Self-Reconfigurable Control System for Autonomous Vehicles

2013 ◽  
Author(s):  
Rahmat A. Shoureshi ◽  
Sunwook Lim ◽  
Christopher M. Aasted
Keyword(s):  
Control Systems ◽  
Autonomous Vehicles ◽  
Iterative Learning Control ◽  
Design Procedure ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Operating Characteristics ◽  
Reconfigurable Control ◽  
Safety And Reliability

This paper presents a reconfigurable control design technique that integrates a robust feedback and an iterative learning control (ILC) scheme. This technique is applied to develop vehicle control systems that are tolerant to failures due to malfunctions or damages. The design procedure includes solving the robust performance condition for a feedback controller through the use of μ-synthesis that also satisfies the convergence condition for the iterative learning control rule. The effectiveness of the proposed approach is verified by simulation experiments using a radio-controlled (R/C) model airplane. The methods presented in this paper can be applied to design of global intelligent control systems to improve the operating characteristics of a vehicle and increase safety and reliability.

Self-learning control systems using identification-based adaptive iterative learning controller

2008 ◽  
Vol 222 (7) ◽  
pp. 1177-1187 ◽  
Author(s):  
S Ashraf ◽  
E Muhammad ◽  
A Al-Habaibeh
Keyword(s):  
Control Systems ◽  
Design Procedure ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Control Laws ◽  
Performance Requirements ◽  
Novel Approach ◽  
Identification Techniques ◽  
Self Learning

One of the promising algorithms for self-learning control systems is iterative learning control (ILC), which is an algorithm capable of tracking a desired trajectory within a specific period of time. Conventional ILC algorithms have the problem of relatively slow convergence rate and because of their fixed control laws they are unable to adapt to changes in performance requirements and system changes. This paper suggests a novel approach by combining system identification techniques with the proposed ILC approach to overcome such problems. Several practical simulation examples are presented to illustrate the design procedure and to confirm the effectiveness and robustness of the algorithm. The optimal gain matrices values are calculated using the steepest descent approach. Convergence condition for the approach is also derived. Declining cost and increasing power of computers and embedded systems makes the implementation of such schemes highly feasible.

Eliminating Structural Error in Real-Time Adjustable Four-Bar Path Generators Using Iterative Learning Control

2004 ◽  
Author(s):  
Hong-Jen Chen ◽  
Richard W. Longman ◽  
Meng-Sang Chew
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Path Generation ◽  
Structural Error ◽  
Four Bar Linkage

Fundamental concepts of Iterative Learning Control (ILC) are applied to path generating problems in mechanisms. As an illustration to such class of problems, an adjustable four-bar linkage is used. The coupler point of a four-bar traces a coupler curve that will in general deviate from the desired coupler path. Except at the precision points, the coupler curve will exhibit some structural error, which is the deviation from the specified curve. The structural error will repeat itself every cycle at exactly the same points over the range of interest. Since ILC is a methodology that was developed to handle similar repetitive errors in control systems, it is believed that it will be well served to apply it to this class of problems. Results show that ILC can be simple to implement, and it is found to be very well suited for such path generation problems.

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