電動(dòng)負(fù)載模擬器的非線性因素分析及補(bǔ)償
中國(guó)測(cè)試付夢(mèng)瑤1，2， 楊瑞峰1，2， 郭晨霞1，2， 張 鵬1，2， 張新華3
摘 要：為提高電動(dòng)負(fù)載模擬器系統(tǒng)的動(dòng)態(tài)性能和信號(hào)跟蹤準(zhǔn)確度，提出針對(duì)系統(tǒng)摩擦非線性和間隙非線性進(jìn)行補(bǔ)償?shù)姆椒?。分析系統(tǒng)存在的非線性因素及其對(duì)系統(tǒng)造成的影響，在此基礎(chǔ)上建立其非線性數(shù)學(xué)模型。采用基于小波神經(jīng)網(wǎng)絡(luò)的PID控制器實(shí)現(xiàn)摩擦非線性補(bǔ)償，同時(shí)利用間隙逆模型針對(duì)間隙非線性進(jìn)行補(bǔ)償。利用Matlab軟件對(duì)補(bǔ)償結(jié)果進(jìn)行仿真驗(yàn)證，仿真結(jié)果顯示經(jīng)過(guò)補(bǔ)償后系統(tǒng)正弦響應(yīng)曲線跟隨性能變好，跟蹤誤差明顯減小，準(zhǔn)確度得到很大改善。仿真結(jié)果證明：基于小波神經(jīng)網(wǎng)絡(luò)的PID控制器和間隙逆模型分別對(duì)摩擦非線性和間隙非線性有明顯的抑制效果，系統(tǒng)動(dòng)態(tài)性能得到提高。
關(guān)鍵詞：電動(dòng)負(fù)載模擬器；非線性；摩擦補(bǔ)償；神經(jīng)網(wǎng)絡(luò)
文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1674-5124（2016）01-0096-06
Analysis and compensation for the nonlinearity of electric load simulator
FU Mengyao1，2， YANG Ruifeng1，2， GUO Chenxia1，2， ZHANG Peng1，2， ZHANG Xinhua3
（1. School of Instrument and Electronics，North University of China，Taiyuan 030051，China；
2. Key Laboratory of Instrumentation Science & Dynamic Measurement，Ministry of Education，
North University of China，Taiyuan 030051，China；
3. Beijing Automation Control Equipment Research Institute，Beijing 100000，China）
Abstract: To improve the dynamic performance and signal tracking accuracy of electric load simulator systems， a method have been proposed for compensating the friction nonlinearity and the gap nonlinearity of the system. Particularly， the nonlinear factors of the system and its impact are analyzed and a nonlinear mathematical model has been established. The friction nonlinearity is compensated with a PID controller based on the wavelet neural network and the gap nonlinearity is compensated through a gap inverse model. The results are verified with MATLAB software. The simulation test shows that， after compensation， the tracing performance of the sinusoidal response curve of the system is improved and the tracking error is largely reduced. Also， the simulation results indicate that the PID controller and the gap inverse model have significantly inhibited friction nonlinearity and gap nonlinearity so as to enhance the dynamic performance of the system.
Keywords: electric load simulator； nonlinearity； friction compensation； neural network
