Mechanical arm general control method based on determined learning theory

Abstract

The invention discloses a mechanical arm general control method based on determined learning theory. The method comprises the following steps: establishing a mechanical arm dynamic model, establishing an expected period trajectory; establishing an adaptive RBF (radial basis function) neural network controller, adjusting a weight of the RBF neural network controller, thus conditions that a mechanical arm tracks the expected period trajectory and the RBF neural network locally approaches an unknown dynamic model in a mechanical arm closed-loop system; establishing a constant neural network; and using the constant RBF neural network to finish a control task. By using the mechanical arm general control method provided by the invention, the experience period trajectory of the mechanical arm closed-loop control system unknown dynamic along the mechanical arm can be accurately learned in a local region under a condition that a system parameter is completely unknown; the effective knowledge of the closed-loop system dynamics can be learned in a stable dynamic control process, and can be stored in a manner of constant RBF network weight; the effective knowledge can be successfully applied to the subsequently same or similar control task so as to improve the control performance of the control system and save energy.

Description

technical field [0001] The invention relates to a general control method for a manipulator, in particular to a closed-loop dynamic learning and tracking control method for a manipulator based on deterministic learning theory. Background technique [0002] With the high development of science and technology, the application fields of robot system are more and more extensive, such as industry, agriculture, medical treatment and so on. With the widespread application of robotic systems, intelligence has become an important direction of its development. Designing a general-purpose controller for a manipulator whose system parameters are completely unknown has not been reported in the literature. It not only simplifies the structure of the control system of the manipulator, but also saves the cost, and can improve the control precision at the same time. [0003] In recent years, neural networks have made gratifying achievements in many fields such as pattern recognition, digita...

AI Technical Summary

Problems solved by technology

The main disadvantage of this method at present is that it is difficult to understand the physical meaning of the neural network, it is difficult to ensure that the weights of the neural network converge to the true value (optimal value), and then it is difficult to ensure that the neural network is truly close to the system dynamics

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