Generic model control ( G M C ) takes care of parametric mismatch for underdamped, closed-loop specification, whereas robust generic model control (RGMC) can handle parametric mismatch for any closed-loop specifcation. But, neither GMC nor RGMC is capable of compensating for structural mismatch. In this study, adaptive GMC(AGMC) and adaptive RGMC (ARGMC) structures are proposed, and their effectiveness over GMC and RGMC is demonstrated with several examples. AGMC exhibits better performance over ARGMC, GMC, and RGMC in all the cases of no process/model mismatch, parametric mismatch as well as structural mismatch.Distillation adaptive generic model control (DAGMC) structure is also proposed for dual composition control of distillation. Since embedding of distillation statespace model in the basic GMC law is practically impossible, linear and nonlinear models are proposed with adaptation using distillation process data, and DAGMC is applied to two typical nontrivial distillation units. Nonlinear DAGMC exhibited better performance over linear DAGMC. In trod uc t ionThe strong nonlinearities of many chemical processes limit the application of model-predictive, linear, multivariable controllers. For the control of nonlinear processes, Economou et al. (1986) developed an internal model control (IMC) approach, which employs nonlinear models in the control strategy. This technique, however, required the user to develop complex numerical inverses to the process model.Recently, Lee and Sullivan (1988) introduced a generic model control (GMC) that employs a nonlinear process model directly into a control strategy. In a case study, Lee et al. (1989a) have demonstrated that the performance of GMC is superior over both traditional and modern control strategies for a forced circulation evaporator. GMC has been applied successfully to the temperature control of exothermic batch reactor for the startup and subsequent temperature maintenance (Cott and Macchietto, 1989). The process-model-based engineering approach (Cott et al., 1989) is shown to have several advantages over existing approaches, as process optimizers may be incorporated and integrated easily with the GMC-based controllers to provide an effective controller-optimizer system. GMC as the process-model-based control is applied for the p H control of wastewater (Williams et al., 1990). Lundberg and Bezanson (1990) have shown that GMC lacks robustness for critical and overdamped closed-loop specifications and therefore proposed a n enhanced robust GMC by using derivative feedback to take care of process/model mismatch. The enhanced robust GMC (RGMC) structure is similar to the model-predictive control structures-IMC (Garcia and Morari, 1982), MAC (Rouhani andMehra, 1982), and DMC (Cutler and Ramaker, 1979) in model error compensation, but is distinct in that RGMC estimates and compensates for the error between the process and process model output time derivative.The real process and process model for control can differ in two ways: one in which the structure of the pr...

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A control algorithm which has been acclaimed as the best algorithm for a real system may not be the best algorithm for a different real system. Therefore, various self-tuning algorithms for real distillation columns have been evaluated, in order to compare their performances. A variable forgetting factor algorithm is modified using a filter which permits the employment of one instead of two covariance matrices for distillation control. A cautious self-tuning control of SISO system is extended to MIMO system of distillation control. Multivariable self-tuning regulator, multivariable self-tuning controller and multivariable cautious self-tuning controller are implemented with modified variable forgetting factor for linear transfer function model, Waller et al. column, and rigorous non-linear model, Wood and Berry column. For distillation control, a multivariable cautious self-tuning algorithm with modified variable forgetting factor is much simpler than earlier reported algorithms. This has produced better results and demonstrated its effectiveness, even in the presence of noise when other adaptive controllers give unsatisfactory performance.