Pinning Decision in Interconnected Systems with Communication Disruptions under Multi-Agent Distributed Control Topology

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.

A Comparative Study of Nonlinear Control Schemes for Induction Motor Operation Improvement

In the objective of improving the performance of induction motor operation and ensuring a robust control against different uncertainties and external disturbances, especially at low-speed regions, this research highlights the main features of two nonlinear control techniques. First, the control design is based on the backstepping approach (BSA) with integral action, and then the sliding mode control (SMC) theory. The BSA principle is to define successive causal relations in order to construct the control law in a recursive and systematic way. This allows overcoming the obstacle of the higher-order system's dimension. SMC is designed to drive and then constrain the system state to lie within a neighborhood of the switching surface, this provides very strong and inherent robustness to the resulting controllers. The main reason behind developing the nonlinear control techniques is to ensure a decoupled control of the machine. Besides, it guarantees the stability of the overall system by tracking the speed reference with the fewest static error. Moreover, as the sensorless control increases the reliability and decreases the cost of the control system, an extended Kalman filter is implemented to improve speed and flux observation. The simulations of all the discussed results have been obtained by MATLAB/Simulink.

Enhanced Sliding Mode Control for a Nonlinear Active Suspension Full Car Model

This paper delivers findings on optimal robust control studies of nonlinear full car models. A nonlinear active suspension full car model is used, which considers the dynamic of a hydraulic actuator. The investigation on the benefit of using Sliding Mode Control (SMC) structure for the effective trade-off between road handling. The design of SMC in the chassis/internal subsystem is enhanced by modifying a sliding surface based on Proportional-Integral-Derivatives (PID) with the utilization of particle swarm optimization (PSO) algorithm in obtaining the best optimum value of control parameters. The switching control is designed through the Lyapunov function, which includes the boundedness of uncertainties in sprung masses that can guarantee the stability of the control design. The responses of the proposed controller have improved the disturbance rejection up to 60% as compared to the conventional SMC controller design and shown the high robustness to resist the effect of varying the parameter with minimal output deviations. The study proved that the proposed SMC scheme offers an overall effective performance in full car active suspension control to perform a better ride comfort as well as the road handling ability while maintaining a restriction of suspension travel. An intensive computer simulation (MATLAB Simulink) has been carried out to evaluate the effectiveness of the proposed control algorithm under various road surface conditions.

Spiking PID Control Applied in the Van de Vusse Reaction

Artificial neural networks (ANN) can approximate signals and give interesting results in pattern recognition; some works use neural networks for control applications. However, biological neurons do not generate similar signals to the obtained by ANN. The spiking neurons are an interesting topic since they simulate the real behavior depicted by biological neurons. This paper employed a spiking neuron to compute a PID control, which is further applied to the Van de Vusse reaction. This reaction, as the inverse pendulum, is a benchmark used to work with systems that has inverse response producing the output to undershoot. One problem is how to code information that the neuron can interpret and decode the peak generated by the neuron to interpret the neuron's behavior. In this work, a spiking neuron is used to compute a PID control by coding in time the peaks generated by the neuron. The neuron has as synaptic weights the PID gains, and the peak observed in the axon is the coded control signal. The neuron adaptation tries to obtain the necessary weights to generate the peak instant necessary to control the chemical reaction. The simulation results show the possibility of using this kind of neuron for control issues and the possibility of using a spiking neural network to overcome the undershoot obtained due to the inverse response of the chemical reaction.

Pulse Width Modulation Analysis of Five-Level Inverter- Fed Permanent Magnet Synchronous Motors for Electric Vehicle Applications

In recent times, intense research has been focused on the performance enhancement of permanent magnet synchronous motors (PMSM) for electric vehicle (EV) applications to reduce their torque and current ripples. Permanent magnet synchronous motors are widely used in electric vehicle systems due to their high efficiency and high torque density. To have a good dynamic and transient response, an appropriate inverter topology is required. In this paper, a five-level inverter fed PMSM for electric vehicle applications, realized via co-simulation in an electromagnetic suite environment with a reduced stator winding current of PMSM via the use of in-phase disposition (PD) pulse width modulation (PWM) techniques as the control strategy is presented. The proposed topology minimizes the total harmonic distortion (THD) in the inverter circuit and the motor fed and also improves the torque ripples and the steady-state flux when compared to conventional PWM techniques. A good dynamic response was achieved with less than 10A stator winding current, zero percent overshoot, and 0.02 second settling time synchronization. Thus, the stator currents are relatively low when compared to the conventional PWM. This topology contribution to the open problem of evolving strategies that can enhance the performance of electric drive systems used in unmanned aerial vehicles (UAV), mechatronics, and robotic systems.

Treatment of Bone Marrow Cancer Based on Model Predictive Control

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.

Robust Global Synchronization of a Hyperchaotic System with Wide Parameter Space via Integral Sliding Mode Control Technique

The inherent property of invariance to structural and parametric uncertainties in sliding mode control makes it an attractive control strategy for chaotic dynamics control. This property can effectively constrain the chaotic property of sensitive dependence on initial conditions. In this paper, the trajectories of two identical four-dimensional hyperchaotic systems with fully-known parameters are globally synchronized using the integral sliding mode control technique. Based on the exponential reaching law and the Lyapunov stability principle, the problem of synchronizing the trajectories of the two systems was reduced to the control objective of asymptotically stabilizing the synchronization error state dynamics of the coupled systems in the sense of Lyapunov. To verify the effectiveness of the control laws, the model was numerically tested on a hyperchaotic system with a wide parameter space in a master-slave configuration. The parameters of the hyperchaotic system were subsequently varied to evolve a topologically non-equivalent hyperchaotic system that was identically coupled. In both cases, the modeled ISM control laws globally synchronized the dynamics of the coupled systems after transient times, which sufficiently proved the invariance property of the ISMC. This study offers an elegant technique for the modeling of an ISMC for hyperchaotic coupling systems. As an open problem, this synchronization technique holds promises for applications in robot motion control, chaos-based secure communication system design, and other sensitive nonlinear system control.

Soft Pneumatic Exoskeleton for Wrist and Thumb Rehabilitation

A huge population of the world is suffering from various kinds of disabilities that make basic daily activities to be challenging. The use of robotics for limb rehabilitation can assist patients to recover faster and reduce therapist to patient ratio. However, the main problems with current rehabilitation robotics are the devices are bulky, complicated, and expensive. The utilization of pneumatic artificial muscles in a rehabilitation system can reduce the design complexity, thus, making the whole system light and compact. This paper presents the development of a new 2 degree of freedom (DOF) wrist motion and thumb motion exoskeleton. A light-weight 3D printed Acrylonitrile Butadiene Styrene (ABS) material is used to fabricate the exoskeleton. The system is controlled by an Arduino Uno microcontroller board that activates the relay to open and close the solenoid valve to actuate the wrist. It allows the air to flow into and out of the pneumatic artificial muscles (PAM) based on the feedback from the sliding potentiometer. The mathematical model of the exoskeleton has been formulated using the Lagrange formula. A Proportional Integral Derivative (PID) controller has been implemented to drive the wrist extension-flexion motion in achieving the desired set-points during the exercise. The results show that the exoskeleton has successfully realized the wrist and thumb movements as desired. The wrist joint tracked the desired position with a maximum steady-state error of 10% for 101.45ᵒ the set point.

Maximum Torque per Ampere Control of Permanent Magnet Assisted Synchronous Reluctance Motor: An Experimental Study

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.

A New Approach to Fault Detection in the Power Converter in Wind Turbine Conversion Systems

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.