Constrained discrete model predictive control of an arm‐manipulator using Laguerre function

Pinheiro, Tarcisio Carlos F.; Silveira, Antonio S.

doi:10.1002/oca.2667

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…Model predictive control (MPC) is a strategy to achieve control effects through repeated optimization and feedback correction of the predictive model, which has good robustness and control effects [28,29] . Therefore, the predictive control method of the manipulator model is proposed, which regards the dynamic feature points of the assembly line lettuce as the solution and ensures the real-time dynamic tracking of the manipulator.…”

Section: Model Building and Mpc Controller Designmentioning

confidence: 99%

Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm

Zhang,

Lu,

Jian

et al. 2023

International Journal of Agricultural and Biological Engineering

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The existing steering device in the fruit and vegetable packaging assembly line cannot adjust the attitude of lettuce to a unified attitude, affecting the input and packaging process of the packaging machine. This study proposes an intelligent assembly line sorting method based on the visual positioning and model predictive control of a robotic arm. First, lightweight improvement based on the YOLOv5 is realized, the lettuce stalk in the background of the conveyor belt is promptly identified, the image of the lettuce stalk in the anchor box area is processed, and the edge contour point set is determined to extract the pixel coordinates of the optimal grasp point and mirror inclination angle of the lettuce. For the intelligent assembly line system, a robot arm kinematics model is constructed and the robot kinematics inverse solutions are calculated. Additionally, the lettuce movement speeds are dynamically measured by the vision system. A combination of the model prediction control, dynamic tracking, and rapid sorting of the lettuce by the robot claw is realized. The results show that the average detection time of a single frame image in the visual positioning part is 0.014 s, which is reduced by 50%; the accuracy and recall are 98% and 95%, respectively. The detection time is significantly reduced by ensuring accuracy. Within the current speed range of the packaging assembly line conveyor belt, the manipulator can grasp lettuce at different speeds stably and fast; the average axial error, average radial error, and adjusted average inclination angle error are 0.71 cm, 1.02 cm, and 3.79°, respectively, verifying the high efficiency and stability of the model. The proposed method of this study enables application in the intelligent sorting operation of industrial assembly lines

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Section: Model Building and Mpc Controller Designmentioning

confidence: 99%

Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm

Zhang,

Lu,

Jian

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

“…Furthermore, since the exponential decay factors are included in the Laguerre network, the increments of control signals expressed by Laguerre functions will converge to zero. Recently, the application of Laguerre function-based MPC (Lag-MPC) have received significant attention in various areas, e.g., permanent magnet synchronous machine [21], stratospheric airship trajectory tracking [22], non-minimal state space model [18], vehicle automation [23], [24], and autonomous underwater vehicle [25].…”

Section: Introductionmentioning

confidence: 99%

Robust Nonlinear Model Predictive Control for Ship Dynamic Positioning Using Laguerre Function

et al. 2022

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This paper is devoted to the issue of computationally efficient and robust nonlinear model predictive control (NMPC) for ship dynamic positioning (DP) systems subjected to input constraints and unknown environmental disturbances. The Laguerre functions, typically applied to the linear systems, are introduced to the constrained NMPC design of the nonlinear DP system to reduce the computational burden. The unscented Kalman filter is adopted to estimate the unknown disturbances and states; thus, the disturbance estimates are utilized as the cancellation signal to achieve robust offset-free control. Simulations of the proposed Laguerre function-based NMPC scheme are implemented and compared with the performance of typical Laguerre function-based linear model predictive control (LMPC) for the DP system. Simulation results well demonstrate the effectiveness, robustness and superiority of the proposed controller.INDEX TERMS Laguerre functions, nonlinear model predictive control, input constraints, ship dynamic positioning

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“…However, because it requires high computational power, it was limited to industrial applications that are considered slow dynamic systems, such as chemical factories. However, with recent massive technological improvements in the capabilities and speed of controllers and power electronics, MPC has received more attention as a useful tool for a wider range of applications [26,27].…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Model Predictive Controller Using Laguerre Functions for Active Flutter Suppression of a 2D wing with a Flap

2022

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In this paper, a discrete-time model predictive controller using Laguerre orthonormal function-based (LMPC) for active flutter suppression of a two-dimensional wing with a flap is presented. In this work, a linear mathematical state-space model for the pitch, plunge, and flap degrees of freedom under unsteady aerodynamics is derived and used to determine the linear flutter velocity and frequency of the parameters of a selected experimental wing. To verify the model, the open-loop simulation results are compared to an experimental study using the same wing from the literature. The state-space system is then discretized and LMPC with a Kalman filter is designed and tuned using the MATLAB® simulation environment at a selected speed in the linear flutter region. The predictive control advantage of dealing with input constraints in a systematic manner is explored through a quantitative analysis of the response of both constrained and unconstrained LMPC controllers. The results indicate that theoretically both cases can give excellent performance. However, the input trajectory generated by the unconstrained LMPC is very aggressive in a way that it is considered impractical when compared to the physical limits of an experimental actuator from the literature. The potential of LMPC to achieve a reasonable performance at a significantly lower computational cost compared to the classical model predictive controller (MPC) is investigated by measuring the time required by the same computer to compute the control trajectory for both controllers. The data suggest that LMPC requires remarkably low computational power, which makes it an excellent choice for fast aeroelastic applications.

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Constrained discrete model predictive control of an arm‐manipulator using Laguerre function

Cited by 13 publications

References 29 publications

Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm

Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm

Robust Nonlinear Model Predictive Control for Ship Dynamic Positioning Using Laguerre Function

Discrete-Time Model Predictive Controller Using Laguerre Functions for Active Flutter Suppression of a 2D wing with a Flap

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