Model predictive control for current balancing in a four-phase buck converter

Multiphase buck topology offers smaller ripple current and lower component ratings. This, however, compromises unbalanced output current between each phase of an inductor which leads to over-current and inductor saturation issues. Often when discussing the linear control schemes, it involves the use of superposition theorem to understand the system’s response. However, the limitation of superposition theorem in this application is that it assumes the circuit to be completely linear. For components with nonlinear behaviour such as power switches and diodes, the analytical results may not be accurate resulting to unexpected behaviour as the algorithm is implemented on a real system. Hence, the use of a more advanced control scheme is necessary to improve a system with a non-linear characteristic. This paper proposes a current limit control (CLC) consists of MPC for inner loop control and PID for outer loop control for phase current balancing in a four-phase buck converter. The controller is designed to achieve balanced current for each phase with acceptable response time. The proposed system is designed using MATLAB/Simulink simulation software and verified by a laboratory prototype with a TMS320F28335 as the main controller. Simulation and experimental results are provided to validate the system performance

Experimental implementation controlled SPWM inverter based harmony search algorithm

An optimum PI controller using harmony search optimization algorithm (HS) is utilized in this research for the single-phase bipolar SPWM inverter. The aim of this algorithm is to avoid the conventional trial and error procedure which is usually applied in finding the PI coefficients in order to obtain the desired performance. Then, the control algorithm of the inverter prototype is experimentally implemented using the eZdsp F28355 board along with the bipolar sinusoidal pulse width modulation (SPWM) to control the output voltage drop under different load conditions. The proposed overall inverter design and the control algorithm are modelled using MATLAB environment (Simulink/m-file Code). The mean absolute error (MAE) formula is used as an objective function with the HS algorithm in finding the adaptive values of  and  parameters to minimize the error of the inverter output voltage. Based on the output results, the proposed voltage controller using HS algorithm based PI (HS-PI) showed that the inverter output performance is improved in terms of voltage amplitude, robustness, and convergence rate speed as compared to PSO algorithm based PI (PSO-PI). This is to say that the proposed controller provides a good dynamic responses in both cases; transient and steady-state. Finally, the experimental setup result of the inverter controller is verified to validate the simulation results

Switching speed improvements in multiphase buck converter via two-shunt voltage-sources

Multiphase Buck converters are well known for their smaller ripple current, smaller filter design and smaller component ratings. The converter operating responses however depend on the value of inductor and load resistance. Increasing the switching frequency can improve the transient responses. However, it reduces the system efficiency and controlling the current response can be complex. This paper proposes an improved multiphase buck converter for a lithium-ion battery charging applications. The proposed system is based on the ‘Bi-level drives’ concept, which requires two input voltage in which one is higher than the other input and is connected in series to the system. By driving the load with a higher input voltage, the transient response is observed to be much faster. Upon reaching the desired reference output, the system will revert to a lower input voltage, thus, the efficiency of the system will not be affected. This scheme requires additional two power switches and a power diode. The operation and the switching scheme will be discussed in this paper

Offering and hospitality in Arabic and English

This paper examines the conventional linguistic practices involved in everyday hospitality situations. We compare offers in Arabic and English and, rather than focusing on the differences between the ways interactants in these two cultures make offers, we challenge the notion that offering is in essence differently handled in the two languages. We argue instead that we should focus just as much on the similarities between the ways offers are made, since no two cultural/linguistic groups are diametrically opposed. Furthermore, no cultural or linguistic group can be argued to be homogeneous. Through a detailed analysis of four naturally occurring hospitality encounters, we explore the nature and sequencing of offering and receiving hospitality in each cultural community and discuss the extent to which offers and refusals are conventionalized in each language. In this way we hope to develop a more contextual discursive approach to cross-cultural politeness research. Drawing on Spencer-Oatey's notion of sociality face, we examine the conventions for being hospitable in order to appear sincere. A qualitative analysis of the data reveals that, while there are similarities in offering behaviour in both English and Arabic, in Arabic, the interactional moves of insisting and refusing are slightly more conventionalized. This however does not constitute a radical difference between the offering norms of these two cultural groups

Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model.

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning

Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

Recently, renewable energy (RE) has become a trend in power generation. It is slowly evolving from an alternative energy source into the main energy source. The technology is currently working as an auxiliary to the existing generators. Demands for electricity is expanding rapidly nowadays, which require generators to run near its operation limit. This activity put grieve risk to the generators. Nonetheless, the extensive analysis should be conducted upon RE integration into the existing power system. This paper assesses its economic impact on the power system. Setting up RE technology such as photovoltaic and wind turbine are costly, yet may reduce generator’s fuel cost in the long run. Thus, economic load dispatch (ELD) is conducted to compute the operating cost of power system with the integration of RE system. In this study, the operating cost represents the fuel cost of conventional fossil-fuel generators. Furthermore, a novel optimization technique namely Differential Evolution Immunized Ant Colony Optimization is proposed as the optimization engine. Comparative studies are conducted to assess the performance of the proposed approach

Neuro-fuzzy modeling of a conveyor-belt grain dryer

The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model