An Algorithm for Tuning NMPC Controllers with Application to Chemical Processes

Abstract

The advantages of linear/nonlinear model predictive control (N)MPC for dealing with the multiple input multiple output problem, for performing optimization and for handling constraints are well-known and because of that it has been applied widely in the chemical industry. However, there is a recurrent problem for this kind of controllers and it is how to define the best tuning parameters to achieve a good closed-loop response. This is an open question for research even when the common practice is to define the (N)MPC tuning parameters by trial and error or by the expertise of the control engineer. There are some works that propose a systematic definition of the (N)MPC tuning parameters, but most of them are restricted to certain prediction models or do not define all the tuning parameters. This papers presents a general tuning algorithm for (N)MPC controllers that allows the systematic definition of all the tuning parameter and it is not restricted to a specific prediction model or to a problem formulation. It is based on multiobjective optimization for the definition of the objective function weights and on the optimization of a closed loop performance index for the definition of the horizon lengths. Three cases of chemical processes are considered in detail to illustrate the application and advantages of the proposed (N)MPC tuning algorithm.

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

“…However, its performance degrades when there are uncertainties in plant parameters and plant-model mismatch. With a fixed sampling interval and horizon values, optimization-based weight tuning procedures found to have improved performance as compared with the trial and error approach [16]. Controller matching [17] is one of the widely used techniques to determine these weight parameters.…”

“…If all the metrics are improved to the satisfactory level of the operator, then the operator selects none of the metrics (j = 0) and will terminate the algorithm. Weight corresponding to the identified weakly improved metric (w i=j ) is increased by a smaller increment (n i ) as in Equation (16), and weights of the other metrics (w i j ) are decremented equally in accordance to Equation (17) without loss of generality. GA-based MPC weight tuning is performed for this new set of fitness weights, and performance improvement is determined.…”

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

“…23 Several attempts to find an optimal solution using the NMPC/stochastic model predictive control (SMPC) algorithm were accomplished by many researchers within the field of controls. 21,24 Among them, frequently used methods are tube-based, min–max and multi-stage approaches. Adaptive NMPC for benchmark non-linear systems was proposed where parameters were estimated by a derivative-free Kalman filter (KF).…”

Tuning of Model Predictive Controllers Based on Hybrid Optimization

“…In Lozano and Gómez [18], a general tuning algorithm for nonlinear model predictive control (NMPC) was presented. The method was based on the utopia tracking concept in a multi-objective optimization problem to adjust the weights of the objective function.…”