فصلنامه روشهای هوشمند در صنعت برق
پیاپی 58 (تابستان 1403)

In this paper, we investigate a hybrid controller for wheeled mobile robots in the presence of external disturbances and parametric uncertainty. Robot models include kinematic and dynamic equations of motion. In this paper, in order to reach the final position, the wheeled moving robot must be controlled in such a way that it can follow a reference path. Many studies often use a motion control strategy for the wheeled mobile robot. In this study, the proposed control strategy has two stages including cinematic control and dynamic control. In this regard, first after introducing the kinematic model of the robot, we design a predictive controller for this part and prove it. Then, based on the nonlinear dynamic dynamics of the robot, an adaptive sliding mode dynamic controller is introduced to estimate the disturbances online, automatically adjust the gain of the control and eliminate the umbrella phenomenon completely. Then, the proposed design is analyzed and proved using Lyapanov's theory of stability. According to the proposed adaptive control law, optimal convergence and tracking performance of all signals are guaranteed and tracking errors can converge arbitrarily in finite time to the source. Simulation results have been performed to show the effectiveness of the proposed design using Matlab software.

The purpose of this paper's prediction of survival or death within 30 days is based on a cerebral hemorrhage. Timely and correct diagnosis and treatment of cerebral hemorrhage are essential. If the patient's death is predicted during these thirty days, the treating physician should use intensive care and more treatment for the patient. Cerebral hemorrhages require immediate treatment and rapid and accurate diagnosis. In this article, using the volume of cerebral hemorrhage and the patient's age and using the neural network of support vector machine (SVM), it is predicted what percentage of people with cerebral hemorrhage survive and what percentage die. Parameters of cerebral hemorrhage volume and, age of patients, neural network input are considered. The network's output is the survival or death of patients with cerebral hemorrhage over the next thirty days. The data we used included the bleeding volume and age of 66 patients with lobar hemorrhage, 76 patients with deep bleeding, nine patients with Pontine hemorrhage and 11 patients with cerebellar hemorrhage. All bleeding models are considered as input to the support vector machine neural network. The overall accuracy of the designed support vector machine neural network is 93%. Regardless of the type of cerebral hemorrhage, the survival or death of people with cerebral hemorrhage within 30 days is predicted.

Wireless mesh networks are appropriate and cost-effective infrastructure for Internet but due to the limited scalability and capacity, a lot of research has been doing on new ways to improve these limitations such as optimization of scheduling, routing, etc. In this paper focusing on time division multiple access (TDMA) method, a new algorithm called a</strong>nt colony for l</strong>ink s</strong>cheduling in m</strong>esh networks (ALSM) based on Ant Colony algorithm is proposed which reduces the possibility of collision to zero by scheduling links. In this algorithm, we will try to size super frames and assign each link to a time slot in such a way that limitations are satisfied and finally, the end-to-end latency is minimized. In multi-input multi-output (MIMO) networks, we face two types of interference (weak and strong). In ALSM, the ant colony has been modified in such a way that the optimal timing of the links can be obtained by considering these two types of interferences. Our results show that ALSM algorithm can schedule links with shorter super frames compared to other recent algorithms.

Restructuring of power systems and integration of different renewable energy sources with complex dynamic behaviors and high structural uncertainties has made the issue of load frequency control more important. For a hybrid power system that includes a thermal power plant taking into account nonlinear limitations such as the governor dead band and generator rate constraints and renewable energy sources including a wind turbine, solar-thermal power plant, electrolyzer, fuel cell, and plug-in electric vehicle, this paper proposes an adaptive wavelet neural network fractional order PID controller (AWNNFOPID) based on self-recursive wavelet neural networks and fractional order PID controller. To compare the performance of the proposed AWNNFOPID controller, four different scenarios are considered and the simulation results are compared with traditional I, PI, and PID controllers as well as with the optimized FOPID controller. The simulation results show that the proposed AWNNFOPID controller has better performances than the other control strategies used for the studied hybrid power system based on performance indicators such as settling time, rise time, maximum overshoot, maximum undershoot, integral time absolute error (ITAE), and integral absolute error (IAE).

This paper presents the optimal participation of distribution networks and energy hubs in the day-ahead wholesale and retail energy markets. The proposed scheme is a two-objective optimization model. In one objective function, it minimizes the energy cost of electricity, gas, and heating network as private distribution companies in the mentioned markets. In another objective function, it minimizes the energy cost (equal to the difference between selling and purchasing energy) of hubs in the retail market. This scheme is subject to optimal power flow formulation in the mentioned networks, and the operation model of sources and active loads in a hub format. Then, the Pareto optimization based on the weighted functions method according to the fuzzy decision is used to achieve the optimal compromise solution. Finally, by implementing the proposed scheme on a system test, the obtained simulation results confirm the capabilities of the scheme in improving the economy of energy hubs and the economic and operation situation of the mentioned networks.

Implementation of neuromorphic computing systems (NCSs) using digital and analog circuits occup ies a high chip area and consumes high power. With the advancement of nanotechnology, the hybrid Magnetic tunnel junction/Complementary metal–oxide–semiconductor (MTJ/CMOS) circuits have made it possible to implement NCSs with higher density and lower power consumption. However, still there is a gap between the performance of the human brain and NCSs. To mitigate this gap, it is essential to further decrease the energy consumption and the delay of the NCS. The high energy consumption of the MTJ-based NCS is mostly related to the high current needed to switch the MTJ state. Hence, some previous methods tried to perform real-time tracking of the MTJ state by monitoring its voltage and cutting off its current immediately after switching. However, due to the small voltage changes after switching, these methods suffer from a high-power consumption (they need power-hungry amplifiers). In this paper, a new method based on the tracking of MTJ current (instead of voltage) and terminating the MTJ current after switching is proposed. Due to the large changes in the MTJ current after switching (about 40%), there is no need to use an amplifier in the proposed circuit. Therefore, the conventional voltage-mode sensing circuit is replaced with the proposed circuit, to improve the energy efficiency, speed and delay of the NCS. In all state-of-the-art designs, the voltage changes on nodes across the MTJ (PL, FL or both of them) have been used to detect the MTJ switching. However, the proposed circuit detects the MTJ switching by properly sensing the MTJ current and terminates its current immediately. The simulation results in 65-nm CMOS technology confirm that the proposed technique improves the energy consumption and speed of the NCS by 49% and 2.1X compared with the typical NCS.

In recent years, metropolises around the world have seen a declining trend in the air quality index. Much of this pollution is related to public transportation. Upgrading public transportation can be a way out of this impasse. Due to environmental concerns, it is recommended to switch from conventional diesel buses to electric buses, which have several benefits in terms of reducing pollution, noise and fuel. In this paper, a high power induction switched reluctance motor used in a city electric bus is studied. The stator and rotor structure of this electric car is non-segmental. The structure is such that a short magnetic flux path is created in the rotor and stator core. As a result, high torque is produced with low losses. Since the electric motor has a very high power, it therefore requires a large amount of permanent magnets, so it is desirable that the electric motor in the electric bus does not have a permanent magnet. Here is a three-phase induction switched reluctance motor with a power of 220 kW, with 6 stator poles and 4 rotor poles. A two-dimensional finite element model is designed and its magnetic analysis is performed. The flux path, torque and efficiency of the induction switched reluctance motor are calculated and the results are presented.

Following the scarcity of non-renewable resources such as oil, gas and coal, more research is focused on the issue of high energy consumption and society's dependence on fossil fuels. The use of renewable energy and the development of microgrids can be necessary to reduce dependence on fossil fuels. Photovoltaic systems play a key role in microgrids as a source of renewable energy supply. In these systems, the output voltage of the cell is usually much lower than the voltage required by the DC bus, and as the output current increases, the amount of this voltage decreases significantly. Therefore, the presence of a step-up DC-DC converter with a wide input voltage range is necessary to connect the low-voltage cell source and the high-voltage DC bus connected to the inverter. In this paper, a new structure is presented for non-insulated DC-DC boost converters based on voltage lift technique. The proposed converter has a proper voltage gain at output and an acceptable voltage stress on switch and diodes in comparison with recent references. The proposed converter has a switch with easier control and high reliability due to the input source common point and the output load of the converter. Analysis of the voltage stress as well as selection of suitable elements along with converter analysis in continuous mode are performed and calculation validity is confirmed by presented laboratory results.

The development of electric vehicles (EVs) will increase power transmitted through the distribution grid. Such an effect leads to accelerated aging in grid equipment, including pole-top distribution transformers. In the form of preventive and corrective measures, it is possible to centrally manage the charging operations of a group of electric vehicles connected to a specific pole-top transformer (through the distribution system operator or an independent aggregator). This paper presents a centralized model to co-optimize transformer loss-of-life with benefits for charging and discharging management of consumers' electric vehicles. The proposed model is compared to the decentralized model, in which EV owners optimize their benefits without considering the damages to the transformers. The centralized and decentralized strategies were applied to a small local grid with a maximum number of 6 EVs connected to a local pole-top transformer and implemented and solved using GAMS software. The results show the benefits of the centralized strategy in balancing the grid assets, while consumers' arbitrage benefits are slightly reduced.

Design of complex analog integrated circuits requires the appropriate choice of various design parameters such as MOSFET’s aspect ratio, compensation capacitance and load capacitance in a way that improves user’s desired parameters like gain, bandwidth, power dissipation and phase margin. Considering previous works, in this paper, a two-stage miller compensated operational amplifier with PMOS input pair is designed using artificial neural network. The inputs of the neural network are design parameters including DC gain, bandwidth, power dissipation and phase margin and in its output, the sizing of transistors and the amounts of reference current supply, compensation capacitance and load capacitance are acquired. In this design method, a sampling method based on parallel HSPICE simulations is employed for data acquisition from the 15-dimensional design space which results in simplicity and automation of the dataset collecting procedure and reduces the total sampling time and then this data is used for training the neural network model. In the next stage, a range sampling method is applied for making new designs from the trained model which has facilitated the design procedure and made the user-desired tradeoffs between different performance parameters of the operational amplifier possible. Moreover, if the amplifier performance figure of merit (FOM) is defined as the result of the multiplication of unity gain bandwidth and load capacitance divided by power consumption, the comparison between obtained designs of this paper’s proposed method and the results of some other methods applied for designing operational amplifiers with relatively similar topologies in previous works, indicates that this parameter has increased by 154% at the minimum.