Motion Control of Underactuated Mechanical Systems

Moreno–Valenzuela, Javier; Aguilar-Avelar, Carlos

doi:10.1007/978-3-319-58319-8

Cited by 70 publications

(36 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Reference [15] has proposed a book for controlling underactuated mechanical systems. The authors start by describing and formalizing a MATLAB-based identification procedure of two underactuated mechanical systems [16]: the Furuta pendulum and the inertia wheel pendulum.…”

Section: Introductionmentioning

confidence: 99%

A Review on the Control of Second Order Underactuated Mechanical Systems

2018

View full text Add to dashboard Cite

This paper describes some important classes of two degrees of freedom of underactuated mechanical system and also surveys review of the recent state-of-the-art concerning the mathematical modeling of these systems, their classification, and all the control strategies (linear, nonlinear, and intelligent) that have been made so far (i.e., from the year 2000 to date) to control these systems. Future research and challenges concerning the improvement, the effectiveness, and robustness of the proposed controllers for underactuated mechanical systems are presented.

show abstract

Section: Introductionmentioning

confidence: 99%

A Review on the Control of Second Order Underactuated Mechanical Systems

2018

View full text Add to dashboard Cite

show abstract

“…To obtain a precise model of system (1), the weight coefficient vectorŵ should be updated over and over again [50,51], least-mean-square (LMS) algorithm [52], etc. The gradient method is commonly adopted for parameter adjustment; that is,ŵ can be updated once in the negative gradient direction after each offline learning.…”

Section: Offline System Identification the Identification Error Can mentioning

confidence: 99%

MTN Optimal Tracking Control of SISO Nonlinear Time-Varying Discrete-Time Systems without Mechanism Models

Zhang

Yan

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Nonlinear time-varying systems without mechanism models are common in application. They cannot be controlled directly by the traditional control methods based on precise mathematical models. Intelligent control is unsuitable for real-time control due to its computation complexity. For that sake, a multidimensional Taylor network (MTN) based output tracking control scheme, which consists of two MTNs, one as an identifier and the other as a controller, is proposed for SISO nonlinear time-varying discrete-time systems with no mechanism models. A MTN identifier is constructed to build the offline model of the system, and a set of initial parameters for online learning of the identifier is obtained. Then, an ideal output signal is selected relative to the given reference signal. Based on the system identification model, Pontryagin minimum principle is introduced to obtain the numerical solution of the optimal control law for the system relative to the given ideal output signal, with the corresponding optimal output taken as the desired output signal. A MTN controller is generated automatically to fit the numerical solution of the optimal control law using the conjugate gradient (CG) method, and a set of initial parameters for online learning of the controller is obtained. An adaptive back propagation (BP) algorithm is developed to adjust the parameters of the identifier and controller in real time, and the convergence for the proposed learning algorithm is verified. Simulation results show that the proposed scheme is valid.

show abstract

“…In fact, the inequalities (38) and (39) provide an explicit tuning guideline for the control gains D 1 and D 2 . Further details about the local analysis of the system (36) can be found in Moreno-Valenzuela et al 41,42 Experimental results…”

Section: Adaptive Neural Network Controllermentioning

confidence: 99%

Tracking of periodic oscillations in an underactuated system via adaptive neural networks

Puga-Guzmán

Aguilar-Avelar

Moreno–Valenzuela

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

Self Cite

View full text Add to dashboard Cite

In this paper, the tracking control of periodic oscillations in an underactuated mechanical system is discussed. The proposed scheme is derived from the feedback linearization control technique and adaptive neural networks are used to estimate the unknown dynamics and to compensate uncertainties. The proposed neural network-based controller is applied to the Furuta pendulum, which is a nonlinear and nonminimum phase underactuated mechanical system with two degrees of freedom. The new neural network-based controller is experimentally compared with respect to its modelbased version. Results indicated that the proposed neural algorithm performs better than the model-based controller, showing that the real-time adaptation of the neural network weights successfully estimates the unknown dynamics and compensates uncertainties in the experimental platform.

show abstract

Motion Control of Underactuated Mechanical Systems

Cited by 70 publications

References 0 publications

A Review on the Control of Second Order Underactuated Mechanical Systems

A Review on the Control of Second Order Underactuated Mechanical Systems

MTN Optimal Tracking Control of SISO Nonlinear Time-Varying Discrete-Time Systems without Mechanism Models

Tracking of periodic oscillations in an underactuated system via adaptive neural networks

Contact Info

Product

Resources

About