## 1 Introduction  
Modern control systems are organized in a hierarchical structure. The process under control is situated at the lowest level of this hierarchy. The next level in the hierarchy is the instrumentation layer which enables upper levels to communicate with the process. The regulatory control layer is organized around basic univariate loops that most typically utilize PID-based control algorithms. The ubiquitous PID controllers represent more than 95% of the utilized algorithms [1, 2]. Although widely used, the performance achievable with the PID-based control law is limited. More complicated controllers, such as multivariate, non-linear, predictive, adaptive or algorithms that incorporate soft computing techniques fall under the general category of advanced process control (APC). This class of APC algorithms can be used in conjunction with PID-based algorithms to extend the control capabilities and effectiveness of conventional PID-only based loops. The majority of model predictive control (MPC) implementations utilize this hybrid approach of incorporating a PID controller in conjunction with an MPC algorithm. However, in some cases MPC plays the role of a regulatory control without any downstream PID loops. The supervisory level typically incorporates a superset of capabilities of the regulatory level, such as process optimization, economic planning and long-term scheduling. Industrial control systems frequently do not perform effectively [3] due to many reasons such as: inadequate supervision, process non-stationarity, instrumentation failures,incorrect design, poor tuning, changing operating points, lack of engineering expertise, disturbances, process and/or measurement noise or human factors [4]. Efforts performed by plant personnel to remedy these issues are often ad hoc and typically prove to be ineffective. Scientists and practitioners continually strive to develop automatic and autonomous solutions to address these problems. Control performance assessment (CPA) efforts start with simple univariate PID-based loop assessments. A comprehensive report was proposed by [PERSON] [5] in 1967, for a pulp and paper plant using process variable standard deviation benchmarking. Control assessment solutions have evolved in many directions over the past years and have successfully provided mature approaches, measures, and procedures to the industry. A search of the literature yields various, and often overly complicated, methods and approaches to CPA. We believe that taking an industrial perspective can simplify the problem to a search for a pragmatic methodology that can be applied to industrial control systems. Simplicity of the CPA methodology is key to the effectiveness and success of any given methodology. Success is achieved by reducing the scope of required a priori knowledge necessary to employ a selected approach. Methods that do not require specific knowledge can be simply evaluated as they tend to deliver clear and simple results. These simpler methods do not disturb the process with any externally injected inputs. A further categorization of methods is data-driven vs. model-based. Data-driven methods only require operational plant data where model-based approaches always require some initial assumptions such as the model type or structure. The selected methodology must be robust in that it must be independent of the existing loop characteristics as well as the statistical properties of the assessed variables. The ultimate goal is to measure the internal control quality in a manner that is not affected by noise, disturbances or other possible plant influences of any origin [6]. This paper focuses on a model-free approach based on the control error signal. The advantage of the controller error variable over other control loop signals is that it provides continuous information on the control quality and is independent of the process operating point or setpoint changes.