Purpose

The purpose of this paper is to resolve the problem of the dynamic response performance of the driving control system for a six-wheeled planetary rover. An adaptive sliding mode controller based on an improved genetic algorithm (IGA) to tune PID sliding surface parameters was used in the driving control system of the planetary rover.

Design/methodology/approach

First, the mathematical model of planetary rover driving control is established. Second, according to sliding mode variable structure control, an equivalent controller and a disturbance controller are constructed to solve the problem of a multi-disturbance nonlinear driving control system of planetary rovers and an IGA is used to tune PID parameters.

Findings

Simulation results show that the proposed control algorithm improves the accuracy of the driving control system and optimizes the smoothness of rover motion control.

Practical implications

The controller based on the IGA to tune PID sliding surface parameters has good self-adaptability and real-time controllability for the control object which is difficult to present a precise mathematical model.

Originality/value

The advanced control method is adopted to solve the uncertainty and external interference of planetary rovers in a complex environment. The mathematical model of the six-wheeled rover is established as the control object and the uncertainty and external disturbance of the model are considered. The controller based on IGA has good adaptability and real-time performance and the control algorithm can be used to drive robots in complex environments.

中文翻译：

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基于改进遗传算法整定PID的行星漫游器自适应滑模控制。

目的

本文的目的是解决六轮行星漫游车驱动控制系统的动态响应性能问题。基于改进遗传算法（IGA）的自适应滑模控制器用于调节PID滑动表面参数，用于行星漫游车的驱动控制系统。

设计/方法/方法

首先，建立了行星漫游者驾驶控制的数学模型。其次，根据滑模变结构控制，构造了等效控制器和扰动控制器，以解决行星漫游车多扰动非线性驱动控制系统的问题，并利用IGA对其PID参数进行了调节。

发现

仿真结果表明，所提出的控制算法提高了驾驶控制系统的精度，优化了漫游者运动控制的平稳性。

实际影响

基于IGA的PID滑动面参数整定控制器对控制对象具有良好的自适应性和实时可控性，难以给出精确的数学模型。

创意/价值

采用先进的控制方法，解决了复杂环境下行星漫游车的不确定性和外部干扰。建立了六轮流浪者的数学模型作为控制对象，并考虑了模型的不确定性和外部干扰。基于IGA的控制器具有良好的适应性和实时性能，该控制算法可用于驱动复杂环境中的机器人。