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Design of Manipulator Control System of Forest Picking Robot Based on Fractional Order PID Sliding Mode Control
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This paper studies the control system design of forest picking robot manipulator based on fractional PID sliding mode control. In this paper, the structure characteristics, learning algorithm and application of fractional order PID sliding mode control in manipulator control are analyzed. In this paper, the nonlinear approximation property of fractional order PID sliding mode control is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.
Title: Design of Manipulator Control System of Forest Picking Robot Based on Fractional Order PID Sliding Mode Control
Description:
This paper studies the control system design of forest picking robot manipulator based on fractional PID sliding mode control.
In this paper, the structure characteristics, learning algorithm and application of fractional order PID sliding mode control in manipulator control are analyzed.
In this paper, the nonlinear approximation property of fractional order PID sliding mode control is theoretically verified.
This paper analyzes the basic structure of picking manipulator system in detail.
At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed.
The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.
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