In this study, we propose a decision-making strategy for pinning-based distributed multi-agent (PDMA) automatic generation control (AGC) in islanded microgrids against stochastic communication disruptions. The target microgrid is construed as a cyber-physical system, wherein the physical microgrid is modeled as an inverter-interfaced autonomous grid with detailed system dynamic formulation, and the communication network topology is regarded as a cyber-system independent of its physical connection. The primal goal of the proposed method is to decide the minimum number of generators to be pinned and their identities amongst all distributed generators (DGs). The pinning-decisions are made based on complex network theories using the genetic algorithm (GA), for the purpose of synchronizing and regulating the frequencies and voltages of all generator bus-bars in a PDMA control structure, i.e., without resorting to a central AGC agent. Thereafter, the mapping of cyber-system topology and the pinning decision is constructed using deep-learning (DL) technique, so that the pinning-decision can be made nearly instantly upon detecting a new cyber-system topology after stochastic communication disruptions. The proposed decision-making approach is verified using a 10-generator, 38-bus microgrid through time-domain simulation for transient stability analysis. Simulations show that the proposed pinning decision making method can achieve robust frequency control with minimum number of active communication channels.

Bone marrow is a spongy tissue that contains stem cells that are found inside some bones, including the hip and femur. Bone marrow cancer is a type of cancer that is caused by stem cells that make up the blood cells in the bone marrow. Sometimes these cells grow too fast or abnormally, which is called bone marrow cancer. Bone tissue cells are mainly composed of osteoblasts and osteoclasts. Osteoblast cells constantly build new bone throughout the life of each bone, and other cells called osteoclasts constantly absorb pieces of bone, so the bone is constantly being renewed. In this paper, mathematical models of tumors, the effect of the body on the drug, and the drug on the body are introduced, and then the appropriate dose of the drug to reduce tumor density is calculated using the model predictive control (MPC) algorithm. To obtain an adaptive MPC strategy, the extended least squares (ELS) method developed to learn the parameters of the tumor growth model is used. Finally, the simulation in MATLAB, assuming the model is correct, shows that the tumor is gone, and the bone mass improves over a period of time. The results demonstrate that the proposed method is effective for the treatment of bone marrow cancer.

The detection of faults in a wind turbine chain is of prime importance in order to maintain safety, enhance reliability and improve economic performance. In addition, wind systems have to ensure a continuity of service for a considerable period of time in the event of an electrical fault in the network or a fault in one of the elements of the electromechanical conversion system. This paper presents a fault detection methodology of the power converter within a wind turbine chain, equipped with a Doubly-Fed Induction Generator (DFIG). A configurable, fast, and accurate scheme is developed, the basis of which is the reliable identification of the failed switch. The solution proposed in this work involves the deployment of a redundant arm in the event of a fault; the replacement arm is utilized while waiting for a maintenance or repair operation to be carried out. The approach developed in this paper provides continuity of service after the occurrence of a fault in the network system and fault detection time is reduced. The validity of the proposed identification methodology is assessed by means of simulation of the model of a wind turbine conversion system.

In recent times, permanent magnet assisted synchronous reluctance motors (PMaSRM) have been considered as suitable traction motors for electric vehicle applications. In this type of machine, where the share of reluctance torque is more significant than the excitation torque, it is more appropriate to use a control strategy that can fully utilize the reluctance torque. This paper deals with a new structure of permanent magnet-assisted synchronous reluctance motors that was designed and manufactured in a previous study. This paper suggests applying, in a first study, a constant parameter maximum torque per ampere (MTPA) strategy to make a contribution towards the control of such structure that is becoming increasingly attractive in the field of electric transportation. This method is usually used to control interior permanent magnet synchronous motors to minimize the copper losses of the system. Before implementing and simulating this method, the mathematical models of the suggested motor and the inverter are given. An experimental study is conducted on a small-scale 1 kW prototype PMaSRM using a MicrolabBox Dspace to test and examine the proposed control. Simulation and experimental results are presented in this article in order to verify the validity of the developed control strategy.

In this paper, we propose a modified version of the Proportional Integral Derivative (PID)-type iterative learning algorithm. It is very simple to implement on a digital control device for tracking control a continuous-time system. Matlab software is used to model and simulate control algorithms. The simulative application of it to control a gearing transmission system, such that its output response follows the desired trajectory, is then carried out computationally. Obtained studying results proves that this proposed iterative learning algorithm has provided a good output tracking behavior as expected and which is robust in the sense of reducing external disturbance effects.

The electronic implementation, synchronization, and control of hyperchaos in a five-dimensional (5D) autonomous homopolar disc dynamo are investigated in this paper. The hyperchaotic behavior is found numerically using phase portraits and time series in 5D autonomous homopolar disc dynamo is ascertained on Orcad-PSpice software. The synchronization of the unidirectional coupled 5D hyperchaotic system is also studied by using the feedback control method. Finally, hyperchaos found in 5D autonomous homopolar disc dynamo is suppressed thanks to the designed single feedback. Numerical simulations and electronic implementation reveal the effectiveness of the single proposed control.

Indonesian Abu Robot Contest (KRAI) in 2018 with the theme "Throwing a Blessing Ball". The main purpose of this robot is to be able to navigate automatically in an area that is bordered by walls to complete the mission. The main problem with the robot is the navigation system. The application of PID control in the wall following system has been able to make robot movements smooth, responsive, and fast. In this study, PID control aims to smooth the movement of the robot while walking along the wall in the race arena. The PID parameter is obtained from the results of tuning with the trial and error method, the values of KP = 3, KI = 0, and KD = 5. At the PWM 150 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 4.4. cm. At the PWM 200 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.4 cm. At the PWM 250 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.8 cm. This error does not have an effect on the performance of the wall following system, because the system only uses the distance value reading data with a decimal value in front of the comma. So it can be concluded that the wall following system which is designed using ultrasonic sensors with measurement error that occurs is zero.