本專著是嘗試將數(shù)據(jù)驅(qū)動(dòng)的思想應(yīng)用于自動(dòng)控制中,形成一種新的控制策略-數(shù)據(jù)驅(qū)動(dòng)控制。這也引導(dǎo)著可以將數(shù)據(jù)驅(qū)動(dòng)的思想與目前眾多已有的控制方法進(jìn)行相結(jié)合,以構(gòu)成更為簡便,更實(shí)用的控制方法。比如將數(shù)據(jù)驅(qū)動(dòng)思想引入至模型預(yù)測控制中,得到數(shù)據(jù)驅(qū)動(dòng)預(yù)測控制;將數(shù)據(jù)驅(qū)動(dòng)思想引入至自適應(yīng)控制中,得到數(shù)據(jù)驅(qū)動(dòng)自適應(yīng)控制等。為真正了解和熟悉數(shù)據(jù)驅(qū)動(dòng)思想,本專著首先詳細(xì)給出了鋪墊性的前期工作,即數(shù)據(jù)驅(qū)動(dòng)思想與系統(tǒng)辨識(shí)的相結(jié)合。從數(shù)據(jù)驅(qū)動(dòng)的角度更深入地展開對系統(tǒng)辨識(shí)的研究,研究深度上更深入,更深層次。分別在閉環(huán)反饋系統(tǒng)的數(shù)據(jù)驅(qū)動(dòng)辨識(shí),閉環(huán)系統(tǒng)的數(shù)據(jù)驅(qū)動(dòng)模型結(jié)構(gòu)檢驗(yàn),非線性系統(tǒng)的數(shù)據(jù)驅(qū)動(dòng)辨識(shí)展開結(jié)合。在數(shù)據(jù)驅(qū)動(dòng)思想在自動(dòng)控制中的應(yīng)用上,介紹了兩種目前申請人正在從事研究的數(shù)據(jù)驅(qū)動(dòng)控制策略數(shù)據(jù)驅(qū)動(dòng)模型預(yù)測控制和數(shù)據(jù)驅(qū)動(dòng)迭代校正控制。理論需要工程實(shí)踐的驗(yàn)證,為此將理論上的數(shù)據(jù)驅(qū)動(dòng)思想,數(shù)據(jù)驅(qū)動(dòng)辨識(shí)和數(shù)據(jù)驅(qū)動(dòng)控制分別應(yīng)用在鋰電池和飛機(jī)顫振的工程實(shí)踐中,用以實(shí)現(xiàn)理論與實(shí)踐的完美結(jié)合。
王建宏,男,1980年10月生,江西吉安人,博士,教授。2002年7月畢業(yè)于江西師范大學(xué),2007年7月畢業(yè)于云南大學(xué),2011年7月畢業(yè)于南京航空航天大學(xué),2013年7月博后出站于南京大學(xué)。2013年7月-2017年12月工作于中國電子科技集團(tuán)第28研究所,2018年1月-2018年12月工作于意大利米蘭理工大學(xué),2019年1月-2019年12月工作于西班牙塞維利亞大學(xué),2020年1月-2020年12月工作于墨西哥蒙特雷科技大學(xué),2021年1月-至今工作于江西理工大學(xué)。主要從事自動(dòng)控制、自適應(yīng)控制、模型預(yù)測控制、飛行控制和編隊(duì)控制等研究。
Chapter 1 Introduction of data driven
control/1
1.1 Introduction/l
1.2 Outline and contributions/6
1.3 Publications/8
Chapter 2 Data driven model predictive
control/11
2.1 Introduction/11
2.2 Application of bounded error
identification
into model predictive control/13
2.3 Application of interval predictor model
into model predictive control /29
2.4 Stability analysis in cooperative
distributed model
predictive control /48
2.5 Summary/60
Chapter 3 Data driven identification for
closed loop system/61
3.1 Introduction/61
3.2 Stealth identification strategy for
closed loop
linear time invariant system/63
3.3 Performance analysis of closed loop
system with
a tailor made parameterization / 81
3.4 Minimum variance control strategy for
closed loop system / 98
3.5 Summary/107
Chapter 4 Data
driven model validation for
closed loop system/108
4.1 Introduction/108
4.2 Model structure validation for closed
loop
system identification/109
4.3 Non-asymptotic confidence regions in
closed loop
model validation /123
4.4 Further results on model structure
validation / 128
4.5 Finite sample properties for closed
loop identification/135
4.6 Summary/149
Chapter5Data driven identification for
nonlinear system/151
5.1 Introduction/151
5.2 Parallel distributed estimation for
polynomial
nonlinear state space models/152
5.3 Summary/175
Chapter 6 Data driven iterative tuning
control/176
6.1 Introduction / 176
6.2 Zonotope parameter identification for
piecewise affine system/177
6.3 Iterative correlation tuning control
for
closed loop linear time invariant system /
194
6.4 Controller design for many variables
closed
loop system under non-interaction condition
/213
6.5 Summary/225
Chapter 7 Data driven applications/226
7.1 Introduction/226
7.2 Applying set membership strategy in
state of
charge estimation for Lithium-ion battery
/227
7.3 Optimal input signal design for
aircraft flutter
model parameters identification / 254
7.4 Summary/290
Chapter 8 Conclusions
and outlook/291
8.1 Conclusions/291
8.2 Outlook/294
References/296
Postscript/310