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.
Highlights
Model Predictive Control [15] significantly contributes to the frequent usage of the Advanced Process Control (APC) in process industry
Minimum variance and normalized Harris index [75], Control Performance Index [76], and other variance benchmarking methods [77]; all types of the model-based measures [78], derived from close loop identification, such as aggressive/oscillatory and sluggishness indexes [79]; frequency methods starting from classical Bode, Nyquist and Nichols charts with phase and gain margins [69] followed by deeper investigations, such as with the use of Fourier transform [80], sensitivity function [81], reference to disturbance ratio index [82], and singular spectrum analysis [83]; and alternative indexes using neural networks [84] or support vector machines [85]
Multi-parametric quadratic programming analysis has been used to develop maps of minimum variance performance for constrained control over the state-space partition [124]; predictive DMC structures used to compare and assess implemented as a single controller or as a supervisory level over PID regulatory control [125]; orthogonal projection of the current output onto the space spanned by past outputs, inputs or setpoint using normal routine close loop data [126]; the infinite-horizon Model Predictive Control (MPC) [65]; Filtering and Correlation Analysis algorithm (FCOR) approach used to evaluate the minimum variance control problem and the performance assessment index [127]; and many others [122,128,129]
Summary
On-line performance monitoring, diagnostics, and maintenance play increasingly important roles and constitute inevitable aspects of good practices on site These aspects appear at the PID regulatory level, but they are crucial for APC [8,9] solutions, as advanced controls mostly operate close to the technological constraints. The implementation of APC predictive controllers is a complex task, taking more time and materials than the startup of a univariate PID loop [14] Such an installation is always preceded by and concluded with an assessment, which is used to justify the effort and calculate the benefits. The prime objective of the paper is to present the available control performance quality measures and approaches, which can be effectively used to assess real Model Predictive Control applications.
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