An Intelligent Compensation Through B-Spline Neural Network for a Delta Parallel Robot

Escorcia-Hernández, Jonatan Martín; Aguilar-Sierra, Hipolito; Aguilar-Mejía, Omar; Chemori, Ahmed; Arroyo-Nunez, Jose Humberto

doi:10.1109/codit.2019.8820472

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…For the development of the IDM of the delta-like positioning device of SPIDER4, in this study, the simplification hypotheses for delta-like manipulators presented in [26] is considered. It is worth mentioning that these hypotheses also have been considered in previous studies [27], [28], [23]. The considered modeling simplifications are as follows:…”

Section: B Inverse Dynamic Model Of Delta-like Positioning Devicementioning

confidence: 99%

Adaptive RISE Feedback Control for Robotized Machining With PKMs: Design and Real-Time Experiments

Escorcia-Hernández

Chemori

Aguilar-Sierra

2023

IEEE Trans. Contr. Syst. Technol.

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The development of high-precision tasks, such as machining, needs a positioning device for the cutting tool with the smallest possible error. Multiple design factors need to be considered to ensure a mechatronic device successfully performs such tasks. One of these factors may be attributed to the control scheme, which is responsible for controlling the position of the machine. In view of the importance of designing a good control scheme for a robotic system, in this paper, we propose a new extension of the robust integral sign of the error (RISE) for the positioning device a parallel kinematic machine (PKM). This extension consists in including a nominal feedforward term based on the inverse dynamic model of the robot and replacing the RISE fixed feedback gains with adaptive ones. The RISE part of the proposed controller ensures semi-global asymptotic stability. Moreover, it can accommodate sufficiently smooth bounded disturbances. The feedforward part cancels the nonlinearities of the system, improving the tracking performance of the controller. The adaptive feedback gains produce corrective actions when an increase in the tracking errors is due to the contact forces that occur during the machining process. A Lyapunov-based stability analysis is conducted to prove the semi-global asymptotic stability of the proposed control solution. To show its effectiveness realtime experiments are performed for two case studies; the first one is on a free motion trajectory, and the second on milling experiments under three different forward speeds on SPIDER4, a redundantly actuated PKM.

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Section: B Inverse Dynamic Model Of Delta-like Positioning Devicementioning

confidence: 99%

Adaptive RISE Feedback Control for Robotized Machining With PKMs: Design and Real-Time Experiments

Escorcia-Hernández

Chemori

Aguilar-Sierra

2023

IEEE Trans. Contr. Syst. Technol.

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“…This advanced control solution requires only position information for the RBF inputs. In [29], a PD controller with a BSNN feedforward compensation was applied to a DPR to regulate the trajectory tracking for a P&P application, demonstrating that the addition of intelligent compensation terms may reduce the tracking error considerably and might cancel the steady-state error for the PD controller. However, only the error signal was taken into consideration as inputs of the BSNN so that the resulting dynamic approximation was not accurate.…”

Section: State Of the Artmentioning

confidence: 99%

“…However, as it was mentioned before, the BSNN compensation term aims to emulate the Nominal feedforward term. Therefore, in this section, our proposed control solution is compared to the RISE feedforward, being the combination of (27) and (29) to validate the approximation of the dynamics. The case study 1, including the two scenarios, is considered for this validation.…”

Section: Comparison Of Bsnn Compensation Against Nominal Feedforwardmentioning

confidence: 99%

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Escorcia-Hernández

Aguilar-Sierra

Aguilar-Mejía

et al. 2020

J Intell Robot Syst

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In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.

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“…These challenges have led to the development of other techniques centered on servo error estimation and disturbance rejection [9]- [14]. These include methods like changing the PD gains online as a function of servo error estimates [9], disturbance rejection in the feedback loop using linear disturbance observers [10], [11], injecting inputs learned by a neural network to compensate errors that the PD controller does not reject [12], and using synchronization control strategies to reject coupling disturbances in each actuator from the other actuators [13], [14]. Furthermore, other approaches focus on tuning trajectory-dependent PID controller gains offline to minimize errors along a desired path that is known a priori [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Vibration Compensation of Delta 3D Printer with Position-varying Dynamics using Filtered B-Splines

Edoimioya¹,

Chou²,

Okwudire

2022

Preprint

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The delta robot is becoming a popular choice for the mechanical design of fused filament fabrication 3D printers because it can reach higher speeds than traditional serial-axis designs. Like serial 3D printers, delta printers suffer from undesirable vibration at high speeds which degrades the quality of fabricated parts. This undesirable vibration has been suppressed in serial printers using linear model-inversion feedforward control methods like the filtered B-splines (FBS) approach. However, techniques like the FBS approach are computationally challenging to implement on delta 3D printers because of their coupled, position-dependent dynamics. In this paper, we propose a methodology to address the computational bottlenecks by (1) parameterizing the position-dependent portions of the dynamics offline to enable efficient online model generation, (2) computing real-time models at sampled points (instead of every point) along the given trajectory, and (3) employing QR factorization to reduce the number of floating-point arithmetic operations associated with matrix inversion. In simulations, we report a computation time reduction of up to 23x using the proposed method, while maintaining high accuracy, when compared to a controller using the computationally expensive exact LPV model. In experiments, we demonstrate significant quality improvements on parts printed at various positions on the delta 3D printer using our proposed controller compared to a baseline alternative, which uses an LTI model from one position. Through acceleration measurements during printing, we also show that the print quality boost of the proposed controller is due to vibration reductions of more than 20% when compared to the baseline controller.

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An Intelligent Compensation Through B-Spline Neural Network for a Delta Parallel Robot

Cited by 8 publications

References 19 publications

Adaptive RISE Feedback Control for Robotized Machining With PKMs: Design and Real-Time Experiments

Adaptive RISE Feedback Control for Robotized Machining With PKMs: Design and Real-Time Experiments

A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Vibration Compensation of Delta 3D Printer with Position-varying Dynamics using Filtered B-Splines

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