Next Article in Journal
System-Level Testing and Evaluation Plan for Field Robots: A Tutorial with Test Course Layouts
Previous Article in Journal
Virtualization of Robotic Hands Using Mobile Devices
Open AccessFeature PaperArticle

Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems

1
School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
2
Department of Electrical Engineering, College of Energy Engineering, Aswan University, Aswan 81521, Egypt
3
Department of Mechanical Engineering, Faculty of Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
*
Author to whom correspondence should be addressed.
Robotics 2019, 8(4), 82; https://doi.org/10.3390/robotics8040082
Received: 7 July 2019 / Revised: 13 September 2019 / Accepted: 19 September 2019 / Published: 22 September 2019
(This article belongs to the Section Robotics & Automation)
The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical environments with complete or partial model-based control structures, complexity and integrity in discrete-time environments, and scalability in complex coupled dynamical systems. An online adaptive learning mechanism is developed to tackle the above limitations and provide a generalized solution platform for a class of tracking control problems. This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneous linear feedback control strategies. Reinforcement learning approaches based on value iteration processes are adopted to solve the underlying Bellman optimality equations. The resulting control strategies are updated in real time in an interactive manner without requiring any information about the dynamics of the underlying systems. Means of adaptive critics are employed to approximate the optimal solving value functions and the associated control strategies in real time. The proposed adaptive tracking mechanism is illustrated in simulation to control a flexible wing aircraft under uncertain aerodynamic learning environment. View Full-Text
Keywords: adaptive tracking systems; optimal control; machine learning; reinforcement learning; adaptive critics adaptive tracking systems; optimal control; machine learning; reinforcement learning; adaptive critics
Show Figures

Figure 1

MDPI and ACS Style

Abouheaf, M.; Gueaieb, W.; Spinello, D. Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems. Robotics 2019, 8, 82.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop