A joint angle optimization method of underactuated manipulator based on improved particle swarm optimization

Abstract

The embodiment of the invention discloses a method for optimizing the joint angle of an underactuated manipulator based on an improved particle swarm algorithm. Including: establishing a plane three-degree-of-freedom passive-active-active (PAA type) underactuated manipulator dynamics model; The order is reduced to two passive-active (PA-type) subsystems, and the relationship between the joint angle and the end position of the manipulator is clarified; finally, the difference between the end position of the underactuated manipulator arm and the target position is used as the objective function, based on the particle swarm The optimization principle of the algorithm introduces the Metropolis acceptance criterion of simulated annealing and the crowding factor of the artificial fish swarm algorithm to solve the optimal joint angle corresponding to the target position. According to the technical solution proposed by the embodiment of the present invention, when the position of the underactuated manipulator is controlled, the probability of the optimization algorithm falling into a local optimum can be reduced and the solution accuracy can be improved.

Description

【Technical field】 [0001] The invention belongs to the technical field of automatic control, relates to model reduction and particle swarm algorithm, in particular to an underactuated mechanical arm joint angle optimization method based on improved particle swarm algorithm. 【Background technique】 [0002] In space, in order for the manipulator to complete complex and diverse tasks, the space manipulator should have the characteristics of light weight, low energy consumption, certain redundancy and high flexibility. The energy consumption of the manipulator is positively correlated with the number of driving devices. Underactuated manipulators require fewer drive motors than the number of joints. Under the premise of ensuring the completion of the task, it is more suitable for working in space. [0003] In the position control of the underactuated manipulator, the dynamics of the manipulator has a high degree of nonlinear coupling. Using the idea of ​​inversion control, the th...

AI Technical Summary

Problems solved by technology

In order to achieve precise position control, it is necessary to accurately solve the target angle of the active joint. The particle swarm algorithm initializes the particles uniformly, updates the particles according to the global optimal and historical optimal, and solves the optimal joint angle corresponding to the target position. However, The accuracy of the particle swarm algorithm in solving the optimal joint angle of the manipulator still needs to be optimized urgently

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