A <scp>matlab</scp>-based identification procedure applied to a two-degrees-of-freedom robot manipulator for engineering students

Moreno–Valenzuela, Javier; Miranda-Colorado, Roger; Aguilar-Avelar, Carlos

doi:10.1177/0020720916689102

Cited by 18 publications

(34 citation statements)

References 27 publications

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“…On the other hand, the off-line LS algorithm (46) was implemented using w = 30 in filter (43). Besides, for the off-line LS algorithm [45], the position quantization error was reduced by filtering position measurements through a low-pass non-causal zero-phase digital filter, implemented in Matlab using the filtfilt function, with a cut-off frequency of 0.007 radians. The velocity estimates, required to implement Y f in (44), were computed off-line without phase-shift, using a central difference algorithm, with the low-pass filtered position x 1f obtained by filtering x 1 with the filtfilt function [47,48].…”

Section: Resultsmentioning

confidence: 99%

“…The proposed parameter identification algorithm was experimentally applied to a DC servomechanism with a pendular load. For comparison purposes, an on‐line LSFF algorithm and an off‐line LS algorithm were also applied to the servo. The description of the LS algorithms is given in Appendix VIII.…”

Section: Resultsmentioning

confidence: 99%

“…The procedure reported in [45] is revisited here. In order to apply the off-line LS algorithm, let us rewrite the model (32) in the next form…”

Section: Procedures To Implement the Off-line Ls Algorithmmentioning

confidence: 99%

“…The procedure reported in is revisited here. In order to apply the off‐line LS algorithm, let us rewrite the model in the next form

\frac{1}{b} {\overset{x}{true}}_{2} + \frac{a}{b} x_{2} + \frac{c}{b} \sin (x_{1}) - \frac{β}{b} = u .

The previous description satisfies the property of linearity in the parameters , and allows to express as follows

Y (x_{1}, x_{2}, {\overset{x}{true}}_{2}) {bold-italicθ}_{L S, o f f} = u,

where

\begin{matrix} Y (x_{1}, x_{2}, {\overset{x}{true}}_{2}) & = ([], x_{2} {\overset{x}{true}}_{2} \sin (x_{1}) - 1), \\ {bold-italicθ}_{L S, o f f} & = {([], \frac{a}{b} \frac{1}{b} \frac{c}{b} \frac{β}{b})}^{T}, \end{matrix}

with

Y (\cdot) \in {double-struckR}^{1 \times 4}

being the regression matrix and

{bold-italicθ}_{L S, o f f} \in {double-struckR}^{4}

the parameter vector.…”

Section: On‐line and Off‐line Ls Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

Miranda-Colorado

Moreno–Valenzuela

2017

Asian Journal of Control

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This paper presents a methodology for on-line closed-loop identification of a class of nonlinear servomechanisms. First, a system is defined with the same structure as the actual servomechanism, but using time-varying estimated parameters. No coupling between the actual and the estimation systems is present. Position, velocity and acceleration errors, defined as the difference of the actual respective signals and the signals coming from the estimation system, are required in the identification method. Then, a recursive algorithm for on-line identification of the system parameters is derived from a cost function depending on a linear combination of all the estimation errors. Velocity and acceleration estimates, required in the proposed parameter identification algorithm, are obtained by using an algebraic methodology. The identification algorithm is compared by means of real-time experiments with an on-line least squares algorithm with forgetting factor and an off-line least squares algorithm with data preprocessing. Experimental results show that the proposed approach has a performance comparable to that obtained with the off-line least squares algorithm, but with the advantage of avoiding any preprocessing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The procedure reported in [45] is revisited here. In order to apply the off-line LS algorithm, let us rewrite the model (32) in the next form…”

Section: Procedures To Implement the Off-line Ls Algorithmmentioning

confidence: 99%

“…The procedure reported in is revisited here. In order to apply the off‐line LS algorithm, let us rewrite the model in the next form

\frac{1}{b} {\overset{x}{true}}_{2} + \frac{a}{b} x_{2} + \frac{c}{b} \sin (x_{1}) - \frac{β}{b} = u .

The previous description satisfies the property of linearity in the parameters , and allows to express as follows

Y (x_{1}, x_{2}, {\overset{x}{true}}_{2}) {bold-italicθ}_{L S, o f f} = u,

where

\begin{matrix} Y (x_{1}, x_{2}, {\overset{x}{true}}_{2}) & = ([], x_{2} {\overset{x}{true}}_{2} \sin (x_{1}) - 1), \\ {bold-italicθ}_{L S, o f f} & = {([], \frac{a}{b} \frac{1}{b} \frac{c}{b} \frac{β}{b})}^{T}, \end{matrix}

with

Y (\cdot) \in {double-struckR}^{1 \times 4}

being the regression matrix and

{bold-italicθ}_{L S, o f f} \in {double-struckR}^{4}

the parameter vector.…”

Section: On‐line and Off‐line Ls Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

Miranda-Colorado

Moreno–Valenzuela

2017

Asian Journal of Control

Self Cite

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show abstract

“…A physical robotic laboratory contains a robotic structure, which is specifically designed for the purpose of education. The structure of an educational robot can be as simple as a single-link robot 1 or can be in the form of a more complex structure such as a mobile robot, 2 a flexible arm, 3 a manipulator, 4 etc. Each robotic structure requires a mechanical structure, a set of sensors and actuators, and a processing unit, which is provided either by a computer 5 or by an embedded platform.…”

Section: Introductionmentioning

confidence: 99%

A MATLAB-based virtual robotics laboratory: Demonstrated by a two-wheeled inverted pendulum

Ghayoor

2018

The International Journal of Electrical Engineering & Educa

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In this paper, a procedure for designing a MATLAB-based virtual robotics laboratory is described that provides a realistic three-dimensional animation for the robot’s motion. The proposed laboratory allows students to obtain the linear model and stability condition of a robotic system, without getting involved in its mathematical modeling. The structure of a two-wheeled inverted pendulum robot has been used for demonstrating the laboratory’s features. As part of this virtual experiment, design and evaluation of proportional–integral–derivative, model predictive control, and fuzzy controllers for maintaining the two-wheeled inverted pendulum’s balance are considered. A set of graphical–user interfaces is designed for providing the interaction with the system. This virtual lab is readily extensible for other robotics structures and controllers, and its output data is promptly available for further analysis by other MATLAB toolboxes.

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Construction of a competition practice system for robotics engineering based on a competency model

Zhang

Meng

et al. 2022

Comp Applic In Engineering

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Competition practice is of great significance for improving the engineering ability of undergraduates majoring in robotics engineering, and it occupies an important position in practical teaching systems. However, most existing competitions are spontaneously participated by students, and problems, such as poor systemicity, unclear ability‐training goals, and lack of an effective evaluation system, occur, resulting in limited improvement in students' ability. In this paper, a competency model for robotics engineering was proposed. It includes four dimensions: knowledge, skills, dispositions, and evaluation. The first three dimensions comprise multiple secondary and tertiary indices. On this basis, a competition practice system was established. The system includes four practical course groups and a final competition, and the competency model runs through before, during, and after practice. The competition practice system based on the competency model was applied to two groups of students. The analysis results show that students have a clear understanding of their own ability, and their comprehensive ability has been improved. This finding provides a reference for the construction of practice systems for other engineering majors.

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A matlab-based identification procedure applied to a two-degrees-of-freedom robot manipulator for engineering students

Cited by 18 publications

References 27 publications

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

A MATLAB-based virtual robotics laboratory: Demonstrated by a two-wheeled inverted pendulum

Construction of a competition practice system for robotics engineering based on a competency model

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