Multi sensor control based on fuzzy logic Nikolay Brayanov 1) , Anna Stoynova 1) 1) Department of Microelectronics, Technical University of Sofia, Sofia, Bulgaria, n.brayanov@ecad.tu-sofia.bg,ava@ecad.tu-sofia.bg Abstract: Recently fuzzy logic has increasing its popularity. One of the main reasons is that it gives abstraction, so a system could be controlled, even if it is not fully describable. On the other hand, this approach simplifies integration of data from multiple sources and its proper usage. The paper is focused on a mature area of temperature control, so the data is homogenous and simple. This applicability demonstration is not limitation and the approach could be used for any type and quantity of data. The paper shows how usage of multiple sensors improves controlled parameter’s behavior, giving example with the automotive industry. In this type of products that consist of a network of sensors, it is beneficial to use all available data. The result of this research is analyzed, demonstrating better accuracy as result of a fusion of sensors. 1. INTRODUCTION Proportional–integral–derivative controller (PID) is the most common control algorithm used in industry and has been universally accepted in industrial control. Application of this control algorithm however is a specific task that requires understanding of PID theory and the best practice, but also the nature of the driven process [1]. In the best case algorithm could be optimized for one of the parameters overshoot, convergence time and oscillation (Fig. 1). Fig. 1.Optimization of PID control. From another point of view fuzzy logic based algorithms does not need this knowledge, but simply test data. A review of fuzzy methods in automotive engineering applications [2] shows the increasing part of control algorithms in automotive industry, implemented using fuzzy logic. The reason could be found in the possibility to overcome various nonlinear models and to use intuitive logical rules. Using a system with multiple sensors, benefits of this method become even bigger. In this paper, we present a multisensory-based control approach for stable and responsive control, optimizing the well-known quality parameters – overshoot, response time and convergence time. It demonstrates a relevant easiness of application of externally provided related data and the control improvement that it brings. Thus it states that there is no useless data and fuzzy control logic is a solution for utilizing all available information. Organizationally this paper continues with description of the problem and the techniques provided with PID and Fuzzy control mechanism. In written details it comes obvious the pros and cons of the methods. Then the idea of improvement is shortly described. After that in section Details are placed more details about the simulation and decision taken in order to guarantee objectivity of