Contouring Control for Multi‐Axis Motion Systems With Holonomic Constraints: Theory and Application to a Parallel System

Chen, Shyh‐Leh; Tsai, Yuan-Cheng

doi:10.1002/asjc.1171

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…As shown in [20], the closed‐loop system with will be only ultimately bounded (with the bound depending on δ ) if the integral action is not included in the sliding manifold [36]. With integral sliding manifold , the error dynamics will be asymptotically stable.…”

Section: Contouring Controller Designmentioning

confidence: 99%

“…A desired parametric path corresponding to the desired path is used to determine a velocity field for contouring. Chen et al [19–21] reduce equivalent errors that are the analytical expression of a desired path to minimize the contour errors. Although both methods were implemented to the robotic applications, the tested robots are not five or more degrees of freedom (DOF).…”

Section: Introductionmentioning

confidence: 99%

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MPC‐based robust contouring control for a robotic machining system

Kornmaneesang

Chen

2020

Asian Journal of Control

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Recently, robot manipulators have been adopted to perform machining tasks, instead of only automation tasks. The key machining task is to follow a desired contour. Therefore, the machining accuracy can be improved by reducing contour error. However, only a few researches concentrate on solving the contouring problem in the robotic machining system. In this paper, a model predictive control (MPC)‐based contouring control scheme is proposed for a machining system based on a 5‐DOF dual‐arm robot. The contouring control problem is transformed into the regulation problem by using the method of equivalent errors. An MPC algorithm is developed to minimize the contour error by optimizing a sequence of control actions under torque constraints. In addition, robustness is upgraded by integral sliding mode control (ISMC). Experiments are carried out for the proposed methods and two other conventional methods. The results show that the proposed control scheme can achieve much better contouring performance than the conventional methods, indicating the effectiveness of the proposed controller.

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Section: Contouring Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MPC‐based robust contouring control for a robotic machining system

Kornmaneesang

Chen

2020

Asian Journal of Control

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“…It is certainly true that active control law should be proposed to regulate the amplitude vibration for a PDEs [1][2][3][4][5][6][7][8][9]. Over the past decades, numerous investigations on axially moving systems have been studied [10][11][12]. In [13], for the purpose of suppressing vibration of an axially moving string system, by matching through placement of both sensors and actuators in domain, a designed two-sensor control law is proposed for disturbance rejection.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fault‐tolerant control of an axially moving system with time‐varying constraints

Yue

2019

Asian Journal of Control

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In this paper, for an axially moving system, with the purpose of suppressing vibration, an adaptive fault‐tolerant control method with time‐varying constraints is investigated. The dynamics of the system are comprised of an ordinary differential equation coupled with a partial differential equation. The method used in our study is known as fault‐tolerant control to process affairs of actuator failures occur. Actual control substitutes for ideal control to regulate the vibration when the actuator occurs undesired failures. Time‐varying constraints are appropriate to cope with the variation range of the performance. Lyapunov function has proven that the adaptive control law is feasible, as well as verifying the exponential stability of the system. Eventually, simulations we have done suggest that the effectiveness of the designed control is feasible.

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“…4 PID controllers which are the most commonly used in industrial applications, do not need complete dynamic information of the robot. Although, these controllers usually show desirable performance in tracking of plants with linear time-invariant models, however, for robots with nonlinear dynamics, more advanced techniques such as Lyapunov based methods 5,6 or computed torque control (CTC) 7,8 improve the control performance. These methods require complete dynamic model of the robot, while the exact model is unreachable due to uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive back–stepping robust control of a 3–[P2(US)] parallel robot on optimal trajectory

Mazare¹,

Taghizadeh²

2019

IRATJ

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This paper presents a robust control technique combining adaptive sliding mode control and back-stepping methods to control a 3-DOF translational parallel robot. Dynamic modeling of the robot is accomplished using the Lagrange method. Since the derived dynamic model suffers from uncertainties and disturbances, a combination of robust techniques namely back-stepping and sliding mode controllers are applied together with adaptive control. Determination of uncertainty bounds is intransitive in the design of the proposed controller. To prove the system stability, the system model is divided into several subsystems and stability is illustrated step by step using Lyapunov criteria, then an adaptive law is obtained to estimate the uncertainties. This controller is also robust in presence of time-varying disturbances. Performance of the controller is evaluated by simulations on optimal trajectories. Trajectory planning is performed by optimization of some accuracy points using Harmony Search Algorithm (HSA) and cubic spline interpolation. Simulation results show improved performance of the proposed control technique compared to conventional feedback linearization and sliding mode techniques.

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Contouring Control for Multi‐Axis Motion Systems With Holonomic Constraints: Theory and Application to a Parallel System

Cited by 8 publications

References 27 publications

MPC‐based robust contouring control for a robotic machining system

MPC‐based robust contouring control for a robotic machining system

Adaptive fault‐tolerant control of an axially moving system with time‐varying constraints

Adaptive back–stepping robust control of a 3–[P2(US)] parallel robot on optimal trajectory

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