Assessment of control loop performance for nonlinear process
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Controller performance assessment (CPA) is concerned with the design of analytical tools that are utilized to evaluate the performance of process control loops. The objective of the CPA is to ensure that control systems operate at their full potential, and also to indicate when a controller design is performing unsatisfactorily under current closed loop conditions. Such monitoring efforts are imperative to minimize product variability, improve production rates and reduce wastage. Various studies conducted on process control loop performance indicate that as many as 60% of control loops often suffer from some kind of performance problem. It is therefore an important task to detect unsatisfactory control loop behavior and suggest remedial action. Such a monitoring system must be integrated into the control system life span as plant changes and hardware issues become apparent. CPA is well established for linear systems. However, not much research has been conducted on CPA for nonlinear systems. Traditional CPA analytical tools depend on the theoretical minimum variance control law that is derived from models of linear systems. In systems exhibiting dominant nonlinear behavior, the accuracy of linear based CPA is compromised. In light of this, there is a need to broaden existing CPA knowledge base with comprehensive benchmarking indices for the performance analysis of nonlinear process control systems. The research efforts presented in this thesis focuses on the development and analysis of such CPA tools for univariate nonlinear process control loops experiencing the negative effects of dominant nonlinearities emanating from the process. Two novel CPA frameworks are proposed; first a model based nonlinear assessment index is developed using an open loop model of the plant in an artificial neural network NARMAX (NNARMAX) representation. The nonlinear control loop is optimized offline using a proposed Nelder Mead-Particle Swarm Optimization (NM-PSO) hybrid search to determine global optimal control parameters for a gain scheduled PID controller. Application of the benchmark in real-time utilizes a synthetic process output derived from the NNARMAX system which is compared to the actual closed loop performance. In the case where no process model is available, a second method is presented. An autonomous data driven approach based on Multi-Class Support Vector Machines (MC- SVMs) is developed and analyzed. Unlike the model based method, the closed loop performance is classified according to five distinct class groups. MC-SVM classifier requires minimal process loop information other than routine operating closed loop data. Several simulation case studies conducted using MATLAB™ software package demonstrate the effectiveness of the proposed performance indices. Furthermore, the methodologies presented in this work were tested on real world systems using control loop data sets from a computer interfaced full scale pilot pH neutralization plant and pulp and paper industry.