Performance Assessment of Model Pedictive Control for Variability and Constraint Tuning

Abstract

Multivariate controller performance assessment (MVPA) has been developed over the last several years, but its application in advanced model predictive control (MPC) has been limited mainly due to issues associated with comparability of the variance control objective and that of MPC applications. MPC has been proven as one of the most effective advanced process control (APC) strategies to deal with multivariable constrained control problems with an ultimate objective toward economic optimization. Any attempt to evaluate MPC performance should therefore consider constraints and economic performance. In this work, we show that the variance based performance assessment may be transferred to performance assessment of MPC applications. The MPC economic performance can be evaluated by solving benefit potentials through either variability reduction of quality output variables or tuning of constraints. Algorithms for MPC performance assessment and constraint/variance tuning guidelines are developed through linear matrix inequalities (LMIs) using routine operating process data plus the process steady-state gain matrix. The proposed approach for MPC economic performance evaluation is illustrated and verified via a simulation example of an MPC application as well as a pilot-scale experiment.

Bayesian methods for control loop monitoring and diagnosis

“…As an example, we consider the tuning through adjustment of weights on CVs and MVs. Upon each set of tuning of the MPC weighting parameters, an optimal control problem at the dynamic control level is solved and new distribution of input/output in terms of the shape (variance/covariance for Gaussian) is determined in a similar way as [Xu et al, 2007]. With the shape of the new distribution function, the optimal operating points owing to the change of weighting are again solved from eqn.…”

Enhancement of Investment Decision Making Using Real Options with Application to Advanced Process Control Project

“…Many researches are interested in the techniques to measure the variance reduction by using economic performance functions [8][9][10]. The MPC economic performance evaluation has been studied in [11] and the performance index for assessing the benefits of process control has proposed in [12]. The performance index can be divided into major cost functions, namely, quadratic function, linear function with constraints.…”

Controller Performance Analysis Technology for Industry: Implementation and Case Studies

“…(c) 3. The algorithms for MPC economic performance assessment and tuning guidelines have been introduced in [Xu et al, 2007]. Also, this APC performance analysis tool includes a sensitivity analysis, which provides a selective constraint/variability tuning guidelines for benefit improvement in practice [Lee et al, 2007].…”

“…The linear/quadratic coefficients are given in Table 1. Solving optimization problems specified in [Xu et al, 2007] for only input/output variables with 'on' status by using QP or SDP solvers, we calculate the economic performance costs J 0 = 688.8811 for the base case operation, J I = 686.5121 for the ideal operating condition, and J E = 686.5121 for the adjusted operating condition. Here, the process variables with 'off' status are not included in the optimization.…”