Iterative learning trajectory tracking control and robust optimization method for two-dimensional motion mobile robot

Abstract

The invention discloses an iterative learning trajectory tracking control and robust optimization method for a two-dimensional motion mobile robot. The method includes the steps that firstly, a kinetic equation of a two-dimensional motion mobile robot discrete non-linear motion system model is established; a discrete non-linear state space expression is established; a P type open-closed loop iterative learning controller based on the iterative learning control technology is established; then the robust convergence of the established discrete non-linear control system is theoretically analyzed; then parameter item splitting is conducted on control gains of the P type controller, meanwhile, a quadratic performance index function based on controller parameters is designed, and the purpose is to optimize the control parameters; finally, monotone convergence characteristics of output errors and parameter selection conditions generated when a control algorithm acts on a controlled system are analyzed and optimized, and the two-dimensional motion mobile robot can rapidly track an expected motion trajectory at high precision. The method has the advantages that the robust optimization iterative learning controller is suitable for tracking control in an ideal state and suitable for trajectory tracking tasks under the condition that interference exists outside. A designed iterative algorithm is simple and efficient, introduction of a large number of additional parameter variables is not needed, and engineering realization is easy.

Description

technical field [0001] The invention relates to an iterative learning trajectory tracking control of a two-dimensional motion mobile robot and a robust optimization method thereof, belonging to the field of robot optimization control. Background technique [0002] A mobile robot is a highly intelligent system that integrates multiple functions such as environmental perception, dynamic decision-making and planning, behavior control and execution. It is not only widely used in industrial production, national defense, medical and service industries, but also in mine clearance, It is widely used in dangerous situations such as search and rescue. On the basis of receiving instruction signals, mobile robots can complete corresponding tasks and improve the safety and work efficiency of workers. [0003] As the mobile robot system repeatedly performs a certain task, it will inevitably be affected by certain external interference factors. When these adverse factors interfere with th...

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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of further improving the trajectory tracking speed of non-complete mobile robots under the iterative learning control optimization technology. For a class of discrete nonlinear repetitive systems with random state disturbances, output disturbances, and system initial values ​​that are not strictly consistent, the proposed A P-type open-closed-loop robust iterative learning trajectory tracking control method based on iterative learning control technology is proposed. The strict robust stability of the algorithm is proved by the λ norm theory, and the gain of the iterative learning control law is optimized by multi-objective function performance indicators Matrix parameters to ensure that the actual output of the system under the optimization algorithm tracks the expected trajectory and the system output error has a monotonic convergence characteristic, achieving the purpose of improving the convergence speed and tracking accuracy of the learning algorithm

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