An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study. (June 2020)

• Record Type: Journal Article
• Authors: Silva, Bernardo P.M.; Santana, Bruno A.; Santos, Tito L.M.; Martins, Márcio A.F.
• Is Part Of: Control engineering practice. Volume 99(2020)
• Publication Date: 2020-06
• Subjects: Model predictive control -- Rotary flexible link -- Closed-loop stability -- Feasible-optimization problem; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104396
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: This work addresses the application of implementable stabilizing model predictive control (MPC) strategies to a rotary flexible link (RFL). Despite their practice usefulness and design simplicity, the implementation of these stable MPC controllers with guaranteed feasibility in a real experiment, and dedicated to dynamic features of RFL systems, have not yet been documented by the literature. In contrast to conventional finite-horizon MPC strategies, infinite-horizon MPC (IHMPC) techniques ensure nominal closed-loop stability irrespective of the cost function parameters. Also, if these IHMPC strategies have feasible-optimization problem based formulations, such as implementable ones investigated here, their applicability naturally becomes attractive to the industry. Simulation and experimental results illustrate the usefulness of the implementable stabilizing MPC controllers when compared to non-feasible in practice ones, such as a classical infinite-horizon MPC and a conventional finite-horizon generalized predictive controller, thus ensuring their benefits in terms of performance and computational burden in the context of constrained RFL mechatronic systems. Highlights: Investigation of MPC strategies in a real rotary flexible-link apparatus. Emphasis of feasible optimization based stabilizing MPC formulations in practice. Comparison with classical infinite-horizon MPC and conventional finite-horizon GPC. Benefits in terms of performance and computational burden from implementable MPC.