# Performance Assessment of Predictive Control--A Survey
## 3 Control Performance Assessment  
Industrial control systems frequently do not perform effectively [65] due to many reasons, for instance: inadequate supervision, process non-stationarity, instrumentation failures, incorrect design, feeble tuning, changing operating points, lack of engineering expertise, disturbances, noises, or human factor [66; 67]. Maintenance performed by plant resources is hardly sufficient. Scientists and practitioners continually try to develop an automatic and autonomous solution that would solve the problem. CPA adventure has started with simple univariate PID-based loop assessment. The first adequate report was proposed by Astrom [68] for a pulp and paper plant in 1967 using the benchmarking of process variable standard deviation. Control assessment solutions have evolved for more than 50 years in different direction, delivering to the industry mature approaches, measures, and procedures. There are many different representations of the industrial assessment process. Figure 3 shows generalized diagram of the exemplary CPA industrial utilization process. One may find a few methods' classifications in the literature and block tree diagrams visualizing functional similarities and differences. Figure 4 presents graphical diagram of the generalized CPA techniques classification. The industrial perspective simplifies the picture. Simplicity is the main borderline, i.e., the scope of required a priori knowledge for the utilization of a selected approach. Methods that do not require specific knowledge can be simply evaluated by delivering a clear message. In the literature, there exists basic classification, which might be applied to the MPC rule and to the quality assessment as well. Authors distinguish between data-driven and model-based approaches. There are fundamental traps in the popular interpretation of these notions. First, each method uses data. Without data there is no assessment and actually all methods are data driven. Thereby, one might distinguish between model-free and model-based approaches. From that perspective, the majority of techniques are model-based only apart from simple integral or time based. All the  
Figure 3: CPA industrial utilization process. statistical approaches are model-based, as evaluated measures originate from some probabilistic density function, which is in fact an assumed statistical model. Thereby, the notion of a model has to be specified. Common understanding is that it is a process model. Consequently, the following classification is used throughout the paper:  
* **Model-free** means that no process model is required. * **Process model-based** approaches require performing the modeling of the controlled plant. Therefore, model-free methods require only operational plant data, contrary to the process model-based approaches that always need some initial assumptions, as for instance model type or its structure. Moreover, the preferred methodology must be robust, i.e., it has to be independent on the existing loop characteristics and statistical properties of the assessed variable. The goal is to measure internal control quality, not affected by any noises, disturbances, or possible plant influences of any origin. Present control performance assessment research encompasses various domains and applications of control engineering. Different methods' categories have been investigated [9; 69; 70]. The classification listed below includes short descriptions addressing the above discussed simplicity issues:  
1. Methods requiring plant experiment: * measures that use setpoint step response, such as overshoot, undershoot, rise, peak and settling time, decay ratio, offset (steady state error), and peak value [71]; and * indexes that require disturbance step response, such as Idle Index [72], Area Index, Output Index [73], and R-index [74]. 2.