- Title
- Quadratic model predictive control including input cardinality constraints
- Creator
- Aguilera, Ricardo P.; Urrutia, Gabriel; Delgado, Ramón A.; Dolz, Daniel; Agüero, Juan C.
- Relation
- IEEE Transactions on Automatic Control Vol. 62, Issue 6, p. 3068-3075
- Publisher Link
- http://dx.doi.org/10.1109/TAC.2017.2674185
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2017
- Description
- This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the ℓ₀ -norm of the control input vector at each control horizon instant. Besides this sparsity constraint, bounded constraints are also imposed on both control input and system state. Under this scenario, we provide sufficient conditions for guaranteeing practical stability of the closed-loop. We transform the combinatorial optimization problem into an equivalent optimization problem that does not consider relaxation in the cardinality constraints. The equivalent optimization problem can be solved utilizing standard nonlinear programming toolboxes that provides the input control sequence corresponding to the global optimum.
- Subject
- optimization; stability analysis; asymptotic stability; control systems; matching pursuit algorithms; predictive control; numerical stability
- Identifier
- http://hdl.handle.net/1959.13/1399523
- Identifier
- uon:34621
- Identifier
- ISSN:0018-9286
- Rights
- © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- Language
- eng
- Full Text
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