Performance Assessment of Predictive Control—A Survey

Abstract

Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators

“…However, the linearization is created based on the approximated procedure and it does not take into account the full dynamics of the real system. Among these control techniques, the model predictive control (MPC) provided good performance for a lot of engineering applications [14].…”

Impact of MPC Embedded Performance Index on Control Quality

“…It must be noted that the outlier robustness aspect is not as such addressed. On the other hand, robust indexes are successfully used in the MPC control performance assessment [31]- [33], which is independent of the controller operation, however, should reflect predictive operation.…”

Tuning of Multivariable Model Predictive Control for Industrial Tasks

“…It is shown that the discussed tuning approach makes it possible to use very good control quality, much better than in the most common case in which all tuning parameters are constant. The multicriteria control assessment is used since the control quality is assessed taking into account three factors: the sum of squared errors, the Huber standard deviation and the entropy [36,37].…”