Showing papers by "Robert Babuska published in 1997"
TL;DR: Comparisons with a nonlinear predictive control scheme based on iterative numerical optimization show that the proposed method requires fewer computations and achieves better performance.
154 citations
TL;DR: In this article, the authors investigated the use of GAs for optimization in nonlinear model-based predictive control, where advanced genetic operators and other new features were introduced to increase the efficiency of the genetic search in order to deal with real-time constraints.
122 citations
01 Nov 1997
TL;DR: This paper focuses on local approaches to modeling of complex nonlinear systems, which are conceptually simple and intuitively appealing, as they are close to the way human solve problems.
Abstract: There are several different approaches to modeling of complex nonlinear systems. The main distinction can be made between global and local methods. Global methods describe the system under study using nonlinear functional relationships between the system’s variables. Examples are nonlinear state space models or input-output black-box models such as the popular NARX (Nonlinear AutoRegressive with eXogenous input) structure used often in connection with neural or wavelet networks. Local approaches, on the other hand, attempt to cope with complexity and nonlinearity of systems by decomposing the modeling problem into a number of simpler, in most cases, linear sub-problems (Johansen and Foss, 1993; Banerjee et al., 1995). These methods are conceptually simple and intuitively appealing, as they are close to the way human solve problems. Local models are usually more easily interpretable than complicated global models.
102 citations
TL;DR: A knowledge-based fuzzy model for performance prediction of a rock-cutting trencher has been developed using expert knowledge coded as if-then rules, hierarchically organized in four rule bases to predict the production rate and bit consumption in terms of qualitative linguistic values.
57 citations
01 Jul 1997
TL;DR: This paper presents an identification procedure for a Takagi-Sugeno fuzzy model, which is based on product-space fuzzy clustering, and can be inverted analytically and hence can be easily included in a nonlinear IMC scheme.
Abstract: Fuzzy models can represent highly nonlinear processes and can smoothly integrate a priori knowledge with information obtained from process data. A nonlinear controller can be designed by incorporating an inverted fuzzy model of the process in an internal model control (IMC) scheme. This paper presents an identification procedure for a Takagi-Sugeno fuzzy model, which is based on product-space fuzzy clustering. The obtained model can be inverted analytically and hence can be easily included in a nonlinear IMC scheme. The described method is applied to temperature control in an air-conditioning system. The performance is compared with the performance of a well-tuned PID controller.
19 citations
01 Jan 1997
TL;DR: This chapter considers cluster validity and cluster merging techniques for determining the relevant number of rules for a given application when fuzzy clustering is used for modeling.
Abstract: Redundancy may be present in fuzzy models which are acquired from data by using techniques like fuzzy clustering and gradient learning. The redundancy may manifest itself in the form of a larger number of rules than necessary, or in the form of fuzzy sets that are very similar to one another. By reducing this redundancy, transparent fuzzy models with appropriate number of rules and distinct fuzzy sets are obtained. This chapter considers cluster validity and cluster merging techniques for determining the relevant number of rules for a given application when fuzzy clustering is used for modeling. Similarity based rule base simplification is then applied for reducing the number of fuzzy sets in the model. The techniques lead to transparent fuzzy models with low redundancy.
16 citations
TL;DR: In this article, a linear model based predictive control strategy based on feedback linearization is applied to the control of the pH in a penicillin conversion process, which is a nonstationary and time-varying process.
2 citations
TL;DR: In this paper, a semi-mechanistic and a black-box approach are applied to the modeling of an enzymatic conversion of Penicillin which takes place in a fed-batch reactor.
1 citations
TL;DR: A method for complexity reduction of a single-input, single-output (SISO) Takagi-Sugeno model is proposed, based on a local measure of the second-order derivative of the fuzzy mapping of the input universe of discourse.