Showing papers by "Robert Babuska published in 2001"

Rule base reduction: some comments on the use of orthogonal transforms

Abstract: Comments on recent publications about the use of orthogonal transforms to order and select rules in a fuzzy rule base. The techniques are well-known from linear algebra, and we comment on their usefulness in fuzzy modeling. The application of rank-revealing methods based on singular value decomposition (SVD) to rule reduction gives rather conservative results. They are essentially subset selection methods, and we show that such methods do not produce an "importance ordering", contrary to what has been stated in the literature. The orthogonal least-squares (OLS) method, which evaluates the contribution of the rules to the output, is more attractive for systems modeling. However, it has been shown to sometimes assign high importance to rules that are correlated in the premise. This hampers the generalization capabilities of the resulting model. We discuss the performance of rank-revealing reduction methods and advocate the use of a less complex method based on the pivoted QR decomposition. Further, we show how detection of redundant rules can be introduced in OLS by a simple extension of the algorithm. The methods are applied to a problem known from the literature and compared to results reported by other researchers.

A fuzzy decision support system for traffic control centers

Abstract: We present a fuzzy decision support system that can be used in traffic control centers to provide a limited list of appropriate combinations of traffic control measures for a given traffic situation. The system is part of a larger traffic decision support system (TDSS) that can assist the operators of traffic control centers when they have to reduce non-recurrent congestion using a network-wide approach. The kernel of the system is a fuzzy case base that is constructed using simulated scenarios. By using the case base and fuzzy interpolation the decision support system generates a ranked list of combinations of traffic control measures. The best combinations can then be examined in more detail by other modules of the TDSS that evaluate or predict their performance using macroscopic or microscopic traffic simulation. At a later stage the fuzzy decision system will be complemented with an adaptive learning feature and with a set of fuzzy rules that incorporate heuristic knowledge of experienced traffic operators.

Fuzzy modeling with multivariate membership functions: gray-box identification and control design

Abstract: A novel framework for fuzzy modeling and model-based control design is described. The fuzzy model is of the Takagi-Sugeno (TS) type with constant consequents. It uses multivariate antecedent membership functions obtained by Delaunay triangulation of their characteristic points. The number and position of these points are determined by an iterative insertion algorithm. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. Finally, methods for control design through linearization and inversion of this model are developed. The proposed techniques are demonstrated by means of two benchmark examples: identification of the well-known Box-Jenkins gas furnace and inverse model-based control of a pH process. The obtained results are compared with results from the literature.

Accurate, Transparent, and Compact Fuzzy Models for Function Approximation and Dynamic Modeling through Multi-objective Evolutionary Optimization

Abstract: Evolutionary algorithms to design fuzzy rules from data for systems modeling have received much attention in recent literature. Many approaches are able to find highly accurate fuzzy models. However, these models often contain many rules and are not transparent. Therefore, we propose several objectives dealing with transparency and compactness besides the standard accuracy objective. These objectives are used to find multiple Pareto-optimal solutions with a multi-objective evolutionary algorithm in a single run. Attractive models with respect to compactness, transparency and accuracy are the result.

Efficient training algorithm for Takagi-Sugeno type neuro-fuzzy network

Abstract: The paper describes an algorithm that can be used to train the Takagi-Sugeno (TS) type neuro-fuzzy network very efficiently. The training algorithm is very efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than that the classical backpropagation algorithm (BPA). The proposed training algorithm is based on a slight modification of the Levenberg-Marquardt training algorithm (LMA) which takes into account the modified error index extension of sum squared error as the new performance index of the network. The Levenberg-Marquardt algorithm uses the Jacobian matrix in order to approximate the Hessian matrix and that is the most important and difficult step in implementing this LMA. Therefore, a simple technique has been described to compute first the transpose of Jacobian matrix by comparing two equations and thereafter by further transposing the former one the actual Jacobian matrix is computed that is found to be robust against the modified error index extension. Furthermore, care has been taken to suppress or control the oscillation magnitude during the training of neuro-fuzzy network. Finally, the above training algorithm is tested on neuro-fuzzy modeling and prediction applications of time series and nonlinear plant.

A multi-objective evolutionary algorithm for fuzzy modeling

Abstract: In this paper a multi-objective evolutionary algorithm with a single run is proposed in order to consider several objectives dealing with transparency and compactness in obtaining a fuzzy model besides the standard accuracy objective. In this way the use of Pareto-optimal solutions within the evolutionary algorithm let us obtain attractive fuzzy models with respect to compactness, transparency and also accuracy. The results of the combination of Pareto-based multi-objective evolutionary algorithms and fuzzy modeling are compared with other approaches in the literature.

Fault detection and isolation using multiple Takagi-Sugeno fuzzy models

Abstract: In this paper, soft fault detection and isolation (FDI) for nonlinear plants is addressed. A new method has been developed that combines fuzzy Takagi-Sugeno (TS) models, used for residual generation, with quadratic programming for residual evaluation and fault isolation. This method can isolate and identify total and partial failures, both single and multiple. The information from the FDI module can be used for control reconfiguration. One simulation and one real-time example are given to illustrate the functionality of the proposed approach.

Fuzzy gain scheduling for flight control laws

Abstract: The state-of-the-art methodology for the design of digital flight-by-wire flight control laws is based on dimming and linearization of a nonlinear aircraft model at selected operating points and subsequent tuning of linear control laws. Despite recent advances in the development of computer-aided control design toots, selection of the operating points and the design of the gain schedule still has to be done manually, in a heuristic manner. In order to reduce the design effort, an automated procedure has been developed. The number of operating points and their locations are determined automatically on the basis of the changes in the aerodynamics over the flight envelope, by using fuzzy clustering. This approach also directly provides the interpolation mechanism (membership functions) for the local control law parameters. The procedure has been developed in close cooperation with airframe and control system manufacturers and was evaluated in pilot-in-the-loop flight simulator tests.

Integrated Predictive Adaptive Control of Heating, Cooling, Ventilation, Daylighting and Electrical Lighting in Buildings

Abstract: The present energy consumption of European Buildings is higher than necessary, given the developments in control engineering. Optimization and integration of smart control into building systems can save substantial quantities of energy on a European scale while improving the standards for indoor comfort. Many tools are available for the simulation of one or some of the following aspects: (a) heating, cooling and indoor thermal comfort, (b) ventilation and indoor air quality, (c) daylighting, electrical lighting and light quality, (d) installations, local control and fault detection, (e) Genetic optimized Neuro-Fuzzy control. The interaction between these aspects, however, is very relevant and cannot be neglected. Therefore, an integrated software tool is required. TNO together with the University of Delft develops such an integrated tool. This paper describes the first results of the utilization of this tool and the development of an integrated, predictive, adaptive building system for indoor climate control.

Analysis of interactions in MIMO Takagi-Sugeno fuzzy models

Abstract: Input-output interactions in the inherently nonlinear Takagi-Sugeno (TS) MIMO fuzzy models can not be analyzed by using standard methods such as the relative gain array (RGA). Two methods are proposed that exploit the specific TS structure. The first one is based on a number of RGAs which can indicate sufficiently well the interactions in the model. Another tool to analyze input-output interactions is the output sensitivity, computed as a partial derivative of the output with respect to the considered input. The use of these techniques is illustrated with the help of a TS fuzzy model of a high-purity distillation column.

Fuzzy clustering for multiple-model approaches in system identification and control

Abstract: A review of fuzzy clustering and its use in the data-driven construction of nonlinear models and controllers is given The focus is on algorithms of the fuzzy c-means type Two application examples are presented: automated design of operating points for gain scheduling in flight control systems and nonlinear black-box identification In the latter case, a comparison with an alternative technique is given It is shown that fuzzy clustering is an effective technique for the decomposition of a complex nonlinear problem into a set of simpler local problems

Fuzzy decision support for the control of detergent production

Abstract: This paper describes a fuzzy decision support system (DSS) for the control of a detergent production process. The application has been carried out at a real-world, large-scale industrial production plant in the Netherlands, where a large variety of powder-based detergents for industrial users are produced in a spray drying process. The system consists of several fuzzy rule bases that model the control actions of experienced process operators in response to different quality deviations of the product. A hierarchical architecture of the fuzzy system is introduced to cope with the complexity. A fuzzy supervisor is used to deal with process constraints and to activate the applicable rule bases when control actions are needed. In this way, a system is obtained that enables the control of the process within stricter quality bounds than those applied by human operators alone. During in-production evaluation, the average improvement in the quality parameters for all product classes was above 30 percent. Copyright © 2001 John Wiley & Sons, Ltd.

Nonlinear model predictive control of a GDI engine

Abstract: A model predictive approach to the control of a GDI engine is presented. Fuzzy Takagi-Sugeno type models are used to predict the future engine behaviour. The optimization algorithm is based on instantaneous linearization of the nonlinear prediction model at the current operating point. Special mode switching strategies are designed to minimize the torque bumps during combustion mode changes. The performance of the controller has been evaluated on the European driving cycle using a dynamic simulation model, including powertrain, chassis and driver's submodels. Results have been achieved that show the applicability of the approach to the control of GDI engines.

Fault-tolerant output-feedback control via fuzzy state blending

Abstract: A fault-tolerant scheme for sensor faults in output-feedback control is developed by using standard linear design methods combined with try decision logic and 'soft' reconfiguration based on fuzzy state blending. The main advantages of the presented method are the use of standard deterministic control-oriented observers without the need to design special diagnostic observers for the different faults, and the simplicity and transparency of the decision logic. The control scheme is tolerant with regard to total and partial sensor faults occurring separately or simultaneously (under certain specified conditions). The decision logic gives information both on the localization of the fault and its severity. Experimental real-time results are presented for a laboratory system.

Fuzzy supervision of adaptive control with an application to bioreactors

Abstract: Supervised model-based self-tuning control of fermentation processes is addressed. The diversity, nonlinearity and time-varying nature of these processes make their control a challenging task. Conventional linear (PID) controllers with fixed parameters cannot meet the increasing performance requirements over the whole operating range. In the approach pursued in this research, a local linear model is identified at the current working point by using a limited amount input--output data obtained through an identification experiment. A linear controller is then tuned on the basis of this model. To minimize the intervention into the process operation, this tuning procedure is only initiated if the performance of the current controller deteriorates. To this end, a supervisory expert system is designed whose tasks are to monitor the process performance, design an appropriate identification experiment, validate the obtained model and tune the controller. The supervisory system is based on a combination of a state automaton with a rule-based fuzzy inference system. Experimental results have demonstrated the feasibility of this approach.