Control performance assessment (CPA) began to blossom in the late1980s, and it is an important asset-management technology in the field of industrial process automation. The main objective of CPA is to evaluate the performance of controller without disturbing the normal operation of the control systems, and find the lack with respect to the theoretical best optimized contoller. When the performance of a running controller deviates from the desired aim, we must determinate the reasons of bringing poor performance and point out what should be done to improve the controller. The technology of CPA plays a significant part for maintenance industrial automatic systems with safety and high efficiency work. The application of CPA has been registered in the industrial process fields of petrochemicals, water treatment, ferment engineering, pharmacetical engineering, and so on.There are many researches and industrial applications in control performance assessment. However, because CPA is a synthetical technology, the existing studies are far from containing all the stages. Even in the first step of CPA study that central task is to select performance benchmarks and estimate the performance of running controllers, the most existing methods work on the assumption that the control systems are linear processes. The papers for nonlinear systems in performance assessment have been infrequently written due to its complexity. For the above mentioned problems, this thesis will focus on the following research topics within the framework of minimum variance benchmark.(1) A switching strategy of control performance assessment based on minimum variance is proposed. Many practical industrial processes can be seen as linear systems at the beginning of running, but with the passage of time, the control loops possibly include nonlinearites from external influence. The estimates of most traditional CPA methods will bring mistake because of failing to notice the nonlinearites. The nonlinearity detection tests based on higher order statistics are used to quantify the nonlinear property presented in the output signals before selecting performance assessment methods. If nonlinearity index(NLI) shows that the control systems are linear, then traditional linear performance assessment methods are adopted, otherwise the new nonlinear method is used. Farther, the problem of nonlinear minimum variance performance assessment is converted into model identification with the help of Volterra series. Finally, a simulation example indicates that the proposed algorithm gives better minimum variance performance bound (MVPLB) compared with existing methods for linear system performance assessment.(2) The technology of variance analysis is introduced to the study of control performance assessment for nonlinear systems. First,.a performance estimated method for a specific class of SISO nonlinear systems is proposed. Subsequently, the similar method is expanded into the performance assessment of nonlinear feedforward/feedback control systems. For a class of nonlinear SISO processes that can be described by the superposition of a nonlinear dynamic model and additive linear or partially nonlinear disturbance, the expression of the minimum variance performance lower bound is derived and the conclusion that the MVPLB only depends on the driving forces of most recent ahead disturbances is obtained. Then, the model of closed loop system is identified by using orthogonal least square algorithm. Moreover, based on the achieved model, the minimum variance performance index of nonlinear systems is calculated by using variance decomposition formula. In addition, the existence of MVPLB for two nonlinear feedforward/feedback control systems with different structure is proved in this thesis. And, whether it is appropriate that the variance analysis technology can be used for evaluating the performance of nonlinear feedforward/feedback control systems is analysed. Then, the problem of performance assessment of nonlinear feedforward/feedback control systems is solved with the help of iterative orthogonal least squares identification method. Finally, a simulation example indicates that the algorithm proposed in this paper gives better effect compared with the existing methods.(3) To the shortage of current performance assessment methods for nonlinear systems based on generalized minimum variance (GMV) benchmark, a novel performance index is explored and exploited. The actual application of minimum variance controller is difficult because of the violence of control signal changes and the worst robust performance. Due to the introducing of error and control weighting terms, the magnitude of GMV controller can be restricted. For the present study of estimating the performance of nonlinear GMV control systems, the traditional linear methods are directly adopted. These linear methods are effective because of the output signals of nonlinear control systems in effect of theoretical nonlinear GMV controllers can be described by linear moving average processes. However, this precondition may be easily distorted by model changes and external disturbances in practice, so the results of linear performance assessment methods are inaccurate or at all meaningless. On the basis of variance analysis technology, the configuration of variance of closed-loop output signals is analysed, and a nonlinear GMV control performance assessment method of nonlinear systems is proposed. Finally, a simulation example indicates that the algorithm is effective. |