An intelligent trajectory tracking control method for mobile robots

Abstract

An intelligent trajectory tracking control method for a mobile robot. For a given mobile robot model, the convergence of the least squares method is used to estimate the weights; and the weights of the controller are updated by introducing the method of neural network and strategy iteration in reinforcement learning, Solving the optimal value function, the method includes the following steps: 1) establishing a mobile robot nonlinear system; 2) initializing the state variables of the system, and giving an initial strategy; 3) using the method of reinforcement learning strategy iteration to update the weights , to find the optimal policy; 4) Introduce the neural network algorithm and use the convergence of least squares to calculate the weights; 5) Stop the policy update. The present invention ensures a better trajectory tracking effect without requiring dynamic knowledge inside the power system.

Description

technical field [0001] The invention belongs to the technical field of control, and specifically provides an intelligent trajectory tracking control method of a mobile robot, which ensures a better trajectory tracking effect without requiring dynamic knowledge inside a power system. Background technique [0002] In actual production and life, the value of mobile robots is increasing day by day, and the research on mobile robots has also received more attention. [0003] The dynamic equations of mobile robots are nonlinear, and most of the existing control methods are based on models. Although many feedback linear methods have been widely used in mobile robot control, including accurate feedback linearization, approximate feedback linearization and pseudo-linearization, etc. The model obtained after linearization still can not completely replace the nonlinear robot model, and it is very difficult to use linearization to model mobile robots, and the feasibility is not high, so...

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Problems solved by technology

[0003] The dynamic equations of mobile robots are nonlinear, and most of the existing control methods are based on models. Although many feedback linear methods have been widely used in mobile robot control, including exact feedback linearization, approximate feedback linearization and pseudo-linearization, etc., but The model obtained after linearization still cannot completely replace the nonlinear robot model, and it is very difficult and not feasible to use linearization to model mobile robots, so a data-driven method is proposed

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