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Model predictive control: Past, present and future


M. Morari, J.H. Lee

Computers & Chemical Engineering, vol. 23, no. 4, pp. 667-682

More than 15 years after model predictive control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started to emerge. The issues of feasibility of the on-line optimization, stability and performance are largely understood for systems described by linear models. Much progress has been made on these issues for non-linear systems but for practical applications many questions remain, including the reliability and efficiency of the on-line computation scheme. To deal with model uncertainty ‘rigorously’ an involved dynamic programming problem must be solved. The approximation techniques proposed for this purpose are largely at a conceptual stage. Among the broader research needs the following areas are identified: multivariable system identification, performance monitoring and diagnostics, non-linear state estimation, and batch system control. Many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated systematically and effectively into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program. Efficient techniques for solving these problems are becoming available.


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% Autogenerated BibTeX entry
@Article { MorLee:1999:IFA_1641,
    author={M. Morari and J.H. Lee},
    title={{Model predictive control: Past, present and future}},
    journal={Computers \& Chemical Engineering},
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