Model predictive control: A multi-parametric programming approach
References (8)
- et al.
Comput. Chem. Engng.
(1999) Comput. Chem. Engng.
(1990)- et al.
Automatica
(1999) - et al.
Ind. Eng. Chem. Res.
(1997)
Cited by (14)
Optimal operation and control of intensified processes — challenges and opportunities
2019, Current Opinion in Chemical EngineeringCitation Excerpt :To address the remaining challenges related to large-scale problems, continuous efforts on the development of parallel computing, software and algorithms should be encouraged. The computational complexity of model predictive controllers can be reduced with the implementation of multi-parametric mp-MPC, which enables the offline solution of MPC problem by reformulating it into a multi-parametric optimization problem and obtaining explicit control laws [27,28]. Such strategy has been applied to pressure swing adsorption systems by Khajuria and Pistikopoulos [29] and extended in Khajuria and Pistikopoulos [30].
Embedded linear model predictive control for 8-bit microcontrollers via convex lifting
2017, IFAC-PapersOnLineOn multi-parametric programming and its applications in process systems engineering
2016, Chemical Engineering Research and DesignCitation Excerpt :Therefore, most algorithms which are applicable to mp-QP problems are inherently also applicable to mp-LP problems. Based on the results from the Basic Sensitivity Theorem, a string of papers in 2000 (Bemporad et al., 2000c,b; Pistikopoulos et al., 2000), as well as the famous paper by Bemporad et al. (2002a), described a geometrical approach for the solution of mp-QP problems, which relies on the exploration of the parameter space by moving from one critical region to another. As the initial algorithm was shown to generate a large number of artificial cuts (Tøndel et al., 2003b), new algorithms based on the geometrical principle were described, based on variable step-size (Baotic, 2002), inference of the active set of adjacent critical regions (Tøndel et al., 2003b,c) and combination of the inference of the active set with the original algorithm (Spjøtvold et al., 2006; Spjotvold et al., 2006a).
Embedded explicit model predictive vibration control
2016, MechatronicsCitation Excerpt :The other main class of efficient MPC formulations preserves the optimality of the problem, while still removing some of the burden of solving the task in real time [11]. The best known representative formulation of the latter class rests on the idea of transferring the computational complexity from on-line control into off-line, and is called multi-parametric MPC (MPMPC) or explicit MPC (EMPC) [2,3]. In EMPC, the optimization problem is essentially solved using parametric programming beforehand, formulating the MPC control problem as a set of regions in state space to which linear control laws are assigned.
Integrated production scheduling and process control: A systematic review
2014, Computers and Chemical EngineeringDynamic optimization of an industrial evaporator using graph search with embedded nonlinear programming
2006, Analysis and Design of Hybrid Systems 2006