Simultaneous learning and control of parallel Stewart platforms with unknown parameters

Mirza, Mohammed Aquil; Li, Shuai; Jin, Long

doi:10.1016/j.neucom.2017.05.026

Cited by 32 publications

(13 citation statements)

References 54 publications

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“…When the lengths computed by inverse equation (1) in Fig(7), these lengths at each step used to calculate the position and orientation by using the forward equation, which solved numerically in Newton-Raphson method because the forward equation derived from the system of non-linear equations.…”

Section: Fig (7): Output Parameters (Lengths) From Inverse Block and mentioning

confidence: 99%

“…With the advancement in the field of robotic technologies, robotic manipulators are widely used in the applications of factory automation, which are required to carry out continuous and delayed work, such as lifting and transporting radioactive substances and executing the work in a hazardous [1]. At the last time, great attention has been shown in the most paper in the field of the parallel robot.…”

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confidence: 99%

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Kinematics Simulation of Gough-Stewart Parallel Manipulator by Using Simulink Package in Matlab Software

Alwan

Sarhan

2019

JUBES

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The Gough Stewart Robotic manipulator is a parallel manipulator with six-degree of freedom, which has six equations of Kinematics (Inverse and forward), with six variables (Lengths, Position, and Orientation). In this work derived the inverse equations, which used to compute the lengths of the linkages and its changes depended on the position and orientation of the platform's center, then derived the forward equations to calculate the position and orientation of the moving platform in terms of the lengths. This theoretical model of the kinematics analysis of the Gough Stewart has been built into the Simulink package in Matlab to obtain the lengths, position, and orientation for the manipulator at any time of motion. The input parameters (Position and Orientation) in inverse blocks compared with the output parameters (Position and Orientation) in the forward blocks, which show good results.

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Section: Fig (7): Output Parameters (Lengths) From Inverse Block and mentioning

confidence: 99%

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confidence: 99%

Kinematics Simulation of Gough-Stewart Parallel Manipulator by Using Simulink Package in Matlab Software

Alwan

Sarhan

2019

JUBES

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“…Compared with serial robot, the parallel robot is a closed-loop mechanism presenting very good potential in terms of high stiffness, large payload, and high speed capability [7][8][9][10]. It has been widely used in many fields, such as medical equipment, entertainment, and factory automation [11]. The forward kinematic solution is more complicated than inverse kinematic solution because of the coupling among actuators.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Analysis of a Novel 6-DOF Robotic Crusher Based on Movement Characteristics

Shi

Liu

2019

Mathematical Problems in Engineering

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This paper proposes a novel 6-DOF robotic crusher that combines the performance characteristics of the cone crusher and parallel robot, such as interparticle breakage and high flexibility. Kinematics and dynamics are derived from the no-load and crushing parts in order to clearly describe the whole crushing process. For the no-load case, the kinematic and dynamic equations are established by using analytical geometry and Lagrange equation. Analytical geometry is mainly used to solve the inverse kinematics and then establish the velocity relationship between generalized coordinates and actuators. Lagrange equation which takes into account the weight of the mantle and actuators is used to solve driving forces of actuators. For the crushing case, crushing pressure is related to the compression ratio and particle size distribution, but the selection and breakage functions should be established first. Because the trajectory model of the mantle is difficult to be established by using analytical method, it can be obtained by an eccentric simulation. The results of input velocities and driving forces of actuators are distinctive due to the eccentric angle and selection of the initial position. Finally, the proposed approach is verified by a numerical example and then the energy consumption is calculated.

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“…A fuzzy logic iterative learning control scheme for trajectory tracking of a parallel manipulator was carried out in the simulation platform by Boudjedir et al (2017). A model-free control approach based on the dual neural network was developed for a Stewart platform of which the tracking control of the end-effector was performed in a simulation platform (Mirza et al, 2017). Navvabi and Markazi (2018) proposed an adaptive fuzzy SMC and observer for position control of a developed Stewart platform.…”

Section: Introductionmentioning

confidence: 99%

High order differential feedback controller design and implementation for a Stewart platform

Ayas

Şahin

Altaş

2019

Journal of Vibration and Control

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Stewart platform or other parallel manipulators with a Stewart structure are commonly used in flight simulators, surgical operations, medical rehabilitation processes, machine tools, industrial applications, etc. Therefore, researchers have paid attention to position control of these manipulators in addition to their design and development process. In this study, a developed Stewart platform and its inverse kinematic analysis are presented first. Then, a model-free control scheme called a high order differential feedback controller scheme is designed for the Stewart platform in order to improve its trajectory tracking performance and robustness against to different reference trajectories. Real-time trajectory tracking experiments with varied reference trajectories are carried out to show the robustness and effectiveness of the high order differential feedback controller scheme compared to the traditional proportional–integral–derivative controller of which the parameters are optimally tuned. The obtained visual trajectory tracking results and numerical performance results based on error-based performance measurement metrics such as integral of absolute error, integral of squared error, and integral of time-weighted absolute error are provided for both the proposed high order differential feedback controller scheme and the optimal tuned proportional–integral–derivative controller. Experimental results show that the proposed high order differential feedback controller scheme is more robust than the proportional–integral–derivative controller. Furthermore, the high order differential feedback controller scheme has superiority in both transient and steady-state responses and even the parameters of the proportional–integral–derivative controller are optimally tuned.

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Simultaneous learning and control of parallel Stewart platforms with unknown parameters

Cited by 32 publications

References 54 publications

Kinematics Simulation of Gough-Stewart Parallel Manipulator by Using Simulink Package in Matlab Software

Kinematics Simulation of Gough-Stewart Parallel Manipulator by Using Simulink Package in Matlab Software

Dynamic Modeling and Analysis of a Novel 6-DOF Robotic Crusher Based on Movement Characteristics

High order differential feedback controller design and implementation for a Stewart platform

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