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attention has shifted toward data-driven fuzzy and neuro-fuzzy modeling and nonlinear model-based control. Figure 4: Genetic algorithms are based on a simplified simulation of the natural evolution cycle. The importance of effective model building tools for control can hardly be underestimated. Virtually all advanced control techniques require a reasonably accurate dynamic model of the plant. It is well known that the development of a nonlinear control-oriented model consumes a considerable part of the total project time and effort. By using fuzzy identification techniques, transparent and interpretable nonlinear models can be obtained through effectively combining partial physical knowledge with process data (gray-box modeling). Learning methods for fuzzy models have been developed that preserve semantic interpretability and transparency under numerical optimization of parameters in these systems. Our current research in the area of nonlinear model-based control proceeds along four basic lines: 1. robust gain-scheduling (automated identification of operating points and scheduling functions, robust local and global design), 2. predictive control (development of effective optimization techniques, robustness issues), 3. faulttolerant control (consolidation of signals from redundant sensors, analytical redundancy, soft sensors), 4. self-tuning and adaptive control systems for partly unknown and time-varying systems (supervision of adaptation, identification of nonlinear actuator characteristics). The main current application domains are bioprocess industry (modeling and adaptive control) and fly-by-wire aircraft control systems (gain-scheduling and faulttolerant control). The previous issue of Feedback included articles describing some of these activities in on going projects. Application examples from some of the past projects are: decision-support systems for various industrial processes (Henkel, Vermeer), nonlinear predictive controllers for indoor climate control (TNO Bouw), 10
nonlinear identification and pattern recognition in biomedical applications (Erasmus Medical Center), etc Future perspectives At this moment, the term ‘intelligent control’ is more like an umbrella for various modeling, control and optimization techniques replicating some aspects of biological intelligence, rather than a compact research discipline aiming at the development of systems with a high degree of machine intelligence. The progress in the development of truly intelligent control systems has been slower than the prominent researchers in the field predicted. There may be various reasons for this: High performance, low cost Automatic control, although hidden from the user, is present practically everywhere - in our homes, industry, communication systems, cars, etc. In many applications, control systems are becoming mission critical; a failure of the control system often means a failure of the system as a whole. Consequently, severe requirements are being imposed on the performance of control systems (ultimate stability and robustness, high accuracy and sampling rates, guaranteed real-time performance, etc.). On top of this, the development and production costs are kept as low as possible. As a consequence, we often build systems with the minimal possible number of sensors, actuators, etc., leaving little space for additional intelligent features. In contrast to the above, intelligent biological systems do not seem to be driven by this greedy strategy of low-cost, optimal, fail-safe performance and high accuracy at each and every step toward the final goal. Instead, they adaptively focus and defocus attention, conduct deliberate explorative actions that temporarily worsen the performance, but on the long term lead to improvement, unwillingly make mistakes that eventually facilitate more effective learning. By judging novel control paradigms according to established performance criteria and stability analysis methods, we may actually be hampering the progress in the field. What we may need to introduce are modified or refined performance requirements and criteria to assess intelligent control systems. Generic tools One of the main drivers in control theory is the development of generic tools, independent of the application domain. Consequently, control algorithms and design techniques are applicable to classes of systems, characterized by the dynamic properties of the mathematical model, thus abstracting from the actual physical process. This allows control engineers to reuse similar control concepts in completely different domains and without having detailed knowledge of the application. This approach obviously has many advantages, but it also imposes some limitations on the obtained solutions. First of all, no process exactly fits in one particular class (e.g., linear time-invariant, nonlinear input-affine, Hammerstein, Wiener, etc.) and thus approximations and simplifying assumptions have to be made. Secondly, one tends to select the control structure at an early stage, often on the basis of limited a priori information. The choice is then done based on one's background and experience from previous projects, without seriously considering other solutions. 11
Page 1 and 2: FEEDBACK PERIODIEK van het GENOOTSC
Page 3 and 4: Van het bestuur en redactie Na de a
Page 5 and 6: Oprichting Delft Center for Systems
Page 7 and 8: What is intelligent control? door R
Page 9: the form of if-then rules, in neura
Page 13 and 14: Introductie Rob Kemper door Rob Kem
Page 15 and 16: meer plaats nemen in een simulator
Page 17 and 18: door Hans Roubos Abstract - Dit art
Page 19 and 20: maken voor de beschikbare data, ech
Page 21 and 22: als ook CA degradatie een belangrij
Page 23 and 24: Internet Links: Download proefschri
Page 25 and 26: elektriciteit geeft. Niet alle afne
Page 27 and 28: In de praktijk wordt de programmave
Page 29 and 30: Het is dagelijks de zorg van het pr
Page 31 and 32: Subsidie In de beoordeling van subs
Page 33 and 34: Sportdag Na een veel te lange stilt
Page 35 and 36: Op 4 juni is Govert Monsees gepromo

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