本文選題：輪式探路機(jī)器人　切入點(diǎn)：無刷直流電機(jī)　出處：《沈陽工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：動(dòng)力設(shè)備的高精度快速穩(wěn)定控制一直是機(jī)器人研究的一個(gè)主要領(lǐng)域,而人工智能的高速發(fā)展更是推進(jìn)了智能算法在智能控制的應(yīng)用。模糊控制、神經(jīng)網(wǎng)絡(luò)自適應(yīng)控制、遺傳算法及滑模變結(jié)構(gòu)控制等技術(shù)日益成熟,成為解決非線性復(fù)雜系統(tǒng)控制問題的解決方案。如何將智能算法無縫鏈接到輪式探路機(jī)器人驅(qū)動(dòng)控制系統(tǒng),從而提高其精度仍然成為控制領(lǐng)域的一大研究熱點(diǎn)。本文采用Soildworks軟件設(shè)計(jì)輪式探路機(jī)器人的底盤,建立一個(gè)四輪驅(qū)動(dòng)的輪式探路機(jī)器人物理模型。根據(jù)機(jī)械原理進(jìn)行動(dòng)力學(xué)分析,進(jìn)行運(yùn)動(dòng)學(xué)仿真確定機(jī)器人運(yùn)動(dòng)特性,再根據(jù)機(jī)器人的運(yùn)動(dòng)性能以及負(fù)載等要求選擇合適的電機(jī)模型。然后選取一種無刷直流電機(jī)對(duì)機(jī)器人底盤車輪進(jìn)行驅(qū)動(dòng)并進(jìn)行仿真分析。這種無刷直流電機(jī)的速度環(huán)控制器采用Anti-windup PI和積分滑模變結(jié)構(gòu)控制兩種方式,電流環(huán)采用電流滯環(huán)控制。通過對(duì)比,積分滑�？刂破鞣�(wěn)定性、快速性更為優(yōu)異,但是積分滑�？刂破鲗�(duì)速度環(huán)控制時(shí),存在抖振現(xiàn)象。為了解決抖振現(xiàn)象,引入一種BP神經(jīng)網(wǎng)絡(luò)來構(gòu)成復(fù)合控制。設(shè)計(jì)的這種BP神經(jīng)網(wǎng)絡(luò)采取了在線更新學(xué)習(xí)的方式對(duì)滑模變結(jié)構(gòu)系統(tǒng)進(jìn)行調(diào)節(jié),從而在減小無刷直流電機(jī)控制誤差的同時(shí),也削弱了無刷直流電機(jī)控制系統(tǒng)的抖振問題。本文構(gòu)造一種滑模神經(jīng)網(wǎng)絡(luò)控制器對(duì)輪式探路機(jī)器人上的無刷直流電機(jī)進(jìn)行速度環(huán)預(yù)測(cè)控制,與單獨(dú)的積分滑模變結(jié)構(gòu)控制的仿真結(jié)果相比較,抖振性得到改善,控制精度取得提高,從而驗(yàn)證設(shè)計(jì)的正確性和有效性,體現(xiàn)出該控制方法的優(yōu)越性。
[Abstract]:The high precision, fast and stable control of power equipment has been a main field of robot research, and the rapid development of artificial intelligence has promoted the application of intelligent algorithm in intelligent control, fuzzy control, neural network adaptive control, neural network adaptive control, neural network adaptive control, neural network adaptive control, Genetic algorithm (GA) and sliding mode variable structure control (VSC) are becoming the solution to the control problem of nonlinear complex system. How to link the intelligent algorithm seamlessly to the driving control system of wheeled road detection robot, In order to improve its accuracy is still a research hotspot in the field of control. This paper uses Soildworks software to design the chassis of wheeled road detection robot. A physical model of a four-wheel drive wheeled road finding robot is established. According to the mechanical principle, the kinematics simulation is carried out to determine the kinematic characteristics of the robot. Then according to the motion performance and load requirements of the robot, the appropriate motor model is selected. Then, a brushless DC motor is selected to drive the robot chassis wheel, and the speed of the brushless DC motor is analyzed. The degree loop controller adopts Anti-windup Pi and integral sliding mode variable structure control. The current loop is controlled by current hysteresis loop. By contrast, the stability of the integral sliding mode controller is better than that of the integral sliding mode controller, but the chattering phenomenon exists when the integral sliding mode controller controls the velocity loop. A kind of BP neural network is introduced to form the compound control. The designed BP neural network adopts the on-line updating learning method to adjust the sliding mode variable structure system, thus reducing the control error of brushless DC motor at the same time. The chattering problem of brushless DC motor control system is also weakened. In this paper, a sliding mode neural network controller is constructed to predict the speed of brushless DC motor on wheeled robot. Compared with the simulation results of the single integral sliding mode variable structure control, the chattering property is improved and the control precision is improved, which verifies the correctness and effectiveness of the design and shows the superiority of the control method.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

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