A neural network full-order sliding mode control method for manipulator servo system with dead zone compensation
A technology of servo system and control method, applied in the direction of adaptive control, general control system, control/regulation system, etc.
Active Publication Date: 2018-01-09
扬州祥帆重工科技有限公司
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[0005] In order to overcome the deficiency that the existing manipulator servo system cannot avoid the influence of the dead zone input on the system and the chattering problem of the sliding mode control, the present invention provides a neural network full-order sliding mode control of the manipulator servo system with dead zone compensation method, realizes effective compensation for unknown dead zone, improves chattering and singularity problems of the system, and ensures fast and stable convergence of the system
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[0083] The present invention will be further described below in conjunction with the accompanying drawings.
[0084] Referring to Figure 1- Figure 4 , a neural network full-order sliding mode control method for a manipulator servo system with dead zone compensation, comprising the following steps:
[0085] Step 1, establish the dynamic model of the servo system of the manipulator, initialize the system state, sampling time and control parameters, the process is as follows:
[0086] 1.1 The dynamic model expression of the manipulator servo system is:
[0087]
[0088] where, q, and are the positions, velocities and accelerations of the manipulator joints; M H , C H and D H respectively represent the symmetric positive definite inertia matrix of each joint, the centrifugal Coriolis matrix and the diagonal positive definite matrix of the damping friction coefficient; G H Represents the gravity term; τ is the control signal; T(τ) is the dead zone, expressed as:
[008...
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A neural network full-order sliding mode control method for a manipulator servo system with dead zone compensation. For a manipulator servo system with a dynamic actuator and an unknown dead zone input, the full-order sliding mode control method is used, combined with the neural network Network, design a neural network full-order sliding mode control method for manipulator servo system with dead zone compensation. The dead zone is converted into a linear time-varying system, and then the unknown function is approximated by the neural network, which compensates the additional influence of the traditional unknown dead zone and the unknown parameters of the system. In addition, the design of the full-order sliding mode surface is to ensure the rapid and stable convergence of the system, and to improve chattering and solve singular problems by avoiding differential terms in the actual control system. The invention provides a control method capable of improving the chattering problem of the sliding mode surface and solving the singular problem, and effectively compensating the unknown dynamic parameters of the system and the input of the unknown dead zone, so as to realize fast and stable control of the system.
Description
technical field [0001] The invention relates to a neural network full-order sliding mode control method of a mechanical arm servo system with dead zone compensation, in particular to a control method of a mechanical arm servo system with unknown dead zone input and unknown dynamic parameters of the system. Background technique [0002] As a highly automated device, the servo system of the manipulator has been widely used in high-performance systems such as robots and aviation vehicles. How to realize the fast and precise control of the servo system of the manipulator has become a hot issue. However, the unknown dead zone input widely exists in the servo system of the manipulator, which often leads to the reduction of the efficiency of the control system or even failure. For the control problem of the servo system of the manipulator, there are many control methods, such as PID control, adaptive control, sliding mode control and so on. [0003] Sliding mode control is conside...
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IPC IPC(8): G05B13/04
Inventor 陈强胡鑫
Owner 扬州祥帆重工科技有限公司