Indoor service robot controller design method and system based on visual dynamic system

Abstract

The invention discloses an indoor service robot controller design method under a visual dynamic system and a system. The forgetting factor PD type iterative learning controller is constructed by virtue of the visual dynamic system. The method comprises the following steps that a service robot movement model under the visual dynamic system is constructed; the forgetting factor PD type iterative learning controller of a service robot is constructed; the expected movement track of a movement mechanism of the service robot is preset, and the input variable and the initialization position of the movement mechanism of the service robot are initialized; whether the error between the actual movement track and the expected movement track of the movement mechanism of the service robot meets the requirements or not is judged, if the requirement is met, the actual movement track of the service robot coincides with the expected movement track of the service robot; and otherwise, the above iterationprocess is repeated until the error meets the requirement.

Description

technical field [0001] The invention belongs to the field of indoor service robot control, and in particular relates to a design method and system for an indoor service robot forgetting factor PD type iterative learning controller under a visual dynamic system. Background technique [0002] Service robots have been widely used in homes, and robots have gradually improved our lifestyles and brought more and more convenience to families. However, in the face of a complex home environment, more and more requirements are put forward on how to control the trajectory of the robot. [0003] The service robot itself is a typical highly nonlinear kinematics model, and its trajectory precision control problem has always been a research hotspot in the field of home service robots. The current precise control methods for the trajectory of service robots include: variable structure control, sliding mode control, robust control, and learning control; Interference factors lead to changes...

AI Technical Summary

Problems solved by technology

[0004] The inventor found in the research and development process that the existing learning control methods all have model control and need to control the robot's own information. When there is an error in the acquisition of the robot's own information, it needs to relearn to achieve a better tracking effect.

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