This paper describes a web-based virtual laboratory that improves the automatic control teaching of undergraduate courses. This is achieved by optimizing the time the students spend in the real laboratory. The virtual world or three-dimensional scenes of real laboratory plants gives the students the opportunity to ''learning by doing'' through web-based systems at the location they want, and at the time they like. This virtual laboratory gives the students the tools to be more prepared and therefore, use the time more efficiently and effectively when working in the real laboratory plant. The student does not require the installation of special software on his remote computer; only need a common VRML enabled browser. An example of a developed virtual laboratory of interconnected tanks is presented.
Abstract. Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complexvalued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective H2/H∞ control problem.
This paper describes and approximated zeroing with an equivalent disturbance observer and predictive controller for drive systems.Then this linear controller structure is changed by fuzzy logic such that the controller makes the system respond quickly if the error e is large and vice versa in order to obtain a robust controller which is insensitive to both the plant noise and the observation noise. Next, a variable structure PI controller by fuzzy logic for drive systems is introduced. Then, this control scheme is implemented with neural networks. These controllers are realized with digital signal processors. The experimental results are given. Lastly, the applications of neuro fuzzy methods to inverters, converters, motion controls and sensors are explained.
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