TY - JOUR
T1 - Two adaptive control strategies for trajectory tracking of the inertia wheel pendulum
T2 - neural networks vis à vis model regressor*
AU - Moreno-Valenzuela, Javier
AU - Aguilar-Avelar, Carlos
AU - Puga-Guzmán, Sergio
AU - Santibáñez, Víctor
N1 - Publisher Copyright:
© 2016 TSI® Press.
PY - 2017/1/2
Y1 - 2017/1/2
N2 - The problem addressed in this paper is to achieve robust motion control of the inertia wheel pendulum (IWP). Specifically, trajectory tracking control of the pendulum of the IWP under the assumption of uncertain model is discussed. Two new robust algorithms are introduced whose design departs from a model-based input-output linearization controller. Then, the control problem is firstly solved by means of an adaptive neural network-based controller and secondly by an adaptive regressor-based controller. For both controllers, rigorous analysis of the respective closed-loop system is given, where Barbalat’s lemma is used to conclude asymptotic convergence of the pendulum tracking error. In addition, the wheel velocity and adaptive extension signals are shown to be bounded. An extensive real-time experimental study validates the introduced theory, where the performance of a classical linear PID controller and the two new adaptive schemes are compared.
AB - The problem addressed in this paper is to achieve robust motion control of the inertia wheel pendulum (IWP). Specifically, trajectory tracking control of the pendulum of the IWP under the assumption of uncertain model is discussed. Two new robust algorithms are introduced whose design departs from a model-based input-output linearization controller. Then, the control problem is firstly solved by means of an adaptive neural network-based controller and secondly by an adaptive regressor-based controller. For both controllers, rigorous analysis of the respective closed-loop system is given, where Barbalat’s lemma is used to conclude asymptotic convergence of the pendulum tracking error. In addition, the wheel velocity and adaptive extension signals are shown to be bounded. An extensive real-time experimental study validates the introduced theory, where the performance of a classical linear PID controller and the two new adaptive schemes are compared.
KW - Adaptive control
KW - Inertia wheel pendulum
KW - Model regressor
KW - Motion control
KW - Neural networks
KW - Real-time experiments
UR - http://www.scopus.com/inward/record.url?scp=84954217646&partnerID=8YFLogxK
U2 - 10.1080/10798587.2015.1121618
DO - 10.1080/10798587.2015.1121618
M3 - Artículo
SN - 1079-8587
VL - 23
SP - 63
EP - 73
JO - Intelligent Automation and Soft Computing
JF - Intelligent Automation and Soft Computing
IS - 1
ER -