Nonlinear model predictive control of a planar three-link space manipulator

Kayastha, Sharmila; Shi, Lingling; Katupitiya, Jayantha; Pearce, Garth

doi:10.1109/ascc.2017.8287244

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…Redundant robots achieve a versatile motion in the joints space and dexterity in tasks execution [29]. They can find use in robotic surgery as well as on several industrial tasks, because of offering multiple joints configurations that allow the end effector to reach easier the targeted position in the tasks' space [30].…”

Section: Dynamic Model Of the 3-dof Redundant Robotic Manipulatorsmentioning

confidence: 99%

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

Rigatos,

Abbaszadeh,

Pomares

et al. 2024

International Journal of Systems Science

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The control problem for the multivariable and nonlinear dynamics of robotic manipulators and autonomous vehicles is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigid-link robotic manipulator, (ii) control of a 3-DOF autonomous underwater vessel.

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Section: Dynamic Model Of the 3-dof Redundant Robotic Manipulatorsmentioning

confidence: 99%

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

Rigatos,

Abbaszadeh,

Pomares

et al. 2024

International Journal of Systems Science

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“…In [5], a three-link planar manipulator mounted on a freeflying space robot was controlled via a nonlinear Model Predictive Controller (NMPC) in simulation. The prediction horizon was a single time-step and thus computational analysis is meaningless.…”

Section: Related Workmentioning

confidence: 99%

Towards Efficient Learning-Based Model Predictive Control via Feedback Linearization and Gaussian Process Regression

Caldwell

Marshall

2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a learning-based Model Predictive Control (MPC) methodology incorporating nonlinear predictions with robotics applications in mind. In particular, MPC is combined with feedback linearization for computational efficiency and Gaussian Process Regression (GPR) is used to model unknown system dynamics and nonlinearities. In this method, MPC predicts future states by leveraging a GPR model and optimizes a sequence of inputs over feedback linearized states. The controller was tested in simulation by using a twolink planar robot in the presence of model uncertainty. With respect to trajectory-tracking error, the proposed controller outperformed a conventional Proportional-Derivative Inverse Dynamics controller and a GPR-augmented version. Although a fully nonlinear MPC formulation achieved slightly better performance, the proposed controller had an average control calculation time that was 82× faster.

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“…Taking into account Equations (16) and (19) and the dynamics of the second order system, the following is obtained:…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…This control approach was tested on a four-link closed loop planar mechanism lying on the horizontal plane driven by a torque-controlled electric actuator. In [19], a nonlinear model predictive control of a free-flying space robot was developed and the performance of the proposed control was compared with that of a sliding mode control. In [20], a reactive constraint-based control approach was developed for controlling a mobile manipulator so it could reach its goal and avoid unknown and unpredictable obstacles.…”

Section: Introductionmentioning

confidence: 99%

MPC Control and LQ Optimal Control of A Two-Link Robot Arm: A Comparative Study

et al. 2018

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This study examined the control of a planar two-link robot arm. The control approach design was based on the dynamic model of the robot. The mathematical model of the system was nonlinear, and thus a feedback linearization control was first proposed to obtain a linear system for which a model predictive control (MPC) was developed. The MPC control parameters were obtained analytically by minimizing a cost function. In addition, a simulation study was done comparing the proposed MPC control approach, the linear quadratic (LQ) control based on the same feedback linearization, and a control approach proposed in the literature for the same problem. The results showed the efficiency of the proposed method.

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Nonlinear model predictive control of a planar three-link space manipulator

Cited by 15 publications

References 14 publications

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

Towards Efficient Learning-Based Model Predictive Control via Feedback Linearization and Gaussian Process Regression

MPC Control and LQ Optimal Control of A Two-Link Robot Arm: A Comparative Study

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