Showing papers by "Gade Pandu Rangaiah published in 1999"
TL;DR: In this paper, Luus and Jaakola's random search optimization (LJ) was used to find the global minimum in only a few seconds of computation time on a personal computer.
44 citations
TL;DR: In this article, an adaptive internal model control (AdIMC) for a class of minimum-phase input-output linearizable nonlinear systems with parameter uncertainty is presented, where the parameter adaptation is based on process and model outputs, and the state variables predicted by the model only.
34 citations
TL;DR: In this article, a new time delay compensation strategy for single-input single-output nonlinear processes subject to modeling uncertainty is proposed to reduce the effect of modeling errors on the performance and robustness.
Abstract: The dynamic behavior of many processes is characterized by time delays due to transportation lags and measurement delays, which put severe limitations on the performance of control systems. In this paper, a new time delay compensation strategy for single-input single-output nonlinear processes subject to modeling uncertainty is proposed. At first, controller design based on input−output feedback linearization for a class of nonlinear systems with an input time delay is presented. Then, the input−output linearization controller is used in an internal model control (IMC) structure with time delay compensation. Finally, IMC with feedback compensation is proposed to reduce the effect of modeling errors on the performance and robustness. An adjustable parameter in the feedback compensation can be tuned to satisfy a particular specification. The effectiveness of the proposed method is illustrated via simulation on the temperature and concentration control of a nonisothermal chemical reactor with an input delay.
17 citations
TL;DR: In this paper, the authors evaluate two strategies (augmented internal model control, AuIMC and adaptive internal model controller, AdIMC) for enhancing pH control by NIMC.
Abstract: Control of neutralization processes is very difficult due to nonlinear dynamics, different types of disturbances and modeling errors. The objective of the paper is to evaluate two strategies (augmented internal model control, AuIMC and adaptive internal model control, AdIMC) for enhancing pH control by nonlinear internal model control (NIMC). A NIMC controller is derived directly from input output linearization. The AuIMC is composed of NIMC and an additional loop through which the difference between the process and model outputs is fed back and added to the input of the controller. For the AdIMC, an adaptive law with two tuning parameters is proposed for estimating the unknown parameter. Both AuIMC and AdIMC are extensively tested via simulation for pH neutralization. The theoretical and simulation results show that both the proposed strategies can reduce the effect of modeling errors and disturbances, and thereby enhance the performance of NIMC for pH processes.
10 citations
TL;DR: Hu and Rangaiah as discussed by the authors proposed feedback augmentation for nonlinear IMC (hence named Augmented IMC, AuIMC) for improving control in the presence of modelling errors.
Abstract: Internal Model Control (IMC) and Model Predictive Control (MPC), the two most important members of model based controllers, are favourable alternatives for control of nonlinear processes. However, the performance of these controllers deteriorates drastically in the presence of substantial process-model mismatch. Hu and Rangaiah (1998) proposed feedback augmentation for nonlinear IMC (hence named Augmented IMC, AuIMC) for improving control in the presence of modelling errors, and demonstrated its success on a neutralization process. In the present study, IMC, MPC and AuIMC strategies are tested in a more difficult case of multi-input multi-output (MIMO) operation of a highly nonlinear continuous fermenter. A new control configuration is introduced as the conventional configuration is not applicable. Simulation results for different modelling errors show that IMC is better than MPC for fermenter control. The advantage of augmentation as in AuIMC manifests in the significantly improved regulatory control of the fermenter.
4 citations