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Harib et al. Intell Robot 2022;2:37-71 Intelligence & Robotics
DOI: 10.20517/ir.2021.19

Review Open Access

Evolution of adaptive learning for nonlinear dynamic

systems: a systematic survey

1
1
Mouhcine Harib , Hicham Chaoui , Suruz Miah 2
1
Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada.
2
Electrical and Computer Engineering, Bradley University, Peoria, IL 61625, USA.
Correspondence to: Prof. Hicham Chaoui, Department of Electronics, Carleton University, 7066 Minto Building, Ottawa, ON K1S
5B6, Canada. E-mail: Hicham.Chaoui@carleton.ca
How to cite this article: Harib M, Chaoui H, Miah S. Evolution of adaptive learning for nonlinear dynamic systems: a systematic
survey. Intell Robot 2022;2:37-71. https://dx.doi.org/10.20517/ir.2021.19

Received: 19 Dec 2021 First Decision: 20 Jan 2022 Revised: 3 Feb 2022 Accepted: 24 Feb 2022 Published: 16 Mar 2022

Academic Editor: Simon X. Yang Copy Editor: Xi-Jun Chen Production Editor: Xi-Jun Chen

Abstract
The extreme nonlinearity of robotic systems renders the control design step harder. The consideration of adaptive
control in robotic manipulation started in the 1970s. However, in the presence of bounded disturbances, the
limitations of adaptive control rise considerably, which led researchers to exploit some “algorithm modifications”.
Unfortunately, these modifications often require a priori knowledge of bounds on the parameters and the
perturbations and noise. In the 1990s, the field of Artificial Neural Networks was hugely investigated in general, and
for control of dynamical systems in particular. Several types of Neural Networks (NNs) appear to be promising
candidates for control system applications. In robotics, it all boils down to making the actuator perform the desired
action. While purely control-based robots use the system model to define their input-output relations, Artificial
Intelligence (AI)-based robots may or may not use the system model and rather manipulate the robot based on the
experience they have with the system while training or possibly enhance it in real-time as well. In this paper, after
discussing the drawbacks of adaptive control with bounded disturbances and the proposed modifications to
overcome these limitations, we focus on presenting the work that implemented AI in nonlinear dynamical systems
and particularly in robotics. We cite some work that targeted the inverted pendulum control problem using NNs.
Finally, we emphasize the previous research concerning RL and Deep RL-based control problems and their
implementation in robotics manipulation, while highlighting some of their major drawbacks in the field.

Keywords: Adaptive control, deep reinforcement learning, manipulators, neural networks, reinforcement learning,
robotics

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
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