  
Robot Controller ◽  
Task Period ◽  
Human Partner ◽  
Control Scheme ◽  
Control Objective ◽  
Human Robot Collaboration

Abstract A period-varying iterative learning control scheme is proposed for a robotic manipulator to learn a target trajectory that is planned by a human partner but unknown to the robot, which is a typical scenario in many applications. The proposed method updates the robot’s reference trajectory in an iterative manner to minimize the interaction force applied by the human. Although a repetitive human–robot collaboration task is considered, the task period is subject to uncertainty introduced by the human. To address this issue, a novel learning mechanism is proposed to achieve the control objective. Theoretical analysis is performed to prove the performance of the learning algorithm and robot controller. Selective simulations and experiments on a robotic arm are carried out to show the effectiveness of the proposed method in human–robot collaboration.

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