Modeling, simulation, and construction of a furuta pendulum test-bed

Antonio-Cruz, Mayra; Silva‐Ortigoza, Ramón; Sandoval-Gutierrez, Jacobo; Merlo-Zapata, Carlos Alejandro; Taud, Hind; Márquez-Sánchez, Celso; Hernández-Guzmán, Victor Manuel

doi:10.1109/conielecomp.2015.7086928

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…where L is the Lagrange relation obtained by combining both energy equations, K arm and k p are the kinetic energy equations of the arm and the pendulum respectively, meanwhile V arm and V p are their potential energy equations. After the kinetic and potential energies analysis, as explained in [18] and also validated by [11], the expansion of (1) leads to second order differential equations; the first one, is the differential equation of the motion of the pendulum in terms of the angle generated between the horizontal plane and the pendulum's arm (θ arm ). Meanwhile, the second one is the differential equation obtained from the motion of the system, in terms of the pendulum and the angle between the vertical axis and the pendulum itself (θ p ).…”

Section: System Modellingmentioning

confidence: 99%

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ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

Méndez¹,

Baltazar-Reyes²,

Macias³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Nowadays, process automation and smart systems have gained increasing importance in a wide variety of sectors, and robotics have a fundamental role in it. Therefore, it has attracted greater research interests; among them, Underactuated Mechanical Systems (UMS) have been the subject of many studies, due to their application capabilities in different disciplines. Nevertheless, control of UMS is remarkably more difficult compared to other mechanical systems, owing to their non-linearities caused by the presence of fewer independent control actuators with respect to the degrees of freedom of the mechanism (which characterizes the UMS). Among them, the Furuta Pendulum has been frequently listed as an ideal showcase for different controller models, controlled often through non-lineal controllers like Sliding-Mode and Model Reference Adaptive controllers (SMC and MRAC respectively). In the case of SMC the chattering is the price to be paid, meanwhile issues regarding the coupling between control and the adaptation loops are the main drawbacks for MRAC approaches; coupled with the obvious complexity of implementation of both controllers. Hence, recovering the best features of the MRAC, an Artificial Neural Network (ANN) is implemented in this work, in order to take advantage of their classification capabilities for non-linear systems, their low computational cost and therefore, their suitability for simple implementations. The proposal in this work, shows an improved behavior for the stabilization of the system in the upright position, compared to a typical MRAC-PID structure, managing to keep the pendulum in the desired position with reduced oscillations. This work, is oriented to the real implementation of the embedded controller system for the Furuta pendulum, through a Microcontroller Unit (MCU). Results in this work, shows an average 58.39% improvement regarding the error through time and the effort from the controller.

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Section: System Modellingmentioning

confidence: 99%

“…where the estimation of f (x) can be expressed as the product of the input values with the networks weights, as seen in (18).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

Méndez¹,

Baltazar-Reyes²,

Macias³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…Most of the authors dealing with this problem mentioned the high complexity of the model, especially the friction effects arising from the wide range of velocities the links go through. The parameters representing the dry and viscous frictions in the joints are one of the most difficult parameters to identify [8], [14]- [20]. Indeed, there is no universal model to represent damping, and the literature contains several models that attempt to capture the physics of this complex phenomenon [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer

Wadi,

Mukhopadhyay,

Romdhane

2022

Preprint

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This paper proposes a friction model parameter identification routine that can work with highly nonlinear and chaotic systems. The chosen system for this study is a passivelyactuated tilted Furuta pendulum, which is known to have a highly non linear and coupled model. The pendulum is tilted to ensure the existence of a stable equilibrium configuration for all its degrees of freedom, and the link weights are the only external forces applied to the system. A nonlinear analytical model of the pendulum is derived, and a continuous friction model considering static friction, dynamic friction, viscous friction, and the stribeck effect is selected from the literature. A high-gain Universal Adaptive Stabilizer (UAS) observer is designed to identify friction model parameters using joint angle measurements. The methodology is tested in simulation and validated on an experimental setup. Despite the high nonlinearity of the system, the methodology is proven to converge to the exact parameter values, in simulation, and to yield qualitative parameter magnitudes in experiments where the goodness of fit was around 85% on average. The discrepancy between the simulation and the experimental results is attributed to the limitations of the friction model. The main advantage of the proposed method is the significant reduction in computational needs and the time required relative to conventional optimizationbased identification routines. The proposed approach yielded more than 99% reduction in the estimation time while being considerably more accurate than the optimization approach in every test performed. One more advantage is that the approach can be easily adapted to fit other models to experimental data.

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“…The authors of [1] and [7] proposed a linearized model based on a small angle assumption. Others neglected one or more of the cross coupling terms relating the two rotations [1], [3], [6], [8], [9]. The authors of [7] showed, through simulations, that the aforementioned assumption is not acceptable and it could have an important effect on the dynamic response.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [7] showed, through simulations, that the aforementioned assumption is not acceptable and it could have an important effect on the dynamic response. The authors of [8] derived a significantly simplified system and carried a brief three-second test. All the experimental works on the Furuta pendulum are directed at testing various control strategies [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer

2022

View full text Add to dashboard Cite

This paper proposes a friction model parameter identification routine that can work with highly nonlinear and chaotic systems. The chosen system for this study is a passively-actuated tilted Furuta pendulum, which is known to have a highly nonlinear and coupled model. The pendulum is tilted to ensure the existence of a stable equilibrium configuration for all its degrees of freedom, and the link weights are the only external forces applied to the system. A nonlinear analytical model of the pendulum is derived, and a continuous friction model considering static friction, dynamic friction, viscous friction, and the stribeck effect is selected from the literature. A high-gain Universal Adaptive Stabilizer (UAS) observer is designed to identify friction model parameters using joint angle measurements. The methodology is tested in simulation and validated on an experimental setup. Despite the high nonlinearity of the system, the methodology is proven to converge to the exact parameter values, in simulation, and to yield qualitative parameter magnitudes in experiments where the goodness of fit was around 85% on average. The discrepancy between the simulation and the experimental results is attributed to the limitations of the friction model. The main advantage of the proposed method is the significant reduction in computational needs and the time required relative to conventional optimization-based identification routines. The proposed approach yielded more than 99% reduction in the estimation time while being considerably more accurate than the optimization approach in every test performed. One more advantage is that the approach can be easily adapted to fit other models to experimental data.

show abstract

Modeling, simulation, and construction of a furuta pendulum test-bed

Cited by 10 publications

References 16 publications

ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization

Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer

Identifying Friction in a Nonlinear Chaotic System Using a Universal Adaptive Stabilizer

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