A new information-weighted recursive algorithm for time-varying systems: application to UAV system identification

ABSTRACT

This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive least-squares (RLS) method. This new information-weighted recursive algorithm for time-varying systems has three novel features, discounting of inaccurate estimates through weighting by the Information matrix, using the reuse of past data in computing current parameter estimates, a new tuneable damping factor parameter and a precisely designed compensation term to neutralise the estimation error caused by time-varying coefficients. A rigorous proof of convergence is also provided. Simulations show that the new algorithm significantly outperforms standard RLS, exhibiting better tracking performance and faster convergence. Flight tests on a T-REX 800 helicopter Unmanned Aerial Vehicle platform show that it gives system parameter estimates that are accurate enough and converge quickly enough that flight controllers can be designed in real-time based on the online identified model.

KEYWORDS:

• System identification
• experience replay
• unmanned aerial vehicle
• online-identification method
• time-varying system

Additional information

Funding

This paper’s corresponding research are Supported by Scientific Instruments Development Program of NSFC [grant number 615278010], National Key Basic Research Program of China (973 program) [grant number 2014CB845303], Science and Technology Planning Project of Guangdong, China [grant number 2017B010116005], Science and Technology Program of Guangzhou, China [grant number 2013B020200006].

Notes on contributors

Zun Liu

Zun Liu received the B.E. degree in automation from South University of Technology, Guangzhou, China, in 2011. He is currently working towards the Ph.D. degree at the key laboratory of autonomous system and networked control, Ministry of Education South China University of Technology, Guangzhou, China. His research interests are in the field of system identification of UAV.

Honghai Ji

Honghai Ji received the M.S. degree in automation from Beijing Jiaotong University, Beijing, China, in 2006. He is currently working towards the Ph.D. degree at the advanced control systems laboratory from Beijing Jiaotong University. His research interests are in the field of adaptive control, iterative learning control, Kalman consensus filtering and automatic train operation control.

Hailong Pei

Hailong Pei received his Ph.D degree from the South China University of Technology, China in 1992, and Master and Bachelor degrees from the Northwestern Polytechnical University, China in 1989 and 1986, respectively. Currently, he is a professor of the School of Automation Science and Engineering, South China University of Technology, Director of the Key Lab of Autonomous Systems and Networked Control, Ministry of Education, and Director of the Unmanned Engineering Center of Guangdong Province. He works on unmanned systems and robotic control, and serves now as the deputy editor-in-chief of the Journal of Control Theory and Application, an associate editor of International Journal of Intelligent & Robotic Systems, and an associate editor of Acta Automatica Sinica.

Frank L. Lewis

Frank L. Lewis, Member, National Academy of Inventors. Fellow IEEE, Fellow IFAC, Fellow AAAS, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer. UTA Distinguished Scholar Professor, UTA Distinguished Teaching Professor, and Moncrief-O’Donnell Chair at the University of Texas at Arlington Research Institute. Qian Ren Thousand Talents Consulting Professor, Northeastern University, Shenyang, China. China Liaoning Friendship Award. He obtained the Bachelor’s Degree in Physics/EE and the MSEE at Rice University, the MS in Aeronautical Engineering from Univ. W. Florida, and the Ph.D. at Ga. Tech. He works in feedback control, intelligent systems, cooperative control systems, and nonlinear systems. He is author of 7 U.S. patents, numerous journal special issues, 363 journal papers, and 20 books, including Optimal Control, Aircraft Control, Optimal Estimation, and Robot Manipulator Control which are used as university textbooks worldwide. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award, U.K. Inst Measurement & Control Honeywell Field Engineering Medal, IEEE Computational Intelligence Society Neural Networks Pioneer Award, AIAA Intelligent Systems Award. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the year by Ft. Worth IEEE Section. Was listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. Texas Regents Outstanding Teaching Award 2013. He is Distinguished Visiting Professor at Nanjing University of Science & Technology and Project 111 Professor at Northeastern University in Shenyang, China. Founding Member of the Board of Governors of the Mediterranean Control Association.