Decentralized tracking control method for mechanical arm based on event triggering-neural dynamic programming

Abstract

The invention relates to a decentralized tracking control method for a mechanical arm based on event triggering-neural dynamic programming. The method comprises the steps of: constructing a modular mechanical arm subsystem dynamic model based on a joint torque feedback technology, and introducing an event triggering mechanism by designing an improved cost function of comprehensive tracking performance, controller output and approximate model items; and when and only when the triggering condition is met, updating a system control law, approximately solving a Hamilton equation by using an evaluation neural network based on the event triggering mechanism, and finally obtaining a modular mechanical arm joint module subsystem neural-optimal tracking control strategy based on the event triggering mechanism. Thus, safe and stable operation of a mechanical arm system when the mechanical arm system is in contact with the external environment is ensured.

Description

technical field [0001] The invention relates to an event-triggered-nerve dynamic programming modular manipulator decentralized tracking control method, which belongs to the field of robot control systems and control algorithms. Background technique [0002] Modular manipulator is a kind of manipulator with standard modules and interfaces, which can recombine and configure its own configuration according to different task requirements. According to the concept of modularization, the joint module of the modular manipulator includes communication, drive, control, sensing and other units, which can make the manipulator change its own configuration according to the task under different external environments and constraints, so that after reconstruction The robotic arm can have better adaptability to the new working environment. The modular manipulator is known as one of the most promising robots because of its easy assembly and portability, strong adaptability and low cost, and ...

AI Technical Summary

Problems solved by technology

At present, there are few researches on event-triggered control methods for manipulators, mainly because the manipulator is a type of hardware system with strong real-time performance, and unsuitable event trigger conditions may directly cause unknown behaviors such as locking or tremor of the manipulator.

In addition, for the modular manipulator system, due to the characteristic that its configuration changes with the task requirements, its dynamic model is difficult to establish by traditional methods, which makes the event-triggered control algorithm for each joint subsystem more difficult. advance

Images