Optimal Predictive Control - A brief review of theory and practice

Misik, Stefan; Çela, Arben; Bradáč, Zdeněk

doi:10.1016/j.ifacol.2016.12.058

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Nonlinear dynamic optimization (or optimal control) is at the core of NMPC and plays an important role in terms of implementation. Both the complexity and the computational time of the model-based optimizing controllers are of great concern and must consider the time available for sampling and the possible presence of feedback delays (Misik et al, 2016). The solution methods for dynamic optimization problems fall into the category of direct and indirect methods (Srinivasan et al, 2003b).…”

Section: Introductionmentioning

confidence: 99%

NMPC using Pontryagin’s Minimum Principle-Application to a two-phase semi-batch hydroformylation reactor under uncertainty

Aydın

Bonvin

Sundmacher

2018

Computers & Chemical Engineering

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Nonlinear model predictive control (NMPC) is an important tool to perform real-time optimization for batch and semi-batch processes. Direct methods are often the methods of choice to solve the corresponding optimal control problems, in particular for large-scale problems. However, the matrix factorizations associated with large prediction horizons can be computationally demanding. In contrast, indirect methods can be competitive for smallerscale problems. Furthermore, the interplay between states and co-states in the context of Pontryagin's Minimum Principle might turn out to be computationally quite efficient. This work proposes to use an indirect solution technique within shrinking-horizon in the context of NMPC. In particular, the technique deals with path constraints via indirect adjoining, which allows meeting active path constraints explicitly at each iteration. Uncertainties are handled by the introduction of time-varying backoff terms for the path 2 constraints. The resulting NMPC algorithm is applied to a two-phase semi-batch reactor for the hydroformylation of 1-dodecene in the presence of uncertainty, and its performance is compared to that of NMPC that uses a direct simultaneous optimization method. The results show that the proposed algorithm (i) can enforce feasible operation for different uncertainty realizations both within batch or from batch to batch, and (ii) it is significantly faster than direct simultaneous NMPC, especially at the beginning of the batch. In addition, a modification of the PMP-based NMPC scheme is proposed that enforces active constraints via tracking.

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Section: Introductionmentioning

confidence: 99%

NMPC using Pontryagin’s Minimum Principle-Application to a two-phase semi-batch hydroformylation reactor under uncertainty

Aydın

Bonvin

Sundmacher

2018

Computers & Chemical Engineering

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“…Based on the predictive model and rolling optimization, the predictive control has become an attractive feedback control strategy (Misik et al, 2016). The essence of predictive control is to optimize the system behaviours according to the future state of the system (Farina et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Mixed-kernel least square support vector machine predictive control based on improved free search algorithm for nonlinear systems

Tian

Wang

et al. 2018

Transactions of the Institute of Measurement and Control

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Many controlled objects in the actual industrial process are nonlinear systems, and the traditional control theory cannot achieve very good control effect. Based on swarm intelligence optimization algorithm, the nonlinear prediction and predictive control algorithm, this paper put forwards a nonlinear systems predictive control method based on the mixed-kernel least square support vector machine (LSSVM) model and improved free search (IFS) algorithm. The mixed-kernel LSSVM combines the advantages of radial basis function (RBF) and the Polynomial function, which can achieve a better prediction and modeling accuracy. The optimal parameters of the mixed-kernel LSSVM are obtained by IFS algorithm. The proposed predictive control method utilizes mixed-kernel LSSVM to estimate the nonlinear systems model and forecast the output of the system. The output error is reduced through output feedback and error correction. The rolling optimization of control variables are obtained by IFS algorithm. This predictive control method can be used to design effective controllers for nonlinear systems with unknown mathematical models. The stability analysis shows that the control method is asymptotically stable. The simulation experiment of single input and single output, multiple input multiple output and continuous stirred tank reactor nonlinear systems are performed. The validity of the proposed control method is also verified by an actual electric heating furnace system. The simulation and practical experiment results show that the proposed predictive control method has good tracking signal and anti-interference ability.

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Designing an Adaptive Body Art Maker with Computer Numerical Control-a paradigm of human robot interaction

Das

Deb

2020

2020 International Conference on Inventive Computation Technologies (ICICT)

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Optimal Predictive Control - A brief review of theory and practice

Cited by 5 publications

References 13 publications

NMPC using Pontryagin’s Minimum Principle-Application to a two-phase semi-batch hydroformylation reactor under uncertainty

NMPC using Pontryagin’s Minimum Principle-Application to a two-phase semi-batch hydroformylation reactor under uncertainty

Mixed-kernel least square support vector machine predictive control based on improved free search algorithm for nonlinear systems

Designing an Adaptive Body Art Maker with Computer Numerical Control-a paradigm of human robot interaction

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