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This paper presents a new initial rotor position estimation method for brushless dc motors (BLDC). It consists in injection of four short voltage pulses at the motor terminals and then based on the evaluation of the voltage measured at the terminal of the non-energized (floating) stator phase, the position is estimated. The first three pulses are applied for a very short time to evaluate the rotor position while the fourth (last) pulse is used only for detection of magnet polarity. The estimation time of the position with an uncertainty of 180 o is independent of the motor electrical time constant, being less than 140μs, while the detection of magnet polarity could last longer (up to few milliseconds) until the iron core is saturated. The proposed method is proved only for salient pole BLDC motors (L d ≠L q ). The advantages of the proposed method are taught to be: quickness, simplicity, accuracy, low computation effort, and the most important: insensitive to the high voltage disturbances specific to the battery voltage in the vehicles. No current sensor is used for the proposed setup. Experimental results are provided to prove the concept and validate the solution.

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Temperature requirements of electronic control units used in automotive applications are constantly getting higher. This paper presents a simple driving strategy for the switching elements of BLDC (Brushless DC) motor control electronic unit which enables a better power consumption distribution between them, thus reducing the temperature peaks in the electronic equipment which limits its performance and lifetime. The driving strategy consists in alternating the freewheeling path between low and high-side for each consecutive PWM (Pulse Width Modulation) period. Therefore actively involving in the control of the motor all four switching elements of the bridge during one commutation period, in contrast to the classical low-side freewheeling method which uses only two or three (active freewheeling) switching elements.I.

The paper presents a new vision based algorithm for mobile robots path planning in an environment with obstacles. Cellular Neural Networks (CNNs) processing techniques are used here for real time motion planning to reach a fixed target. The CNN methods have been considered a solution for image processing in autonomous mobile robots guidance. The choice of CNNs for the visual processing is based on the possibility of their hardware implementation in large networks on a single VLSI chip (Cellular Neural Networks -Universal Machine, CNN-UM [2], [11]).