• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.5
• /
• pp.602-608
• /
• 2011

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. The NN for the online feedback-error learning can composed that the input layer consists of six units for the inputs $x_i$, i=1~6, the hidden layer consists of two hidden units for hidden outputs $o_j$, j=1~2, and the output layer consists of two units for the outputs ${\tau}_k$, k=1~2. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels. The initial q value was set to [0, 5, ${\pi}$].

• Advances in Energy Research
• /
• v.1 no.2
• /
• pp.97-106
• /
• 2013

This study designs and tests a photovoltaic system with distributed maximum power point tracking (DMPPT) methodology using a field programmable gate array (FPGA) controller. Each solar panel in the distributed PV system is equipped with a newly designed DC/DC converter and the panel's voltage output is regulated by a FPGA controller using PI control. Power from each solar panel on the system is optimized by another controller where the quadratic maximization MPPT algorithm is used to ensure the panel's output power is always maximized. Experiments are carried out at atmospheric insolation with partial shading conditions using 4 amorphous silicon thin film solar panels of 2 different grades fabricated by Chi-Mei Energy. It is found that distributed MPPT requires only 100ms to find the maximum power point of the system. Compared with the traditional centralized PV (CPV) system, the distributed PV (DPV) system harvests more than 4% of solar energy in atmospheric weather condition, and 22% in average under 19% partial shading of one solar panel in the system. Test results for a 1.84 kW rated system composed by 8 poly-Si PV panels using another DC/DC converter design also confirm that the proposed system can be easily implemented into a larger PV power system. Additionally, the use of NI sbRIO-9642 FPGA-based controller is capable of controlling over 16 sets of PV modules, and a number of controllers can cooperate via the network if needed.