Prediction Error Methods Based Strategies For Control Loop Performance Assessment & Monitoring

Control Performance Assessment/Monitoring (CPA&M) is an important technology developed since the 80's of last century, which aims to monitor the system's performance by using only the normal route operating data without influencing the system's operation. Performance assessment results and root causes for performance degradation or deteriora-tion are supposed to report according to different controller benchmarks and performance indices, corresponding maintenance and improvement suggestions are expected to provide to control engineers. The technology is becoming more and more important for the main-tenance of industrial automatic with smooth, safety and high efficiency operation.Many of the current available CPA&M algorithms require considerable amount of process information, the time delay, process model or even the disturbance model, etc. Moreover, most algorithms are minimum variance control (MVC) based, which cannot provide reasonable performance assessment results for the model predictive control (MPC) systems. Currently, there are only a few results for the control performance assessment of nonlinear processes. For the above mentioned challenging problems, the thesis will focus on the following research topics in both theoretic and industrial application aspects:1. An extended prediction error method based algorithm is developed for the control performance monitoring of multivariable control systems. By using only the routine closed-loop operating data, moving average model of the system can be obtained, based on which the prediction error of the system can be calculated. Consequently a new closed loop potential index is developed. At different time delays the cor-responding potential indices can form a potential trajectory, which can reflect how much performance improvement potential the current control system can have. In-dividual potential index is also defined for each sub-loop, depending on which the performance of the sub-loops can be determined. The proposed algorithm can be used for the performance monitoring of control systems with different kinds of con-trollers (PID, MPC, etc.). Additionally, both the static performance and dynamic per-formance of the system are considered in the proposed closed-loop potential index, based on which a comprehensive tracking and regulating performance assessment result can be obtained.2. A multi-step prediction error based algorithm is presented for the control perfor-mance monitoring of MPC systems. The traditional prediction error methods are all based on the single-step optimal prediction, and essentially it is equal to the min-imum variance control. As for the objective function of MPC, multi-step optimal control action is supposed to be implemented within the prediction horizon, hence the multi-step prediction based benchmark should be more appropriate for the per-formance monitoring of MPC systems. The proposed algorithm first builds the re-lationship between single-step optimal prediction and multi-step optimal prediction; based on the latter a new closed-loop potential index is defined, which is prediction horizon related. With the fixed prediction horizon, a potential trajectory can be ob-tained via different time delays to reflect how much potential performance can be improved for the current control system in comparison with the optimal benchmark. Individual potential trajectory can indicate the performance status for each sub-loop. The above results can provide useful suggestions to control engineers and operators for the maintenance and controller tuning of MPC.3. A generalized minimum variance control benchmark based algorithm is proposed for the control performance assessment of multivariable control systems. To cope with the drawbacks of minimum variance control benchmark, such as high gain, wide bandwidth and unrealistically large control signal variations, etc., constraint on the control action is added to the objective function of generalized minimum variance (GMV) benchmark, which is similar to the objective function of Linear Quadratic Gaussian (LQG). However, in comparison with the constant weighting in LQG, dynamic weighting is used in GMV, based on which the weighting can be assigned not only depending on the importance of different variables but also depend on the importance of different bandwidths. Hence it can be served as a more realistic benchmark. The overall performance index and individual performance indices can both reflect the performance of the whole system and that of the sub-loops. The overall performance index can be obtained using only the closed-loop operating data, which avoids the requirement of process model and the estimation of the interactor matrix, and hence is convenient for the practical industrial application.4. A prediction error method based modeling and control performance assessment al-gorithm is developed for a type of linear parameter varying (LPV) process. Accurate process model can provide useful information to the application of control perfor-mance assessment. The widely used prediction error methods (PEMs) in the identi-fication of linear systems are extended to the modeling of LPV processes. By using the Box-Jenkins (BJ) model structure, both process model and disturbance model are considered. Under the PEMs framework, both the model interpolation based and parameter interpolation based LPV model structures can be effectively identified. With the obtained LPV model, optimal control strategy is applied to obtain the low bound variance, which can be served as the benchmark for the control performance assessment of the LPV process under different control strategies.5. A generalized prediction error method based algorithm is developed for the control performance monitoring of a class of nonlinear process. The practical industrial pro-cesses are essentially all nonlinear processes. For some complex processes the linear models cannot effectively capture the dynamic behaviors, while nonlinear models can provide more accurate process description capacity and more reliable process prediction ability. Orthogonal Least Squares (OLS) method is applied to the param-eter estimation of Nonlinear Polynomial AutoRegressive (NPAR) model with linear time invariant disturbance, based on which the prediction error of the nonlinear pro-cess can be estimated. Finally the nonlinear process potential index is defined con- (?)tering both the static and dynamic performance to achieve the purpose of control pedormance assessment of nonlinear processes.