Modeling and Simulation of a Point to Point Spherical Articulated Manipulator Using Optimal Control

Saraf, Prathamesh; Ponnalagu, R N

doi:10.1109/icara51699.2021.9376496

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…And also tuning PID parameters all the time is not a good practice. Therefore, they extended the LQR technique to 3-link articulated robotic manipulators to provide good stability in [4]. A LQG optimal controller for a flexible joint 2-DOF robot was reported to achieve robustness even in the presence of uncertainties.…”

Section: Figure1: 3dof Robotic Manipulatormentioning

confidence: 99%

“…Where M(θ i ) is the inertia matrix of order 3 × 3, V(θ i , 𝜃 ̇𝑖) are the Coriolis/centripetal matrix and the order of 3 × 1 G(θ i ) is the gravity vector of order 3 × 1 the 3 DOF articulated robotic manipulator [8].…”

Section: Lagrange Euler Formulationmentioning

confidence: 99%

“…LQR technique was extended to a 3-link articulated robotic manipulator for enhanced stability. It was observed from the existing literature that the LQR optimal control is well suitable for the control of industrial robotic manipulators in [8]. This type of controller was able to provide a good measure of robustness against disturbances and was able to achieve good tracking performance.…”

Section: Figure1: 3dof Robotic Manipulatormentioning

confidence: 99%

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Adaptive Lqr-based Control of 3 Dof Spherical Articulated Robotic Manipulator

Suryanarayana

2024

Preprint

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In this work, an Adaptive Linear Quadratic Regulator (LQR) is proposed for the tracking control of a 3 DOF articulated robotic manipulator. The main objective of this work is to drive the robotic manipulator to its equilibrium point from any initial position of the end effector of a robotic manipulator in an adaptive way even if the system dynamics consist of modelling uncertainties. The conventional LQR is able to provide a good measure of robustness against disturbances and achieve good tracking performance. However, it will give degraded tracking performance in case of the presence of model uncertainties. Therefore LQR tracking performance is enhanced by using a Model Reference Adaptive Controller (MRAC) in the literature for enabling a 2 DOF system to achieve good tracking against modelling uncertainties. But it has not been applied for a 3 DOF system till date. Therefore in this work, it is proposed to be applied for such a system for the first time to realize an adaptive control. The forward and inverse kinematics level equations are formulated using an indirect form of the Denavit Hartenberg (DH) convention. The dynamics of robotic manipulator are derived using the Euler-Lagrange formulation. The dynamics of manipulator with the LQR control are considered to be the reference dynamics for the MRAC control and the modelling uncertainty will be added to the system dynamics to test its performance. The MRAC augmented LQR provides faster convergence with improved stability against modelling uncertainties.

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Section: Figure1: 3dof Robotic Manipulatormentioning

confidence: 99%

Section: Lagrange Euler Formulationmentioning

confidence: 99%

Section: Figure1: 3dof Robotic Manipulatormentioning

confidence: 99%

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Adaptive Lqr-based Control of 3 Dof Spherical Articulated Robotic Manipulator

Suryanarayana

2024

Preprint

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“…The kinematic model design starts from the basic step of calculating the DenavitHartenberg (DH) parameters for each leg of the robot. The DH table helps understand the orientation of each link with respect to the previous one [7]. Table I describes the DH parameters for Spot's leg configuration.…”

Section: Kinematic Modelmentioning

confidence: 99%

Terrain Adaptive Gait Transitioning for a Quadruped Robot using Model Predictive Control

Saraf

Sarkar

Javed

2021

2021 26th International Conference on Automation and Computing (ICAC)

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Legged robots can traverse challenging terrain, use perception to plan their safe foothold positions, and navigate the environment. Such unique mobility capabilities make these platforms a perfect candidate for scenarios such as search and rescue, inspection, and exploration tasks. While traversing through such terrains, the robot's instability is a significant concern. Many times the robot needs to switch gaits depending on its environment. Due to the complex dynamics of quadruped robots, classical PID control fails to provide high stability. Thus, there is a need for advanced control methods like the Model Predictive Control (MPC) which uses the system model and the nature of the terrain in order to predict the stable body pose of the robot. The controller also provides correction to any external disturbances that result in a change in the desired behavior of the robot. The MPC controller is designed in MATLAB, for full body torque control. The controller performance was verified on Boston Dynamics Spot in Webots simulator. The robot is able to provide correction for external perturbations up to 150 N and also resist falls till 80 cm.

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