| Abstract
| - Advanced process control (APC)in particular, model predictive control (MPC)has emerged as the mosteffective control strategy in process industry, and numerous applications have been reported. Nevertheless,there are several factors that limit the achievable performance of MPC. One of the limiting factors consideredin this paper is the presence of constraints. To exploit optimal control performance, continuous performanceassessment, with respect to the constraints of MPC, is necessary. MPC performance assessment has receivedincreasing interest, both in academia and in industry. This paper is concerned with a practical aspect ofperformance assessment of industrial MPC by investigating the relationship among process variability,constraints, and probabilistic economic performance of MPC. The proposed approach considers the uncertaintiesinduced by process variability and evaluates the economic performance through probabilistic calculations. Italso provides a guideline for the constraint tuning, to improve MPC performance.
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