Model Deficiency Diagnosis and Improvement via Model Residual Assessment in Model Predictive Control

Abstract

To reduce the effort and cost of model maintenance in model predictive control (MPC) systems, this paper explored a model deficiency diagnosis and improvement method by the assessment of model residual and optimization of disturbance model. A model quality index (MQI) method was first presented to evaluate the model performance with the routine input and output process data. Based on MQI, a leave-one-out method was proposed to further assess the performances of submodels in multi-input–multi-output (MIMO) MPC processes. The root deficient submodel could be diagnosed based on a comparison of the overall MQI with the submodel index by moving it to the disturbance channel. Further, a model performance improvement method through the upgraded optimal disturbance model was proposed to enhance the model performance without altering the control model. The experiment results on the Wood–Berry distillation column process and an industrial process indicated the validity of the proposed model diagnosis and improvement method.

MPC-Based Offset-Free Tracking Control for Intermittent Transonic Wind Tunnel

“…Proposition 1: Consider the feedback gain matrix F and positive-definite matrix P satisfying ( 14) and (15). Then,…”

“…In practice, MPC is one of the most promising methods to a wide range of industrial processes, such as chemical, petrochemical, pulp, gas pipeline and metallurgical [11], [12]. At each sampling time, the controller computes an optimal control sequence and implements the first control input, then the entire optimization is repeated at subsequent sampling time [13]- [15]. Distinguishing from other conventional control methods, one of the most appealing features of the MPC is that it can drive the plant to the most profitable operating condition with constraint satisfaction [16].…”

Assessment of dynamic matrix control controller parameters via estimating Markov parameters of disturbance

“…Then, in this article,Ĥ is used to estimate H: the system Markov parameters of the disturbance model H can be computed by using Equations ( 12) and ( 14) along with Equation (18). It is noted that the accuracy of estimated Markov parameters only depends on the quality of the simulated observer.…”

“…Last year, our group published an article that explored a model deficiency diagnosis and improvement method by model residual assessment and optimization of a disturbance model. 18 These theoretical studies on the diagnosis of the root cause of control system performance degradation mainly focus on the detection of model performance deficiencies. The selection of controller parameters is also an important factor affecting the control system performance.…”

“…Sun et al 17 utilized model residual to design a model assessment benchmark. Last year, our group published an article that explored a model deficiency diagnosis and improvement method by model residual assessment and optimization of a disturbance model 18 . These theoretical studies on the diagnosis of the root cause of control system performance degradation mainly focus on the detection of model performance deficiencies.…”