Design of arbitrary-order robust iterative learning control based on robust control theory

Mechatronics ◽  
2017 ◽  
Vol 47 ◽  
pp. 67-76 ◽  
Author(s):  
Minghui Zheng ◽  
Cong Wang ◽  
Liting Sun ◽  
Masayoshi Tomizuka
Keyword(s):  
Robust Control ◽  
Control Theory ◽  
Iterative Learning Control ◽  
Arbitrary Order ◽  
Learning Control ◽  
Iterative Learning ◽  
Robust Control Theory

Arbitrary-Order Iterative Learning Control Considering H∞ Synthesis

2016 ◽  
Author(s):  
Minghui Zheng ◽  
Cong Wang ◽  
Liting Sun ◽  
Masayoshi Tomizuka
Keyword(s):  
Iterative Learning Control ◽  
Arbitrary Order ◽  
Filter Design ◽  
Feedforward Control ◽  
Design Procedure ◽  
Learning Control ◽  
Iterative Learning ◽  
Infinite Impulse Response ◽  
Scanning System ◽  
Tracking Errors

Iterative learning control (ILC) is an effective technique to improve the tracking performance of systems through adjusting the feedforward control signal based on the memory data. It is critically important to design the learning filters in the ILC algorithm that assures the robust stability of the convergence of tracking errors from one iteration to next. The design procedure usually involves lots of tuning work especially in high-order ILC. To facilitate this procedure, this paper proposes an approach to design learning filters for an arbitrary-order ILC with guaranteed convergence and ease of tuning. The filter design problem is formulated into an H∞ optimal control problem. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. Important characteristics of the proposed approach are explored and demonstrated on a simulated wafer scanning system.

Synthesis of Four-Bar Function Generators by an Iterative Learning Control Procedure

1996 ◽  
Author(s):  
Minh Q. Phan ◽  
Meng-Sang Chew
Keyword(s):  
Control Theory ◽  
Control Problem ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Procedure ◽  
Output Function ◽  
Mechanism Synthesis ◽  
Function Generator ◽  
Cost Functionals

Abstract This paper investigates the applicability of learning control theory to mechanism synthesis via the classical four-bar function generator problem. A function to be generated by a mechanism can be looked upon as a trajectory to be tracked. The parameters that define the mechanism can be thought of as the control inputs. In this sense, the problem of synthesizing a mechanism to generate a particular output function can be treated as a “control” problem. Moreover, it is a learning control problem if the mechanism is synthesized by an iterative process. At each trial or iteration, a learning scheme modifies the mechanism dimensions based on how well it generates the desired function in the previous trial so that the synthesized mechanism approximates the desired output function more and more closely. With this thinking, concepts and tools from learning control theory can be adapted to the mechanism synthesis problem. It will be shown that mechanisms with minimum residual error or minimum structural error can be synthesized by a procedure analogous to that derived for iterative learning control. The starting angles of the input and output links are learned together with the mechanism dimensions. By the use of weighted cost functionals, iterative learning schemes that handle the tradeoff between the emphasis on a certain portion of the output trajectory (e.g., local control) and the mechanism dimensions can be derived in a straight forward manner. Numerical examples are used to illustrate the utility and flexibility of the learning formulation.

Robust Iterative Learning Control for Spatially Interconnected Systems using 2D Control Theory*

2021 ◽  
Author(s):  
Bartlomiej Sulikowski ◽  
Krzysztof Galkowski ◽  
Daniel Trzcinski ◽  
Eric Rogers ◽  
Anton Kummert
Keyword(s):  
Control Theory ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Interconnected Systems ◽  
Spatially Interconnected Systems

The CPG Gait Generate Method of the Quadruped Robot Based on Iterative Learning Control Algorithm

2013 ◽  
Vol 677 ◽  
pp. 296-303 ◽  
Author(s):  
Cheng Wang ◽  
Jun Yao Gao ◽  
Xing Guang Duan ◽  
Yi Liu ◽  
Xin Li ◽  
...  
Keyword(s):  
Control System ◽  
Control Theory ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Quadruped Robot ◽  
Structure Design ◽  
Iterative Learning ◽  
Hydraulic Power ◽  
Prototype Design

Based on the quadruped robot, this paper mainly studies the two directions of the content. The first part mainly introduces the mechanical structure design and the construction of the control system of the quadruped robot, completes the prototype design of the quadruped robot based on hydraulic power system. The second part studies the CPG gait generate method of the quadruped robot based on iterative learning control algorithm. From the principle of bionics, firstly, we use the CPG principle to generate gait, and then use the iterative learning control theory to make the control more optimization.

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