Title :
Robust dual control MPC with application to soft-landing control
Author :
Cheng, Y. ; Haghighat, S. ; Di Cairano, S.
Author_Institution :
Res. Labs., Mitsubishi Electr. United States, Cambridge, MA, USA
Abstract :
Dual control frameworks for systems subject to uncertainties aim at simultaneously learning the unknown parameters while controlling the system dynamics. We propose a robust dual model predictive control algorithm for systems with bounded uncertainty with application to soft landing control. The algorithm exploits a robust control invariant set to guarantee constraint enforcement in spite of the uncertainty, and a constrained estimation algorithm to guarantee admissible parameter estimates. The impact of the control input on parameter learning is accounted for by including in the cost function a reference input, which is designed online to provide persistent excitation. The reference input design problem is non-convex, and here is solved by a sequence of relaxed convex problems. The results of the proposed method in a soft-landing control application in transportation systems are shown.
Keywords :
control system synthesis; learning (artificial intelligence); parameter estimation; predictive control; robust control; set theory; uncertain systems; admissible parameter estimation; bounded uncertainty; constrained estimation algorithm; constraint enforcement; control input; convex problems; cost function design; nonconvex problem; reference input design problem; robust control invariant set; robust dual control MPC; robust dual model predictive control algorithm; simultaneously unknown parameter learning; soft-landing control; system dynamic control; system uncertainties; transportation systems; Cost function; Covariance matrices; Prediction algorithms; Predictive models; Robustness; Trajectory; Uncertainty;
Conference_Titel :
American Control Conference (ACC), 2015
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4799-8685-9
DOI :
10.1109/ACC.2015.7171932
