Reference trajectory tuning of model predictive control. (May 2016)

• Record Type: Journal Article
• Authors: Yamashita, André Shigueo; Alexandre, Paulo Martin; Zanin, Antonio Carlos; Odloak, Darci
• Is Part Of: Control engineering practice. Volume 50(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-05
• Subjects: Model predictive controller tuning -- Reference tracking tuning -- Zone control; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.02.003
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: An approach to minimize tuning effort of nominal Model Predictive Control algorithms is proposed. The algorithm dynamically calculates output set points to accommodate user-defined output importance, which is more intuitive than selecting values for the MPC weighing matrices. Instead of tuning the weights on the outputs deviations from their set points, weights on the input values and input increments, which are the usual tuning parameters of MPC, the desired output control performance of the MPC can be specified by performance factors. The proposed method extends the existing methods that consider a reference trajectory for the output tracking to the case of zone control and input targets. The proposed method also assumes that, as in most commercial MPC packages, the controller has two layers: a static layer and an extended dynamic layer. The method is illustrated by three case studies, contemplating both SISO and MIMO systems. It is observed that: the output set point tracking performance can be changed without modifying the MPC tuning weights, the approach is capable of achieving similar performance to conventional MPC tuned by multiobjective optimization techniques from the literature, with a fraction of computer effort, and it can be integrated with Real Time Optimization algorithms to control complex systems, always respecting output constraints. Highlights: The MPC cost function takes into account dynamic set point trajectories. The new MPC is industry-friendly. Its performance is on par with another tuning method from the literature. Its integration with real time optimization algorithms is straightforward.