Robust dual control MPC with application to soft-landing control
- Resource Type
- Conference
- Authors
- Cheng, Y.; Haghighat, S.; Di Cairano, S.
- Source
- 2015 American Control Conference (ACC) American Control Conference (ACC), 2015. :3862-3867 Jul, 2015
- Subject
- Robotics and Control Systems
Robustness
Uncertainty
Prediction algorithms
Cost function
Trajectory
Predictive models
Covariance matrices
- Language
- ISSN
- 0743-1619
2378-5861
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.